Drivers of Product Expiration in Retail Supply Chains - INSEAD

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Drivers of Product Expiration in Retail Supply ChainsArzum Akkas

Questrom School of Business, Boston University, Boston, MA, 02215, [email protected]

Vishal GaurJohnson Graduate School of Management, Cornell University, Ithaca, NY, 14853, [email protected]

David Simchi-LeviEngineering Systems Division, Massachusetts Institute of Technology, Cambridge, MA, 02139, [email protected]

Product expiration is an important problem in the consumer packaged goods (CPG) industry, costing manufacturers about

1-2% of gross retail sales in the U.S.A. We study the drivers of product expiration using retail data for 768 SKUs and

10,000 stores (870,493 store-SKU level observations) as well as upstream supply chain data from a CPG manufacturer.

Thus, we show the extent to which expiration of products in retail stores is caused by factors related to store operations,

supply chain practices, and product configuration decisions. A zero inflated negative binomial regression is applied to

model the occurrence of expiration. Amongst the factors, we find case size, supply chain aging, minimum order rules,

manufacturer’s incentive programs for the sales-force, and forecasting task complexity to be significantly related to expi-

ration. Our counterfactual analysis shows the financial benefits of four types of initiatives to reduce expiration. Other

firms can replicate this analysis to identify the drivers of product expiration in their supply chains. Moreover, identifying

the extent to which manufacturers and retailers contribute to expiration will help improve supply chain coordination.

Key words: retail operations, consumer packaged goods, expiration, empirical, zero-inflated models, generalized linear

models, perishable goods, supply chain, marketing/operations interface

History:

1. Introduction

Consumer Packaged Goods (CPG) products, such as soft drinks, shelf stable dry food, and health and beauty

aids, that turn into waste at retail stores were estimated to cost $15 billion in 2008, representing 1 to 2

percent of gross retail sales in the U.S.A. (Joint Industry Unsaleables Report 2008). This waste, termed as

1

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unsaleables by the CPG industry, spans three categories: damage, expiration and product discontinuation.

According to an industry survey (Joint Industry Unsaleables Report 2008), 17% of unsaleables are disposed

at landfills which is quite substantial in volume considering that CPG products are fast-moving items at

retailer shelves1. Unsaleables impact profits significantly due to narrow industry margins. At our collabo-

rator, the cost of unsaleables is equivalent to 50% of their annual profit. The management of unsaleables

is a complex problem involving coordination across manufacturers and retailers in a supply chain. Despite

ongoing efforts, the root causes of unsaleables, particularly product expiration, are not well understood and

efforts to mitigate the occurrence of expiration have been ineffective.

The CPG industry typically uses audits and surveys to diagnose the occurrence of unsaleables (Raftery

Resource Network, Inc. 2011, Genco 2011). In audit studies, unsaleables are visually inspected at sam-

pled stores or return centers and a reason code is recorded for each instance of unsaleable. Such a visual

inspection usually reveals the cause of damage, such as a packing failure (e.g., weak plastic, case handle,

carton burst, nail damage on pallet, etc.). But a visual inspection of an expired product is not informative. A

product can expire on the shelf due to causes that have occurred anywhere during the sojourn of the product

from the factory to the shelf. Batching in production or transportation, inefficient inventory management at

the warehouse, or suboptimal shelf allocation at the retail store could all cause product expiration. These

causes cannot be identified by examining a product on the shelf after it expires. Thus, audits have been

successful in addressing the causes of damage and product discontinuation, but not expiration.

Surveys, on the other hand, collect information about respondent beliefs on the causes of unsaleables. Not

surprisingly, manufacturers and retailers have different views on the leading causes of unsaleables (Joint

Industry Unsaleables Report 2008). Manufacturers rank rotation practices at retailers as the major cause

of expiration, whereas retailers rank code dating standards and procedures.2 Similarly, manufacturers rank

1 This survey further notes that 35% of unsaleables are donated to foodbanks, 26% have salvage value and are sold in secondary

markets, 19% are sent back to manufacturers, a portion of which might end up in landfills to prevent cannibalization, and only 1%

are recycled.

2 Rotation refers to the practice of putting fresher products to the back of the shelf and pulling older ones to the front. Code dating

refers to open codes or closed codes printed on product packaging by manufacturers to help the store determine how long to display

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product handling as the leading root cause of damage whereas retailers rank package design. Thus, each

associates the main cause of a certain type of unsaleables with the other. Our interviews at one major CPG

company suggest that such differences persist even within the same company—the sales organization iden-

tifies operational practices as the driver of product expiration whereas the operations organization claims

that sales incentives are the main reason. Thus, due to dependent events and lack of transparency in the

supply chain, expiration remains an unsolved problem with cost and waste implications for manufacturers

and retailers.

The objective of our paper is to propose an econometric model to improve the understanding of the root

causes of expiration in the CPG industry. Unlike the methods employed by existing studies, our analysis is

based on archival data collected from the entire supply chain to examine the extent to which the occurrence

of expiration is associated with store operations, supply chain performance, and product characteristics. We

collaborate with a large CPG manufacturer, which we refer to as AlphaCo in this paper. AlphaCo is a multi-

billion dollar food and beverage company operating over 50 manufacturing locations and 400 distribution

centers in North America. AlphaCo services retail stores directly and additionally manages inventory at

about 200,000 consumer points. We employ data for 2011 from AlphaCo’s archival system, which includes

deliveries to and returns from 66,867 retail stores, warehouse inventory counts, product deployment at 449

AlphaCo locations and shelf life and case size information for 768 products.

Is observed product expiration in a CPG business such as AlphaCo natural to expect due to the ran-

domness of demand or is there an opportunity to reduce expiration? We first compare observed expiration

with theoretical benchmarks constructed by simulating a base stock policy on the sample paths of demand

observed in detailed transaction level data for a small subsample of products. We find that the average actual

expiration is 96 times the simulated expiration for 95% service level. Additional scenarios with varying the

service levels (97% and 99%), varying assumptions on shelf rotation, and shipments in case increments

still produce lower simulated expiration than the actual. These results suggest that expiration occurs due

the product for sale. Open codes are calendar dates that take forms such as ‘best buy’, ‘sell by’, ‘use by’, etc., whereas a closed code

represents a series of numbers. Retailers contend that closed codes make it harder to manage rotation. They also claim that with the

recent trend to switch from closed code to open code practices, manufacturers have reduced the shelf life to be more conservative.


to reasons other than the randomness of demand, and thus, could be reduced by improving operations at

manufacturers and retailers.

We identify six potential drivers of product expiration: case size, supply chain aging (i.e., the sojourn

time of a product in the supply chain before it reaches the retail shelf), manufacturer’s sales incentives,

forecasting task complexity, minimum order rules, and shelf rotation. These variables represent different

aspects of practical supply chains, including store execution, back-end supply chain operation, and product

characteristics. We also control for variables such as mean demand variation, store type, and product shelf

life. Our estimation method is based on count models because expiration is a nonnegative integer and is

bounded above by the total shipment quantity. Using data from 870,493 store-product combinations, we

evaluate several econometric specifications: binomial, Poisson, negative binomial, and zero-inflated models.

We find that the zero-inflated negative binomial (ZINB) model yields the best fit and the most unbiased

residuals by addressing two characteristics of our data: probability mass at zero and overdispersion.

Our main result is that case size, supply chain aging, manufacturer’s sales incentives, forecasting task

complexity, and minimum order rule are all statistically significant determinants of product expiration. The

control variables, demand variation, shelf life, and retailer type also affect expiration significantly. Thus, our

study shows that expiration occurring at retail shelves can be caused by both manufacturers and retailers.

To refine our results, we analyze the interaction of sales incentive programs with demand rate—incentive

programs can increase demand rate, which can lead to overestimating the effect of case sizes and minimum

order rule on expiration. We also include potential endogeneity between supply chain aging and expiration

in our model, i.e., more expiration may result in more inventory upstream in the supply chain, which may

increase supply chain aging.

A counterfactual analysis shows that reducing the case size for products that are currently packed in 24

units to 12 units yields a 33.6% decrease in expiration volume and a $5.09M decrease in expiration cost. This

constitutes an opportunity for CPG companies to reduce waste by reducing case sizes. Reducing the days

of supply in the supply chain by one week corresponds to 5.9% decrease in expiration volume and $5.58M

decrease in expiration cost. Relaxing the minimum order rule can reduce expiration volume by 17.5%


and expiration cost by $2.87M. Accordingly, visit frequencies can be reduced at stores that have a high

frequency of orders equal to their minimum order sizes. Lastly, we find that three sales incentive programs

cost $3.32M, $1.98M, and $13.13M with more expiration of 55.1%, 22.3%, 28.5% in volume, respectively.

This analysis shows the performance improvement that can be achieved in practice by addressing various

types of causes of product expiration.

Our paper is the first descriptive study of product expiration in the inventory management academic liter-

ature. While the existing literature on perishables inventory management has focused on inventory policies,

our paper contributes to the literature by developing an econometric model of expiration, and providing

insights into its sources beyond demand uncertainty. Our analysis is distinct in combining sku-level data

from stores and supply chain and marketing data from manufacturers. The results of this analysis can facil-

itate solutions to the problem of expiration by improving the understanding of its root causes. In practice,

manufacturers fully or partially compensate retailers for unsaleables. In the absence of precise knowledge of

the contribution of each party to the occurrence of unsaleables, existing reimbursement mechanisms favor

either the manufacturer or the retailer, depending on the balance of power. Typically, the benefited party has

little incentive to improve the practices that cause unsaleables. Even within a firm, whether the manufac-

turer or the retailer, practices leading to unsaleables span multiple functions. Either one function absorbs the

cost regardless of cause or no particular function is accountable for the cost of unsaleables. Our expiration

model identifies the contribution of different actors to the expiration problem, and presents evidence that

expiration can be alleviated with better management at retailers and manufacturers.

2. Literature Review

Our paper is related to the literature in perishable inventory management, retail operations, and sustainabil-

ity models.

The management of perishables is an important problem in many industries, such as blood banks, food,

and pharmaceuticals. Seminal research in this area was conducted by Nahmias (1975) and Fries (1975), who

analyze the optimal inventory policy considering expiration and shortage costs under a cost-minimizing

dynamic program and show that the optimal policy is non-stationary and is dependent on the age distribution


of inventory. Nahmias (1982) presents an extensive review of the issuance and replenishment decision

models for perishable inventory. Most of this literature has focused on single-location models and ignored

aging in the supply chain, i.e., a product is available for its full life upon receipt at the retailer.

Ketzenberg and Ferguson (2006) consider a two-stage system with supply chain aging and order batching

in order to evaluate the benefit to the retailer from the availability of product life information at the supplier.

This benefit manifests in the retailer ordering more product when the supplier has fresher inventory avail-

able, and less otherwise. Through a simulation study, the authors show that sharing product life information

increases the retailer’s profit by an average of 4.4%, increases the average remaining shelf life of retail

inventory at the time of replenishment by 8%, and decreases the incidence of product expiration by 40%.

The retailer benefits the most from information sharing when the variability of the demand or the remaining

shelf life of the items is high, product lifetimes are short, and the cost of the product is high.

Ketzenberg and Ferguson (2008) focus on slow-moving items that are ordered in single case pack sizes.

For a two-stage supply chain with one supplier and one retailer, they evaluate the value of two supply

chain improvements—sharing of inventory and replenishment information by both partners in a decentral-

ized supply chain, and centralized control. Using a numerical study, they find that, compared to the base

scenario, the total supply chain expected profit increases by 4.2% with information sharing and by 5.6%

with centralization. Further, the benefits of information sharing or centralized control are minimal when an

optimal case size is chosen.

Our work contributes to the literature on perishable inventory by examining it in a real-world context,

integrating data from the manufacturer and many retailers to identify the role of each player, and identifying

the role of supply chain execution variables such as rotation compliance, supply chain aging, case size, and

manufacturer’s sales incentive programs. Whereas the literature has developed optimal policies and useful

heuristics for inventory management for perishable products, we develop insights into the relative effects of

different supply chain variables that can be managed to reduce the occurrence of expiration.

The recent literature on retail operations and supply chain execution is also relevant to our paper. Several

papers in this literature have studied real-world phenomena through empirical research or analytical mod-

els. For example, DeHoratius and Raman (2008) identify the sources of inventory record inaccuracy using


hierarchical linear modeling and emphasize the need to incorporate these sources into inventory planning

tools. Kok and Shang (2007), DeHoratius et al. (2008) and several others have since developed inventory

planning algorithms under inventory data inaccuracy. Van Donselaar et al. (2010) study the ordering behav-

ior in a supermarket chain and show that store managers deviate predictably from automated replenishment

systems due to factors such as in-store handling cost, case size, demand rate, produce variety, and demand

variability. Accordingly, they devise a method to improve automated replenishment systems. Kesavan et al.

(2014) investigate the relationship between flexible labor resources and financial performance. Corstjens

and Doyle (1981) study optimal shelf space allocation among multiple products, considering main and cross

space elasticities, in order to minimize procurement, inventory carrying, and out-of-stock costs. Kok and

Fisher (2007) study the optimal allocation of shelf space to an assortment of substitutable products in a

category.

Our work contributes to this stream of research by studying an important but unexplored issue in the CPG

industry, and identifying its causes, which have only been partially analyzed in theoretical research. We

exploit the statistical characteristics of expiration in practice, i.e., count data with frequent zero observations,

to employ zero-inflated count models for hypothesis testing. Some variables used in our research are based

on the literature. For instance, excess shelf space, whether due to suboptimal shelf allocation or case size

considerations, can lead to excess inventory, which then can cause product expiration. Thus, our paper

builds on these topics in retail operations by showing the waste implications of different aspects of supply

chain execution.

Other aspects of supply chain execution include sales incentive programs, order batching, and order infla-

tion. Examples of this work span the literature in operations management and economics. Chen (2000)

proposes a salesforce compensation package that induces a smooth ordering behavior to match the pro-

duction cycle. He compares this to a widely used compensation plan based on annual quotas which causes

salespersons to concentrate their efforts in the last period. Oyer (1998) empirically shows that salespersons

and executives influence the timing of customer purchases, driven by nonlinear incentive contracts, result-

ing in business seasonality with high sales at the end of the fiscal year and low sales at the beginning. Our


work adds to this literature by identifying sales incentive programs and order batching as factors that cause

increased product expiration.

Finally, our paper adds to the sustainable operations management literature that is concerned with waste

disposed at landfills. Examples in this research stream is less extensive relative to the sustainable opera-

tions management literature focusing on other themes in the field such as closed-loop-supply chains (Atasu

and Subramanian 2012), product design (Agrawal and Ulku 2012), and carbon emissions (Cachon 2014).

Among authors concerned with landfill waste, Ata et al. (2012) examines alternative subsidy schemes for

waste-to-energy firms that divert organic waste from landfills to produce renewable energy. More relevant

to our research with a focus on food waste in retailing, Belavina et al. (2016) studies financial and environ-

mental implications of revenue models in online retailing.

3. Research Context and Hypotheses

In Section 3.1, we set the context for our research by presenting an overview of AlphaCo’s supply chain

operations. These operations and their attendant challenges for managing unsaleables are representative of

many types of CPG manufacturers. In Section 3.2, we compute a benchmark by analyzing the amount of

expiration that can naturally occur due to the randomness of demand. A comparison of this benchmark

with the actual expiration reveals that demand uncertainty explains only a small portion of the observed

expiration. Section 3.3 discusses other factors that must play a role in generating expiration.

3.1 Supply Chain Operations at AlphaCo

AlphaCo operates through the direct-store-delivery (DSD) sales & distribution model, which involves deliv-

ering products directly to retail stores bypassing retailers’ distribution centers, as well as managing store

inventory. Each AlphaCo sales representative is responsible for a fixed set of stores, called a route, and

makes regular visits to them according to a fixed schedule. At each visit, the sales representative creates a

return order for damaged or expired products and moves them to the back room for pick up, then observes

the on-hand inventory and creates replenishment orders. The sales representative is also responsible for

restocking shelves from the back room, rotating the shelf, and setting up promotional displays. Deliveries

are made the following day by a different employee, a truck driver, who also picks up the returns from


the back room. A store always receives all of its deliveries from one warehouse. Stores are categorized by

annual sales volume into two groups: small format and large format stores. Large format stores typically are

supermarkets and mass merchants. Small format stores are drug stores, convenience stores, and gas stations.

The cost of unsaleables consists of the procurement cost of the product, sales & delivery cost to place

it in the store, and reverse logistics cost. An internal unsaleables study conducted at AlphaCo in 2010

suggested that the reverse logistics cost is approximately equal to the sum of the other two cost components.

Thus, even though unsaleables make up only 0.87% of the total sales volume at AlphaCo, the total cost of

unsaleables is equivalent to 50% of the net profit, which is approximately 3% of sales. Further, unsaleables

have indirect costs such as the opportunity cost of occupying shelf space that would otherwise be used for

saleable products and the cost of lost goodwill due to consumers switching to competing products. As a

result, unsaleables are a matter of great importance. Expiration comprises about 65% of the unsaleables

volume while damage makes up the remaining 35% at AlphaCo. Discontinued products go through a phase-

out process and eventually enter reverse logistics once they expire.

AlphaCo conducted a comprehensive study of product waste in 2010. It involved audits at sample stores

to document the root cause for each instance of unsaleables. The study was able to identify the root causes

for damaged products. However, the causes for expired products were not definitively established. Figure 1

presents the causes of expiration as identified in the study. Note that the second most frequently cited root

cause for expiration is unknown. This shows the inability of audits to diagnose the causes of expiration.

3.2 Simulation Benchmark

Unsaleables are expected to occur because a retailer faces a tradeoff between the costs of insufficient inven-

tory and unsaleables. Excess inventory would arise from stocking decisions that balance this tradeoff. Thus,

we first assess whether the amount of expiration observed in our data set is explained by inventory decision

models. To answer this question, we compare the observed expiration against model-based benchmarks.

We conduct a simulation analysis using point-of-sale (POS) data for 40 SKUs of AlphaCo obtained from

one retail store. These products include all items carried at this store supplied by AlphaCo, i.e., whose UPC

codes match AlphaCo inventory IDs. The products vary in their shelf lives, case sizes, and demand rates.


The median, maximum, and minimum values of these variables are respectively as follows: shelf life, 14,

104, and 12 weeks; case size, 12, 24, and 1 units; and daily demand rate, 0.12, 0.65, and 0.01 units. We

receive daily point of sale data covering 604 days. Since the store is serviced once a week by AlphaCo,

we aggregate the daily point of sale data by week. 26 out of 40 products had zero sales for some of the

weeks, most likely due to product introductions and discontinuations. To account for such scenarios, we

discard observations prior to the first week of positive sales and after the last week of positive sales. We

construct an empirical demand distribution from the observed sales, and use it to generate a sample path of

10,000 demand occurrences. Order quantities for each week are computed based on a heuristic order-up-to

inventory replenishment policy. We use a heuristic because the optimal policy for perishables suffers from

the curse of dimensionality and is hard to compute. Each week, an order equal to the difference between

the order-up-to level and the sum of the inventories of different ages is created. AlphaCo’s own inventory

policy3 involves minimum order rules and requires the knowledge of shelf space data which is not included

in the POS data we received. Therefore, we choose an appropriate policy from inventory theory for our

simulation analysis.

We evaluate 12 scenarios determined by assumptions on three dimensions: varying service level (95%,

97%, and 99%), inventory issuing policy (FIFO and LIFO corresponding to full shelf rotation and no shelf

rotation cases), and shipment rule (in single units and in case-size increments). Table 1 presents the simu-

lation results. The main observation is that although simulated product expiration increases monotonically

with service level, with case size increments, and with a switch from FIFO to LIFO, actual occurrence

of expiration is still higher. The lowest expiration occurs under FIFO and single-unit shipments: the total

simulated expiration for 40 products is 0.0795, 0.1357, and 0.2982 units/week for the 95%, 97%, and

99% service level scenarios, respectively. The total actual expired quantity, according to AlphaCo’s product

3 The current inventory policy at AlphaCo is the following. Order quantity is the maximum of the hole on the shelf (the difference

between the shelf capacity allocated to the product and the current inventory level) or the forecast between two delivery periods,

without a buffer for safety stock. Next, orders are inflated to reach the minimum order level, if necessary. Slow moving products

usually are not stored in the backroom and usually bind with the first type of ordering (i.e.,fill the hole).


return records, is 7.6346 units/week, which is 96.0, 56.3, and 25.6 times these simulated expiration quanti-

ties, respectively. Switching to LIFO results in an approximately five-fold increase in simulated expiration.

Constraining shipment case sizes further increases simulated expiration. The highest total expiration occurs

under LIFO inventory issuing policy and shipments in case-size increments; here, the simulated expiration

is 2.0022, 2.2273, and 3.0667 units/week for the 95%, 97%, and 99% service level scenarios, respectively.

This corresponds to 3.8, 3.4, and 2.5 times more actual expiration relative to the simulated expiration.

The above simulation also shows that case-size replenishment has a more substantial impact on expiration

than no rotation. To understand the impact of case-size shipments and lack of shelf rotation, we compare the

results from these scenarios to the baseline scenario of FIFO and single-unit replenishment using Table 1.

Accordingly, single-unit replenishment scenario assuming LIFO produces 5.3, 4.7, and 4.5 times more sim-

ulated expiration than the baseline scenarios for 95%, 97%, and 99% service levels, respectively, whereas

case shipment scenario assuming FIFO produces 18.6, 11.7, and 6.4 times more simulated expiration. This

suggests a greater importance of case-size replenishment in causing expiration than the lack of rotation.

3.3 Drivers of Expiration

To motivate the hypotheses, we consider the inventory replenishment of a single product at a retail store.

Let S denote the shelf life of the product, Dt denote the random demand in period t, and Q denote the

amount of inventory shipped to the store at the beginning of period 1 with zero starting inventory. Thus, the

amount that will eventually expire from this batch of shipment under the FIFO issuing policy will be EQ =

[Q−∑S

t=1Dt]+, which depends on the demand realization, the shelf life of the product, and the shipment

quantity. For the same demand realization, a larger shipment quantity or a shorter shelf life correspond to a

higher amount of expiration. Therefore, we expect practices or circumstances that reduce shelf life or inflate

shipments to cause expiration. We discuss these practices identified through our interviews and industry

reports, and set up our hypotheses.

HYPOTHESIS 1. The amount of product expiration increases with the case size in which the product is

shipped to the store.


CPG manufacturers ship items in multiples of case size to stores4. Usually, case sizes are set for an SKU

group which some manufactures call a ”package”. All SKUs within a package have the same container

type (bottle versus can) and same size (12oz versus 20 oz), but have different flavors (cherry, blueberry,

honey, etc.). Manufacturers usually do not consider waste implications when making product configuration

decisions.

In the simulation study in Section 3.2, we find that the target inventory for 95% service level is less than

the case size for 33/40 products. Thus, stores must round up their shipment quantities to one case. Further,

case size cover (i.e., case size divided by mean demand) is greater than shelf life for 15/40 products. In other

words, a case of inventory is expected to last longer than the shelf life for many products, which would

result in expiration. Low demand rates are not unique to this store. According to Weitzel (2011), nearly half

of the SKUs at retail stores sell less than one unit a week. For such products, shelf life does not need to be

very short for expiration to occur. For instance, expiration will occur if case size is 24 units and shelf life is

less than 6 months or if case size is 12 units and shelf life is less than 3 months.

A test of this hypothesis is valuable because higher case sizes do not necessarily correspond to a higher

occurrence of expiration when shelf life and demand are sufficiently large. Indeed, a large case size can be

beneficial since it makes handling more efficient. Therefore, it is important for manufacturers to verify the

value of smaller case size in order to make an informed optimal case size decision, since reducing case sizes

increases handling and packaging costs and may even require upgrading production lines.

HYPOTHESIS 2. The amount of product expiration increases with inventory aging of the product in the

supply chain.

Every product has a fixed shelf life at the time of production. Time spent in the supply chain erodes

this shelf life. We call this supply chain aging. It can occur due to reasons such as production and trans-

portation batching, high safety stocks, and poor forecasts. By reducing effective shelf life, supply chain

4 Typically, the industry uses three terms in SKU records: each, pack, and case. Case is the unit by which products are shipped

within the supply chain. Pack and each are the units by which products are sold to consumers (e.g. 6-pack beer contains 6 eaches of

bottles, where the product is sold in packs; a salad dressing is usually sold in eaches). A case may consist of multiple packs; a pack

or a case consists of multiple eaches. We use case size to define the number of packs or eaches, whichever is the unit of measure

for consumer purchase, contained in one case. Typical case size numbers are 1, 4, 6, 12, and 24.


aging increases the probability of expiration. Further, the effect of supply chain aging on expiration can be

expected to depend on the demand rate, store inventory, and shelf rotation.

We measure supply chain aging for each product-store combination as the cumulative average days of

supply of that product in its supply chain. AlphaCo has a multi-tier supply network consisting of plant ware-

houses and satellite warehouses serving local retail outlets. High velocity items are typically produced in all

plants, whereas low velocity items are produced only in a subset of plants. With full truckload shipments,

low velocity items are distributed to the plants that do not produce these items. We map the multi-tier sup-

ply chain for each product-store combination and compute the total average days of supply of the product

across the stages of the supply chain.

Most sources of supply chain aging provide advantages. Production batching reduces unit production cost

by reducing changeover times and increasing utilization. Similarly, transportation batching (via full truck-

load or pallet shipments) reduces transportation and handling costs. High safety stock helps reduce lost

sale. Given these benefits, it is important to measure whether and how much supply chain aging generates

expiration before contemplating on reducing supply chain inventory. A clear picture of the trade-offs can

form the basis of future optimization studies determining optimum batch sizes and safety stock levels.

HYPOTHESIS 3. The amount of product expiration decreases with rotation discipline at the store.

Shelf rotation is the practice of placing fresher products in the back of the shelf while pulling older ones

to the front. Rotation facilitates first-in-first-out issuing of inventory. Perishable inventory theory on the

issuance of inventory in LIFO or FIFO order suggests that rotation affects the occurrence of expiration

(Nahmias 1982). According to the 2008 Joint Industry Report, CPG manufacturers believe lack of shelf

rotation to be the most common root cause of expiration. An internal study by AlphaCo, however, found that

63% of the time when an expired product is found on the shelf, the shelf was in fact rotated. This suggests

that the impact of rotation may not be as dramatic as believed in the industry.

There exist solutions in industry making rotation more efficient, such as back-loading shelves or color

coded caps indicating production period. These solutions typically involve a fixed installation or imple-

mentation cost. Thus, it is important to understand the extent to which rotation effect expiration in order to

evaluate the net value of solutions that facilitate rotation.


HYPOTHESIS 4. The amount of product expiration increases with compliance to minimum order rules

in the store.

Inflation of order quantities to reduce transportation cost is a common practice. AlphaCo imposes mini-

mum order sizes for store replenishment orders in order to reduce delivery costs. Our field trips reveal that

original order quantities can be increased to make the total order size equal to the required minimum. This

behavior inflates store inventory, which can be expected to increase expiration. Sales representatives are

measured on their compliance to this rule, which we use as the measure to test this hypothesis.

Minimum order rules are beneficial since they help control transportation cost. Therefore, an evidence of

expiration occurring due to minimum order rules does not suggest relaxing these rules, but rather suggests

reducing the visit frequency of the store. An infrequent visit risks lost sales while a frequent visit may

generate expiration if the demand is low and, as a result, minimum order rule is binding. To find the optimum

visit frequency, we first need to understand the extent to which expiration occurs due to minimum order

rules.

HYPOTHESIS 5. The amount of product expiration increases with manufacturer’s incentives programs

for the sales-force.

CPG manufactures offer a variety of performance incentives to their sales force which can increase the

chances of product expiration. AlphaCo, for instance, offers its sales representatives not only a sales com-

mission applicable on the overall sales volume, but also a second layer of rewards for growing sales volume

or building store displays for specific products. AlphaCo calls these reward programs incentives. Each

incentive is valid during a particular month and focuses on a group of products.

Two types of incentives are offered. One involves competition for the best looking in-store displays

among sales representatives. Products not returned to the warehouse at the end of the display period gen-

erate over supply of inventory at the stores. The other incentive type involves a sales growth target by a

fixed volume or a percentage compared to the prior year. Typically, these targets are achieved by gaining

additional shelf space or permanent displays, which increases store inventory.


Expiration is an operations matter while incentives are developed by the sales&marketing department.

Interdepartmental aspect of this problem makes addressing expiration driven by incentives more challeng-

ing. Sales organization will certainly not agree to abandoning these programs since incentives intend to be

beneficial by stimulating consumer demand as much as they potentially generate excess store inventory.

However, sales organization can be convinced to consider operational factors in the design of incentives.

For example, a moderate, as opposed to an aggressive, growth target can be established for products with

shorter shelf lives. A test of this hypothesis is useful by providing support for the operations function to

request ”smart incentives” from the sales organization that can benefit the organizational objectives of both

functions.

HYPOTHESIS 6. The amount of product expiration increases with the forecasting task complexity for the

sales representatives who initiate replenishment orders.

Each AlphaCo route is managed by one sales representative. Routes serve varying number of stores. For

instance, a sales representative assigned to large format stores such as supermarkets may visit as few as

4 stores a day, whereas one assigned to small format stores may visit up to 15 stores a day. Moreover,

large stores are usually serviced more frequently than small stores which means small format routes overall

cover many more stores than large format routes. Sales representatives initiate replenishment orders at

store visits by forecasting the demand up to the next replenishment epoch. This forecasting task inevitably

gets more complex with increasing store-product combinations in the route and so does the likelihood for

mismanaging inventory. Thus, we expect that as the difficulty in forecasting increases, sales representative

will err on overstocking than understocking, which should increase the chances of expiration. We measure

forecasting task complexity as the number of store-product combinations that a sales representative is in

charge of managing.

The above drivers of expiration can be classified through different perspectives. For example, sources of

expiration can be classified as (i) drivers reducing shelf life, and (ii) drivers increasing shipment quantity.

Among the root causes listed, case size, minimum order rule, incentives programs for the sales-force, and

forecasting task complexity can raise the shipment quantity beyond the amount required to match uncertain


demand, and therefore, can be considered as drivers increasing the shipment quantity. Supply chain aging

and non-compliance with shelf rotation on the other hand reduce effective shelf life. Further, from a chan-

nel viewpoint, drivers can be categorized as (i) manufacturer-related, and (ii) retailer-related drivers. Case

size, supply chain aging, and sales incentive programs can be categorized as manufacturer-related, whereas

minimum order size, rotation, and forecasting task complexity can be considered as retailer-related drivers.

This classification is helpful in improving supply chain coordination issues in product expiration5.

4. Data Description and Estimation Model

Section 4.1 describes the data received from AlphaCo and defines our variables. Section 4.2 presents our

estimation models.

4.1 Data Description

We obtain delivery, return, supply chain, and marketing data for 768 SKUs and 66,867 stores in the United

States for the year 2011. There are 8 store types in our dataset: supermarkets, convenience stores & gas

stations, other grocery (stores bigger than convenience stores and smaller than supermarkets are categorized

as other grocery at AlphaCo’s business systems), dollar discount stores, drug stores, mass merchants, club

stores, and supercenters. For computational efficiency, we do not estimate our models on the entire data set.

Instead, we draw a random sample of 10,000 stores. Thus, our final data set consists of 870,493 store-SKU

level observations across 768 SKUs and 10,000 stores. Table 2 provides comparative statistics showing that

our sample is representative of the full dataset.

Our data are obtained from three sources: data warehouse, spreadsheets, and picture files. Different parts

of the data have varying levels of aggregation with respect to time (i.e., day, month, year) and supply chain

structure (i.e., warehouse, route, store). We construct the following variables from these data:

• returnps is an integer-valued variable representing the total number of expired units for store s and

product p during 2011. returnps is our dependent variable.

• deliveryps is a discrete variable representing the net amount of product p delivered to store s. A store

receives all its inventory from a single warehouse and a single route. Our data set includes total deliveries,

5 Since our collaborator AlphaCo is a direct-store-delivery manufacturer, in our specific analysis, all root causes are manufacturer

related.


total saleable returns, and total returns due to damage aggregated for 2011 by shipping warehouse, route,

store, and product. Saleable returns are unsold display products that are returned to the warehouse at the end

of the display period. We deduct saleable returns and returns due to damage from the delivery amount to

obtain net delivery amount, deliveryps. We use it to represent the number of Bernoulli trials in our rate (i.e.,

binomial) model, and as the exposure variable in our count models (i.e., Poisson and negative binomial).

• case size coverps, the explanatory variable associated with Hypothesis 1, represents the expected

amount of time to sell one case at the store and is defined as the ratio of case size to mean consumer demand.

Case size is the number of consumer units contained in one case of product p, which we obtain from the

products table in the data warehouse. Case size varies by product. The case sizes at AlphaCo are 1, 2, 3, 4,

6, 8, 12, 15, or 24 units of products. Figure 2 shows the frequency distribution of case size across all 768

products in our data set. We approximate the mean consumer demand at store s as the net deliveries (i.e.

deliveries after saleable and unsaleable returns are deducted). Here, we construct demand from sales data,

therefore there is a potential censoring problem in our demand measure. If there is censoring in demand, our

parameter estimate for case size coverps would be deflated. However, we expect censoring to be negligible,

since stockout rates among DSD suppliers are typically lower in the CPG industry6.

• supply chain agepw, the explanatory variable associated with Hypothesis 2, denotes the average num-

ber of days spent by product p in the supply chain before shipment to all stores served from warehouse

w. We utilize several elements of information obtained from the data warehouse to construct this mea-

sure. They include shipments among AlphaCo warehouses for each product aggregated for 2011, individ-

ual physical inventory count records per warehouse-product for 2011, deliveries made to retail stores by

warehouse-product aggregated for 2011, and annual production quantity by product aggregated for 2011.

Using inventory count records, outgoing shipments, and deliveries, we calculate days-of-supply for each

product-warehouse combination. We also derive the supply chain network using production and shipment

data. As discussed in Section 3.3, supply chain age is computed as the cumulative days-of-supply across

the supply chain.

6 www.gmaonline.org/downloads/research− and− reports/DSDF inal111108.pdf


• rotations, the explanatory variable associated with Hypothesis 3, represents a binary indicator for

large format stores, which exhibit lower propensity for rotation negligence. Our field interviews reveal

that behavioral issues of field employees at small format stores lead to rotation problems. At small format

stores, rotation falls under the responsibility of both drivers as well as sales representatives, in which case

they expect each other to perform the task. This is in contrast with large format stores where only sales

representatives are responsible for rotation. Audit data supports this claim. In Online Appendix, we provide

statistical analysis showing correlation of rotation problems with the store type (large versus small format

store).

• min order rule coverps, the explanatory variable associated with Hypothesis 4, represents the length

of time mean demand is covered by the excess inventory generated by the minimum order rule. We measure

the excess inventory as the count of orders with quantity equal to either 15 or 75 cases, the two minimum

quantities imposed by AlphaCo on small and large format stores, respectively, divided by the total count

of orders by store. Since we have a large amount of transaction data, we calculate this measure using

detailed order data for the first quarter of 2011 only, assuming that the distribution of order sizes remains the

same throughout the year. We divide this excess inventory measure by mean consumer demand to calculate

min order rule coverps. Mean consumer demand is approximated by net deliveries (i.e. deliveries after

saleable and unsaleable returns are deducted).

• si(j)p is a binary variable indicating whether incentive program j was applied to product p. AlphaCo

stores incentive program data in spreadsheets and picture files. Spreadsheets contain names of the incentive

programs, their dates of effectiveness, a list of products or product groups covered, and rewards offered to

sales representatives. Associated with each incentive program, a picture file illustrates the corresponding

information in a poster. We include nine incentive program in our data set. We allow a time lag between

the dates of an incentive program and the occurrence of expiration because expiration associated with an

incentive program is likely to occur with a delay depending on the shelf life of the product and on the time

required to remove expired product from shelves and return them to the manufacturer. Thus, we include

incentive programs offered in the last six months of 2010 and the first six months of 2011 in our data.


Table 3 provides information about the characteristics of incentive programs. The incentive programs

differ from each other in the times of the year when they are applied, the type of growth targets, and the set

of products to which they are applied. For instance, incentive program 1 is active in the eighth and ninth

months of 2010, focuses on a specific brand consisting of 15 products with relatively short shelf lives, and

has an aggressive growth target of 20%. The median, maximum, and minimum shelf lives are 13, 14, and

12 weeks. Since this incentive program takes place close to the end of 2010, we expect most of the returns

due to expiration to take place in 2011. Incentive program 2 is valid during the seventh and eight months of

2010. It has a narrower scope compared to the other incentive programs and includes only nine products of

a specific brand. Their median, maximum, and minimum shelf lives are 30, 52, and 26. Due to their long

shelf lives, we expect their associated returns from expiration to occur in 2011. Incentive program 2 has a

mild growth target with 5%. Similar details for all nine incentive program types are presented in Table 3.

si(j)p is exogeneous in our model, because selection criteria for incentives does not have any relationship

with expiration or any other variables included in our model, although incentives are not randomly decided.

Selection criteria of sales incentives varies. Some incentives include products that already have high market

penetration and brand dominance over the competitor, while some include products that AlphaCo intends

to improve the market penetration. Some incentives are funded by manufacturers that use AlphaCo’s distri-

bution network and some focus on particular flavors only (e.g., cherry) or diet products.

• forecasting task complexityr is the explanatory variable associated with Hypothesis 6. It represents

the complexity of forecasting demand as described in Section 3.2. Using deliveries by route, store and

product aggregated for 2011, we measure forecasting task complexityr as the count of store-product

combinations in route r. Since routes are determined based on locational proximity of stores for transporta-

tional efficiency, which is not related to expiration or the variables included in our model, we do not expect

endogeneity in this measure. This variable is scaled in our model (by dividing into 100) to make reviewing

of parameter estimates easier.

• demand variationps is a control variable representing the coefficient of variation of the warehouse

demand for product p at warehouse w, which is calculated based on total monthly shipments. This measure


captures several dynamics related to expiration including retail promotions following a hi-low pricing, sea-

sonality, and forecasting difficulty. When products go off promotion or go off season, excess inventory may

be put in the backroom, which increases the chances of rotation issues and mismanagement of inventory.

Also, a high variation of the warehouse demand signals a high variation of the store demand which makes

forecasting difficult resulting in excess inventory at the stores.

• shelf lifep is a control variable indicating the shelf life of product p in weeks. The data are obtained

from the products table in the data warehouse.

• st(k)s is a binary control variable indicating whether store s is of type k. There are eight different store

types: supermarkets, convenience stores & gas stations, other grocery, dollar discount stores, drug stores,

mass merchants, club stores, and supercenters.

Table 5 presents summary statistics of all variables. Gas stations and convenience stores make up 46%

of the data set, supermarkets 22%, other grocery channel 10%, dollar discount stores 4%, drug stores 11%,

mass merchants 4%, super centers 3%, and club stores 0.024%. The annual number of returns due to expi-

ration for a store-product has an average of 2.86 units and a median of zero. About 80% of the return data

consist of zeroes indicating a large mass at zero. The annual number of deliveries made per store-product

has an average of 384.81 units and a median of 120. Supply chain age has an average of 28.19 days and a

median of 22.79 days. The average is higher than the median showing positive skewness. Case size cover

ranges between 0 and 24, with an average of 0.37 and a median of 0.09. Shelf life ranges between 10 and

104 weeks, with an average of 21.6 and median of 27.71 weeks. The measure for forecasting task complex-

ity has an average of 4046.64 and a median of 4463, meaning that the median sales representative manages

4463 store-product combinations. The average value of the rotation measure is 0.287, representing the por-

tion of store-SKUs at large format stores. The average and median values of minimum order rule cover are

0.195 and 1.43, respectively. Out of nine sales incentive programs, programs 1, 2, 3, 4, 6, and 9 cover 3%,

3%, 4%, 6%,6%, and 9% of the data points, respectively. Incentive programs 5, 7, and 8 are more prevalent

and include 22%, 13%, and 23% of the data points. The median case size cover is 0.09, which implies that

a one-case shipment covers 0.09× 365 = 32.85 days of demand at the median. On average, this number is

0.37, corresponding to 135 days. This large gap is due to a skewed distribution of demand rate.


We exclude some of the routes due to data accuracy concerns. Prior to 2011, the versions of the AlphaCo’s

hand held system allows product returns to be recorded only in full cases and not in units. For example,

if there were only two units of expiration found of a given product that has 12 units/case configuration,

the sales representative waited until there are 12 units (i.e. 1 case) of the same product in the back room

before they are returned to the warehouse. Alternatively, it was common to combine different flavors of

the same item together and record it as one case return, since store managers usually do not want waste

waiting in the back room. This did not impact the credit given to the customer; since same price items were

being combined together, however, return records were not always accurate. In 2011, AlphaCo upgraded

the handheld software to allow single unit returns. We notice that some sales representatives do not use this

new feature. For this reason, we exclude the routes that do not contain any single unit returns in 2011, which

is about 10% of all routes, from the analysis based on the belief that their return records may not be reliable.

4.2 Zero-Inflated Negative Binomial Regression Model

We examine different count models to find the most suitable specification for predicting expiration: bino-

mial, Poisson, negative binomial and their zero-inflated generalizations, namely, zero-inflated binomial

(ZIB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models. A linear predictor

that is common across all models forms the basis for these six models. The predictor contains the variables

associated with six hypotheses about case size, inventory aging, rotation, minimum order rule, sales incen-

tives, and forecasting task complexity, as well as control variables for shelf life, demand variation, and store

types. Let X(i) denote the i-th row of the data matrix X and β denote the vector of coefficients for the

explanatory variables. Then we set up the predictor as follows:

X(i)β = βk · st(k)(i)s +β1 · case size cover(i)ps +β2 · supply chain inventory age(i)pw

+β3 · shelf life(i)p +β4 · forecasting task complexity(i)r +β5 · demand variation(i)pw

+β6 ·min order rule cover(i)ps +β7 · large format store(i)s +β8+j · si(j)(i)p . (1)

Here, i indexes observations in our data set, k indexes store types, and j indexes sales incentive programs.

Products, routes, stores, and warehouses are denoted by p, r, s, and w, respectively. Finally, βk are the fixed

effects for each store type.


Ideally, the functional specification of the predictor should be derived from a theoretical model of expi-

ration. Unfortunately, this is difficult because of the complexity of the theoretical model. Thus, we analyze

different count models, since our response variable takes non-negative integer values, and include zero-

inflated models as part of our evaluation, since zero counts account for 80% of the observations in our data

set. In all six models, predictions represent the percentage of expiration for each store-product combination.

In a binomial model, the delivered volume serves as the number of Bernoulli trials and the expired volume

as the number of successes over an extended period. To establish a similar upper bound on the estimate

of the response variable with other models, we utilize deliveryp,s as an exposure variable. The exposure

variable enters the data matrix as an offset with a log transformation and its parameter is constrained to one.

Let γ denote the vector of coefficients for the explanatory variables in the zero-inflation part of the mixture

models ZIB, ZIP, and ZINB. The following regression forms represent the six models we examine:

E[return(i)

ps

delivery(i)ps

] = delivery(i)ps ·exp(X(i)β)

[1+ exp(X(i)β)](2)

E[return(i)ps ] = delivery(i)ps · exp(X(i)β) (3)

E[return(i)

ps

delivery(i)ps

] = delivery(i)ps ·1

[1+ exp(X(i)γ)]· exp(X(i)β)

[1+ exp(X(i)β)](4)

E[return(i)ps ] = delivery(i)ps ·

1

[1+ exp(X(i)γ)]· exp(X(i)β) (5)

The binomial and ZIB models take the form (2) and (4), respectively. The Poisson and negative bino-

mial regressions are both represented by the specification (3), while model (5) specifies the ZIP and ZINB

models. In zero-inflated models, exp(X(i)γ/[1+exp(X(i)γ)] represents the probability that zero expiration

occurs with observation i.

We estimate our models in the statistical language R version 2.14.2 (R Core Team 2012) utilizing pack-

ages stats, MASS (Venables and Ripley 2002), and pscl (Zeileis et al. 2007). We use the glm func-

tion for the binomial and Poisson regressions, glm.nb function for the negative binomial regression, and


zeroinfl function for the ZIP and ZINB regressions. Since zeroinfl does not support the zero-inflated

binomial model, we develop our own function in R to estimate model parameters for the ZIB model. Our

approach is based on the Expectation-Maximization (EM) algorithm (Hall 2000, Lambert 1992). To calcu-

late the standard errors from this model, we use the hessian and numericGradient functions in R

that are part of the numDeriv and maxLik packages to compute the variance-covariance matrix and the

estimating functions. To calculate marginal effects for the binomial, Poisson, and negative binomial mod-

els, we use the mfx (Fernihough 2014) package while we develop our own functions for the ZIB, ZIP, and

ZINB models. Following the Delta method, we build our own functions in R to calculate the standard errors

of the marginal effects for the ZIB, ZIP, and ZINB models.

5. Results

In Section 5.1, we test the hypotheses and interpret the estimation results from the ZINB model, and conduct

comparisons with alternative models, alternative samples, and varying store and SKU types. Lastly, we

evaluate the relative impact of different explanatory variables included in our model. In Section 5.2, we

investigate the effect of sales incentive programs on demand rate and expiration in more detail. In Section

5.3, we examine potential reverse causality in the relationship between supply chain aging and expiration.

Finally, in Section 5.4, we present a counterfactual analysis to estimate the effects of different types of

managerial actions on the occurrence and cost of expiration.

5.1 Estimation results

The estimates of the ZINB model show that Hypotheses 1, 2, 4, 5, and 6 are supported by our data. We first

present the results for these hypotheses, then discuss Hypothesis 3 which is not supported.

Table 6 shows the marginal effects of the ZINB model, as well as standard errors clustered at the prod-

uct, warehouse-product, store, and route levels. For tests of statistical significance, we use the standard

errors of the cluster that is at the same level as the explanatory variable. For example, since supply chain

age is a warehouse-product level variable, we use standard errors clustered at the warehouse-product for

testing significance. Following this approach, standard errors that correspond to the explanatory variable

are highlighted in Table 6. We observe that the marginal effects for case size cover, supply chain aging,


min order rule cover, and forecasting task complexity are all positive and statistically significant at

p < 0.01, which supports Hypotheses 1, 2, 4, and 6. Among the sales incentive program variables, si(1),

si(6), and si(8) have positive marginal effects and are statistically significant at p < 0.1. Thus, Hypothesis

5 is supported by three of the nine sales incentive programs. A common characteristic of these three sales

incentive programs is that the median shelf life of their products is relatively short with 13 weeks, as illus-

trated in Table 3, in comparison with the median shelf life of products from si(2), si(4), si(5), and si(9),

which are 30, 78, 26, 26 weeks, respectively. Furthermore, we observe that si(6) is a display type incentive

and incentives si(1) and si(8) require growth with an aggressive target of 20% and up to 25%, respectively,

which contrast with AlphaCo’s 3% overall historical growth. Hence, we conclude that incentives covering

products with shorter shelf lives, when they are display types incentives or when they require aggressive

growth targets, lead to higher amounts of expiration.

In comparing the sizes of marginal effects of these variables, we find that case size cover has the largest

impact on the amount of expiration, followed by manufacturer’s incentive si(1), demand variation, and min-

imum order rule. Among store types, club stores and drug stores have the largest occurrence of expiration,

and supercenters have the least.

The marginal effect of rotation is negative, in alignment with our expectations, but it has p = 0.35,

rejecting Hypotheses 3. An insignificant relationship between rotation and amount of expiration might

occur because rotation is correlated with business types bt(k). Table 4 compares the count of stores across

route types, our measure for rotation based on audit data, and eight different store types. Accordingly,

23%, 96%, 98%, 95%, 95%, 22%, 93%, and 3% of supermarkets, gas stations and convenience stores,

dollar discount stores, drug stores, mass merchants, club stores, and supercenters, respectively, fall under

small format routes. A chi-squared test concludes that an equal count of store types across business types

is rejected at p < 0.001, suggesting that store types and business types are correlated. As a result, bt(k)

fixed effects might already account for a part of the rotation effect. For example, drug stores primarily

fall under small format routes and exhibit a high probability of expiration with a marginal effect of 1.41%

while supercenters fall under large format routes, exhibiting a low occurrence of expiration with a marginal


effect of -2.38%. It is important to note that store type binary variable may potentially also account for over

ordering behavior at large format stores driven by transportation batching (i.e. full pallet shipments), which

offsets the effect of rotation.

Table 7 shows estimation results for the alternative models, i.e., binomial, Poisson, negative binomial,

ZIB, ZIP, and ZINB. Log likelihood values show that the ZINB model has the best fit to our data set.

In general, zero-inflated versions of all count models perform better: ZIP regression performs better than

Poisson with log likelihood values of -3,012,715 vs. -6,244,122, ZINB regression performs better than the

negative binomial regression with log likelihood values of -1,020,582 vs. -1,071,307, and ZIB performs

better than the binomial regression with log likelihood values of -2,421,080 vs. -5,178,333. This shows

that zero-inflated models are effective in addressing excess zero points in our data set. We use the ZINB

model because it gives us the best log likelihood value and the most unbiased distribution of residuals,

while addressing overdispersion7. It is also useful to note that the marginal effects are similar across the

six models, but are not identical which is expected since each model is based on a different distributional

assumption of the response variable.

To evaluate the impact of proposed drivers and understand the extent to which the ZINB model explains

variation in observed expiration, we divide our data into two subgroups, as no-expiration and expiration,

and calculate how much of the difference can be explained by the estimated ZINB model. We find that

observed expiration amount in the expiration subcategory would be 59% less if the explanatory variables

in the expiration subcategory were equal to their mean values in the no-expiration subcategory. Thus, the

model explains 59% of the variation in product expiration across observations.

Table 2 compares the summary statistics of our sample and the full dataset to evaluate the representative-

ness of our sample. We test the null hypothesis that sample mean of a given variable is equal to the mean

of the variable in the full dataset. A z-test at p < 0.001 fails to reject the null hypothesis for most variables

including returnps, deliverps, bt(massmerchant), case size cover, supply chain aging, shelf life,

7 The dispersion parameter in both the negative binomial and ZINB models is statistically significant at p < 0.001 suggesting the

existence of overdispersion in our data. However, a likelihood ratio based Vuong test favors ZINB model over the negative binomial

model at p < 0.001.


si(j), demand variation. Therefore, we conclude that our sample represents the full dataset. In addition,

we perform robustness analysis on our hypotheses tests taking three additional samples from the complete

dataset. Our original sample is based on 10,000 randomly selected stores. Each additional sample covers

randomly selected 14,688 stores which are mutually exclusive and are not part of the original sample. Table

7 presents the marginal effects and standard errors from the ZINB model for all four samples. We find that

our hypotheses test results still hold across three additional samples. Figure 4 compares the marginal effects

from four samples, which shows that estimates overlap for most variables, with the exception of the fixed

effect for club stores.

We investigate whether results from the ZINB model vary across stores and major SKU types. Table

9 presents the results for three subgroups of data: small format stores only, SKU category A, and SKU

category B. These SKU categories are the two largest categories generating 88% of the overall sales. We find

that in small format stores, inventory aging in the supply chain affects expiration with a greater magnitude,

with a marginal effect of 0.32%, higher than the marginal effect from the full dataset, which is 0.18%. This

could be explained by differences in product mix at small versus large stores. Otherwise, both types of

stores are served from the same stock in the warehouse, thus, inventory aging does not differ for the same

product placed at a small format store versus a large format store. Moreover, sales incentive programs are

more influential at small format stores, with marginal effects of 7.63%, 2.01%, and 2.72% based on the

small-format-store-only dataset for si(1), si(6), and si(8), respectively. These marginal effects are higher

compared to the marginal effects from the full dataset, which are 6.37%, 1.29%, and 1.84% for si(1), si(6),

and si(8), respectively. The greater impact of sales incentives at small format stores might be due to sales

representatives picking small format stores over large format stores in placing extra inventory required by

the sales incentive program. This possible general preference of small format stores for the execution of the

incentive programs is consistent with our observations from our field trips. Independently run stores, like

many convenience stores, provide more flexibility to the sales representatives in their inventory placement

decisions since there is no centralized system, like in chain stores, imposing standard planograms. For

this reason, sales representatives might find it easier to place the extra inventory required by the incentive

programs at small format convenience stores, since these stores require less sales effort.


The results exhibit greater differences across two SKU groups we evaluate: group A and group B. For

example, case sizes are more influential in group A than in the full dataset and less influential in group

B than in the full dataset (marginal effects for case size cover are 6.39%, 2.1%, and 4.31% for group A,

group B, and full datasets, respectively). This difference in the effect of case size inventory might partly be

explained by the difference in variation of case size cover between the two datasets: standard deviation of

case size cover for group A, group B, and full datasets are 1.8, 1.4, and 1.7, respectively. Nevertheless, this

suggests that group A products can benefit a case size reduction more than group B products. For group B,

marginal effect for minimum order rule cover is negative and insignificant; on the other hand, for group

B the marginal effect is 2.02% which is higher than 1.02%, the marginal effect at the full dataset. This may

be because sales representatives might choose group A SKUs to inflate order quantities since group A is

considered to be the core product group for AlphaCo. However, selecting products with longer shelf lives

among top selling SKUs might be a better strategy when complying with the minimum order rules, which

would minimize the risk of expiration.

To evaluate the relative impact of different explanatory variables included in our model, we com-

pare the increase in the Akai Information Criterion (AIC) value when variables are excluded from

our model. Accordingly, the magnitude of the effect of explanatory variables in descending order are

54122, 1685, 1613, 1514, and 249, respectively, for case size cover, si(1,6,8), supply chain aging,

minimum order rule cover, and forecasting task complexity. We conclude that case sizes are the

most influential source of expiration by a large margin.

5.2 Effect of Manufacturer’s Incentive Programs for the Salesforce

In this section, we analyze the interaction of sales incentive programs, demand, and probability of expira-

tion and discuss the implication of this interaction on our results. We redefine the notation for clarity of

exposition. For product p in store s, let Bps denote the baseline demand in the absence of a sales incentive

program, Sp denote the incentive program indicator, and Dps denote the demand in the presence of a sales

incentive program.

Sales incentive programs may increase expiration but intends to stimulate demand. We represent the first

relationship as

Dps =Bpsθ (6)


We expect θ ≥ 1 where θ = 1 when Sp = 0 and θ > 1 when Sp = 1. Also let Eps denote the expiration

amount observed for product p at store s, Xps denote the matrix representing the explanatory variables

for expiration excluding the variables for sales incentive programs, case size cover, and minimum order

rule cover, and f() denote the link function used in the regression model. In the absence of sales incentive

programs, the model of expiration is:

Eps = f(Xpsβ+case sizep

Bps

γ1 +min order rules

Bps

γ2) (7)

min order rules denotes the percentage of orders where order quantity is equal to the minimum order

size for store s and case sizep denotes the number of units contained in one case of product p. We defined

case size coverps ascase sizep

Bps

and defined min order rule cover ps asmin order rules

Bps

.

Hypothesis 5 proposes that manufacturer’s sales incentive programs impact expiration. Thus, including

Sp in the model for Eps yields


Bps

γ1 +min order rules

Bps

γ2 +Spη), (8)

where η denotes the coefficient of Sp. Further, replacing Bps with the observed demand Dps gives us:


Dps

γ1θ+min order rules

Dps

γ2θ+Spη) (9)

θ can not be identified from equation (9). Moreover, our estimates for the case size cover and

minimum order rule cover will be inflated by θ times if sales incentives in fact increase demand (if

θ > 1).

Our analysis here shows that we overestimate the effect of case sizes and minimum order rule on the

amount of expiration if sales incentives stimulate demand.

5.3 Endogeneity Between Supply Chain Inventory Aging and Expiration

The relationship between supply chain aging and expiration can be prone to reverse causality. Expiration

could lead to inflated estimates of mean demand at warehouses, which could lead to higher warehouse

inventory levels, and thus higher supply chain aging. In this section, we examine the relationship between

supply chain aging and expiration, and test whether expiration causes supply chain aging.


We instrument supply chain inventory aging using forecast errors at the warehouse-product level.

AlphaCo plans supply chain inventory using a forecast of shipments from warehouses to stores. Safety

stock levels at the warehouses are calculated based on forecast error. Therefore, poor forecasting, through

higher safety stock, can cause excess warehouse inventory; as a result, products, on average, spend longer

time at the warehouse. We find that the correlation coefficient between warehouse aging and forecast error

is 0.2, which indicates that forecast error is an appropriate instrument for supply chain aging. We perform

a two-stage control function estimation to model endogeneity (Imbens, Wooldridge 2007). Table 10 reports

results from this estimation. The first stage estimation involves an ordinary least squares model in which we

regress supply chain aging on the explanatory variables and also the forecast error. We add the residuals,

labeled as vhat in Table 10, from this model to the second stage ZINB model. We find that the estimate

of this residual is statistically significant which provides evidence for endogeneity. Therefore, we use the

second stage ZINB model in subsequent counterfactual analysis, in which we quantify the impact of four

types of initiatives.

5.4 Counterfactual Analysis

In this section, we utilize the estimation results to examine remedies that AlphaCo can pursue to reduce

expiration. We look at changes in four areas: (1) case size, (2) supply chain aging, (3) frequency of replen-

ishment, and (4) manufacturer’s incentive programs for the sales-force.

To assess the reduction in the amount of expiration associated with a change, we compare the expected

expired volume for two scenarios, before and after the change. For example, if current expected expired

volume is 100 units and expected expired volume with the change is 70 units, we conclude that the change

reduces expiration by 30%. We use a training data set for parameter estimation and a test data set for

predictions associated with different scenarios in our counterfactual analysis. The training data set is our

original sample including 869,651 data points; the test dataset is the rest of the observations in our full

dataset, which consists of 5,041,210 data points. Let Xtest denote the data matrix based on the test dataset

for the count and inflation components of the ZINB model associated with a given scenario and let i index

the data points that are impacted by the change. βtraining and γtraining are the parameters obtained from the

training data set. We calculate the expected expired volume as:

Author: Article Short Title30 00(0), pp. 000–000, c© 0000 INFORMS∑

i

E[returntesti ] = deliverytesti · exp (Xtest

i βtraining)[1/(1+ exp(Xtesti γtraining))]. (10)

To project the reduction in expiration amount onto monetary benefits for AlphaCo, we use data from

AlphaCo’s 2010 waste study. The study gives us an approximation of the total cost of unsaleables, including

the cost of goods sold, sales & delivery cost, as well as the reverse logistics cost associated with unsaleables

products. In addition, our data indicate that 65% of unsaleables occur due to expiration and the remaining

35% occur due to product damage. To extrapolate the monetary value of benefits, we first express the reduc-

tion in expiration as a proportion of the overall estimated expired volume in our dataset and later convert

it into a monetary value by multiplying this proportion with 65% of total cost of unsaleables provided by

AlphaCo. For instance, if a change corresponds to a reduction in expired volume of 3,000 cases, and the total

number of estimated expired volume is 6,596,095 cases in our data set, then we multiply 3,000/6,596,095

with 65% of the total cost of unsaleables to find the monetary benefit of this change.

It is important to note that four types of initiatives we evaluate do not apply to mutually exclusive datasets.

As a result, some data points are considered more than once when assessing different initiatives. Our goal

with this approach is to evaluate the initiatives independently in order to compare their impact.

Case Size: We find that reducing case sizes from 24 units to 12 reduces expiration volume by 33.6% for

products that are currently packed in 24 unit cases. Such products make up 26% of the data points in our

data set. This corresponds to a $5.09M reduction in expiration cost. AlphaCo concludes that this benefit is

substantially higher than the expected increase in product handling cost. Hence, the management of case

sizes is a significant opportunity for AlphaCo to improve its bottom-line. By quantifying the benefits of

smaller case sizes, our analysis provides a basis for a business case to pursue changes in manufacturing and

business processes. AlphaCo can consider implementing this change either by reducing case sizes directly

or developing a modular case that can be split in half and also be ordered in half cases at stores with low

demand.

As discussed earlier, case size configurations are usually set for a package (i.e., SKU group) which

includes SKUs with different flavors (e.g., blueberry, peach) but same container type (e.g., glass bottle) and

size (e.g., 20oz). To maintain configuration consistency within a package, a firm can choose to reduce case


sizes for select packages (e.g., overall slow moving packages), in which case we expect to see some level

of expiration for other packages.

Supply Chain Aging: Our analysis shows that reducing days of supply in the supply chain by 1 week

corresponds to 5.9% reduction in expiration volume and $5.58M in expiration cost. In this calculation, we

consider no change for the data points that already have less than one week of aging; such data points

constitute 2.7% of the observations in our data set. Figure 3 illustrates the distribution of supply chain age

in our data set. The majority of the data points have more than 7 days of aging.

Through batch size reduction in manufacturing and transportation, inventory aging in the supply chain

can be alleviated. However, batching provides cost advantages, as discussed earlier. Therefore, it might be

expected to have some level of expiration when optimum batch sizes are practiced.

Finally, manufacturers and retailers often try to reduce inventory, usually under pressure from their finan-

cial planners, in order to decrease working capital requirements. Our study suggests that the analysis for

these efforts need not be limited to the opportunity cost of the working capital. It is also important to con-

sider the benefit of inventory reduction on the occurrence of expiration in order to get a more thorough

picture of the benefits.

Frequency of Replenishment: To assess the cost of order inflation that occurs due to the minimum

order rule, we examine the stores that have positive values for min order rule, indicating order(s) at

the minimum order level. We estimate the change in expected expiration volume for such stores when

min order rules is set to zero. These data points make up 41.6% of our data set.

We find that the expected expiration reduces by 17.5% when orders are not inflated to meet the minimum

order rule requirement. Further, this difference corresponds to a monetary value of $2.87M. As a remedy,

AlphaCo can identify such stores using min order rule as a metric on a periodic basis and reduce their

visit frequency accordingly. Considering the significant savings opportunity and relatively small effort this

remedy requires, this is a worthwhile initiative to pursue for AlphaCo.

AlphaCo’s sales representative visit stores according to a pre-determined schedule, such as everyday,

twice a week, once a week, once every other week, once a month, etc. Reducing the visit frequency in


certain cases, for instance from once a week to once every other week, may pose a stockout risk because the

available shelf space and backroom space may not be sufficient to cover extra days of demand, especially

in small stores. Therefore, in pursuing this initiative, it is beneficial to focus on stores with higher scores

of min order rule. Since it may not be cost effective to reduce the visit frequencies of all stores where

minimum order rule is binding, we could observe some level of expiration for the stores where we do not

make any changes.

Manufacturer’s Incentive Programs for the Sales-force: We consider sales incentive programs 1, 6,

and 8, which were significantly associated with higher expiration in Section 5.1. These incentive programs

affect, respectively, 2.8%, 22.9%, and 0.43% of the data points. We find that eliminating these incentive

programs, i.e., setting si(j) = 0, results in 51.1%, 22.3%, 28.5% less expiration, and monetary savings of

$3.32M, $1.98M, and $13.13M, respectively.

As a remedy, AlphaCo’s marketing organization can consider waste implications in the design of these

incentive programs. For example, sales targets for products with shorter shelf lives can be selectively set to

more moderate levels. For instance, a goal of 10% increase in sales as opposed to a goal of 20% increase

should reduce expiration. In addition, sales representatives’ can be assisted with the execution of incentive

programs. For example, a decision support tool suggesting the stores that have higher likelihood of selling

the type of products included in the sales incentive programs can be useful in ensuring that additional

inventory in the market translates to consumer purchases.

6. Concluding Remarks

Product expiration is an important problem with implications for retailers, manufacturers, and supply chain

managers. Using data for a CPG manufacturer and a network of retail stores, we show that expiration can

occur due to various causes related to the manufacturer or the retail stores, such as case size, supply chain

aging, shelf life of products, manufacturer’s incentive programs for the sales-force, minimum order rules,

and forecasting task complexity. The amount of expiration also varies across retail store formats and groups

of products. Counterfactual analysis presented in the paper shows that a reduction in case size, even at the

margin from 24 units to 12 units, can lead to a substantial reduction in expiration. Other variables examined


in the counterfactual analysis include supply chain inventory, sales incentive programs, and frequency of

replenishment.

Overall, our research exposes and explains the complexity of expiration observed in retail supply chains.

We show that expiration does not occur solely due to demand uncertainty, but is also a supply chain coor-

dination problem. It can be reduced by better management at both manufacturers and retailers. Our paper

suggests a number of topics for future research. One topic is to examine perishable inventory from a multi-

location and channel perspective. The existing perishable inventory literature focuses on optimal inventory

policies at a single location. Also, supply chain aging targets and issuance rules for inventory at upstream

levels of supply chains have not been analyzed in current literature. These are relevant future research areas.

A second topic is to incorporate the implications of expiration in retail operations models, such as the

optimization of case sizes and shelf space allocation. The existing literature studies the shelf space allocation

problem considering cross-correlation of demand and substitution effects. If excess shelf space is allocated

to a slow-moving item or an item with a short shelf-life, then its effect on expiration should be included in

the cost of shelf space allocation.

The study of product expiration can also have impact on the literature in sustainable operations, which

has studied topics ranging from green product design to closed-loop supply chains (Kleindorfer et al. 2005).

Thus far, there have been many advancements in the management of durable goods, such as modular prod-

uct design (Chen et al. 1994, Singhal and Singhal 2002), remanufacturing (Guide Jr and Wassenhove 2001,

Ketzenberg et al. 2009), and lean manufacturing (Rothenberg et al. 2001). These practices are useful for

improving sustainability in discrete manufacturing environments more so than in process manufacturing,

which is the main manufacturing method in CPG companies. Thus, further research can examine supply

chain challenges related to expiration, such as disposal, short term production planning, inventory replen-

ishment, and package design.

Firms in the CPG industry can use our analysis as a framework to identify the drivers of expiration

that matter in their supply chain and construct business cases for initiatives to reduce expiration. Current

reimbursement policies practiced in the industry are two extreme schemes in terms of incentives they offer


to reduce unsaleables. For instance, either the manufacturer does not have an incentive to supply fresher

products to the retailer or the retailer does not have an incentive to manage store inventory better. This

is most likely because poor understanding of the sources of expiration makes it hard to share the cost

of unsaleables in an effective and fair way. Similar incentive issues exist within the same firm. Either no

function is held accountable for the cost or one function (e.g., sales or logistics) absorbs it regardless of the

source. Then, we see behaviors such as the sales force flooding the market with excess inventory or plant

managers not having any regard to waste implications when determining production batch sizes. These

behaviors can be altered by designing coordination mechanisms to reduce the occurrence of expiration.

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Figure 1 Root causes of expiration based on AlphaCo’s internal audit study

0%

5%

10%

15%

20%

25%

30%

35%

40%

Rota-on issue

Unknown Over ordered

Other Ship to trade short

coded

Retail price point

increase

Figure 2 Occurrence of different case sizes at AlphaCo

0

20

40

60

80

100

120

140

160

1 2 3 4 6 8 12 15 24

Num

ber o

f SKU

s

Case Size


Figure 3 Histogram of the supply chain age measure

Supply chain age (in days)

Fre

quen

cy

050

000

1000

0015

0000

0 20 40 60 80 100 120 140

Figure 4 Comparison of marginal effects across four samples

!10.00%&

!8.00%&

!6.00%&

!4.00%&

!2.00%&

0.00%&

2.00%&

4.00%&

6.00%&

(Intercept)*

st(gas*sta.on*or*convenience*store)*

st(other*grocery)*

st(dollar*discount)*

st(drug*store)*

st(mass*merchant)*

st(club*store)*

st(supercenter)*

case*size*cover*

supply*chain*age*

shelf*life*

si(1)*

si(2)*

si(3)*

si(4)*

si(5)*

si(6)*

si(7)*

si(8)*

si(9)*

forecas.

ng*task*complexity*

demand*varia.on*

minim

um*order*rule*cover*

rota.on*

Margina

l*effe

ct*

original*sample*

alterna.ve*sample*1*




Table 1 Simulated expiration amount by scenariounits per week actual/simulated simulated/baselineFIFO LIFO FIFO LIFO FIFO LIFO

shipments in units95% 0.0795 0.4187 96.033 18.234 1 5.26797% 0.1357 0.6416 56.261 11.899 1 4.72899% 0.2982 1.3565 25.602 5.628 1 4.549

shipments in cases95% 1.4738 2.0022 5.180 3.813 18.538 25.18597% 1.5924 2.2273 4.794 3.428 11.735 16.41399% 1.9146 3.0667 3.988 2.490 6.421 10.284

Table 2 Summary statistics of sample and full datasetsample full dataset

mean standard deviation median mean standard deviation median zreturn 2.8569 10.8220 0 2.8385 10.8450 0 (1.5799)delivery 385.0160 1107.4120 120 381.1510 1152.6820 120 (3.1269)**st(supermarket) 0.2242 0.4171 0 0.2226 0.4160 0 (3.6194)***st(gas station or convenience store) 0.4639 0.4987 0 0.4667 0.4989 0 (-5.1965)***st(other grocery) 0.0993 0.2991 0 0.0979 0.2972 0 (4.4264)***

st(dollar discount) 0.0419 0.2003 0 0.0466 0.2108 0 (-20.8902)***st(drug store) 0.1052 0.3068 0 0.1020 0.3026 0 (9.8691)***st(mass merchant) 0.0354 0.1847 0 0.0356 0.1853 0 (-1.2146)st(club store) 0.0002 0.0156 0 0.0010 0.0323 0 (-23.2015)***st(supercenter) 0.0299 0.1704 0 0.0276 0.1638 0 (13.2090)***case size cover 0.3678 1.7871 0.0863 0.3645 1.7605 0.0896 (1.7226).supply chain age 4.0266 2.6988 3.2555 4.0210 2.6968 3.2530 (1.9564).shelf life 27.7146 21.6039 21 27.6945 21.5935 21 (0.8664)

si(1) 0.0276 0.1638 0 0.0275 0.1636 0 (0.4246)si(2) 0.0264 0.1603 0 0.0263 0.1601 0 (0.3769)si(3) 0.0443 0.2057 0 0.0443 0.2059 0 (-0.2609)si(4) 0.0596 0.2368 0 0.0596 0.2368 0 (0.0249)si(5) 0.2240 0.4169 0 0.2237 0.4167 0 (0.6722)si(6) 0.0623 0.2417 0 0.0622 0.2415 0 (0.3636)si(7) 0.1338 0.3405 0 0.1343 0.3410 0 (-1.2714)si(8) 0.2288 0.4200 0 0.2285 0.4199 0 (0.6516)

si(9) 0.0043 0.0655 0 0.0043 0.0656 0 (-0.2196)forecasting task complexity 40.4746 19.3186 44.6300 40.6017 19.3905 44.8600 (-6.1126)***

demand variation 0.1794 0.1007 0.1628 0.1794 0.1012 0.1628 (0.2687)minimum order rule cover 0.1951 1.4277 0 0.1899 1.3598 0.0000 (3.5775)***rotation 0.2868 0.4523 0 0.2826 0.4503 0 (8.6140)***

Notes. .,*, **, *** Statistically significant at p=0.10, 0.05, 0.01,0.001 respectively.

Table 3 Characteristics of sales incentive programssi(1) si(2) si(3) si(4) si(5) si(6) si(7) si(8) si(9)

year 2010 2010 2010 2010 2011 2011 2011 2011 2011month(s) 8,9 7,8 6 5,6 1 2 4 6 3,4,5products included 15 9 35 24 108 61 82 165 4median shelf life 13 30 13 78 26 13 13 17 26maximum shelf life 14 52 39 104 104 36 26 35 26minimum shelf life 12 26 12 72 12 12 13 12 26type volume volume volume volume display display volume volume volumefocus brand brand flavor brand-flavor broad flavor brand broad brandtarget +20% +5% +15% +5% +5 to 25% fixed target

per brand in cases


Table 4 Count of stores by typessmall format large format total

st(supermarket) 318 1085 1403(23%) (77%)

st(gas station or convenience store) 4872 226 5098(96%) (4%)

st(other grocery) 1285 29 1314(98%) (2%)

st(dollar discount) 629 34 663(95%) (5%)

st(drug store) 1060 59 1119(95%) (5%)

st(mass merchant) 52 181 233(22%) (78%)

st(club store) 13 1 14(93%) (7%)

st(supercenter) 5 151 156(3%) (97%)

Table 5 Summary statistics of variablesEstimate Std. dev. Median Minimum Maximum % of zero points

delivery (exposure variable) 384.81 1106.96 120 1 102008 -return (dependent variable) 2.86 10.82 0 0 1408 80%Main variables of interest:case size cover 0.37 1.79 0.09 0 24 -supply chain age 28.19 18.89 22.79 0.06 247.05 -min order rule cover 0.195 1.43 0 0 83.333 58%forecasting task complexity 4046.64 1932.27 4463 14 9571 -rotation 0.287 0.452 0 0 1 -si(1) 0.03 0.16 0 0 1 97%si(2) 0.03 0.16 0 0 1 97%si(3) 0.04 0.21 0 0 1 96%si(4) 0.06 0.24 0 0 1 94%si(5) 0.22 0.42 0 0 1 78%si(6) 0.06 0.24 0 0 1 94%si(7) 0.13 0.34 0 0 1 87%si(8) 0.23 0.42 0 0 1 77%si(9) 0.004 0.07 0 0 1 99.6%Control variables:demand variation 0.179 0.101 0.163 0.060 1.540 -shelf life 27.71 21.60 21 10 104 -st(supermarket) 0.22 0.42 0 0 1 78%st(gas station or convenience store) 0.46 0.5 0 0 1 54%st(other grocery) 0.1 0.3 0 0 1 90%st(dollar discount) 0.04 0.2 0 0 1 96%st(drug store) 0.11 0.31 0 0 1 89%st(mass merchant) 0.04 0.18 0 0 1 96%st(club store) 0.00024 0.02 0 0 1 99.976%st(supercenter) 0.03 0.17 0 0 1 97%


Table 6 Estimation results for ZINB modelstandard error

marginal effect no cluster product warehouse-product customer routest(gas station or convenience store) -0.539% (0.00042)*** (0.00156)*** (0.00055)*** (0.00128)*** (0.00082)***st(other grocery) -0.432% (0.00048)*** (0.00109)*** (0.00058)*** (0.00144)** (0.00102)***st(dollar discount) -0.757% (0.00051)*** (0.00129)*** (0.00061)*** (0.00138)*** (0.00095)***st(drug store) 1.413% (0.00068)*** (0.00180)*** (0.00081)*** (0.00205)*** (0.00118)***st(mass merchant) 0.028% (0.00058) (0.00108) (0.00061) (0.00241) (0.00126)st(club store) 3.6% (0.01051)*** (0.00885)*** (0.00823)*** (0.01448)* (0.00401)***st(supercenter) -2.382% (0.00021)*** (0.00109)*** (0.00035)*** (0.00075)*** (0.00038)***case size cover 4.66% (0.00049)*** (0.00350)*** (0.00089)*** (0.00128)*** (0.00050)***supply chain age 0.176% (0.00005)*** (0.00021)*** (0.00007)*** (0.00008)*** (0.00006)***

shelf life -0.116% (0.00001)*** (0.00008)*** (0.00002)*** (0.00002)*** (0.00001)***si(1) 4.035% (0.00137)*** (0.01557)** (0.00268)*** (0.00126)*** (0.00049)***si(2) -0.329% (0.00080)*** (0.00484) (0.00103)** (0.00093)*** (0.00048)***si(3) -0.475% (0.00044)*** (0.00227)* (0.00070)*** (0.00041)*** (0.00027)***si(4) -0.372% (0.00116)** (0.00540) (0.00182)* (0.00170)* (0.00077)***

si(5) 0.034% (0.00035) (0.00352) (0.00070) (0.00036) (0.00024)si(6) 0.836% (0.00052)*** (0.00392)* (0.00087)*** (0.00059)*** (0.00032)***si(7) 0.009% (0.00033) (0.00261) (0.00060) (0.00036) (0.00021)

si(8) 1.477% (0.00037)*** (0.00347)*** (0.00069)*** (0.00045)*** (0.00022)***si(9) 0.592% (0.00167)*** (0.00498) (0.00239)* (0.00199)** (0.00093)***forecasting task complexity 0.012% (0.00001)*** (0.00002)*** (0.00001)*** (0.00003)*** (0.00002)***demand variation 1.124% (0.00109)*** (0.00848) (0.00220)*** (0.00114)*** (0.00072)***minimum order rule cover 0.803% (0.00034)*** (0.00185)*** (0.00049)*** (0.00090)*** (0.00020)***rotation -0.112% (0.00041)** (0.00063). (0.00056)* (0.00120) (0.00083)


Table 7 Marginal effects and standard errors of alternative modelsZINB ZIP ZIB Negative Binomial Poisson Binomial

log likelihood: (1,020,582 ) (3,012,715) (2,421,080) (1,071,307) (6,244,122) (5,178,333)pseudo R-squared: 7.56% 31.86% 51.46% 3.39% 24.18% 40.98%st(gas station or convenience store) -0.54% -0.1% -0.54% -0.54% -0.1% -0.49%

(0.001)*** (0.001) (0.001)*** (0.001)*** (0.0004)* (0.001)***st(other grocery) -0.43% 0.09% -0.37% -0.21% 0.17% -0.2%

(0.001)** (0.001) (0.001)** (0.001)* (0.001)** (0.001)*st(dollar discount) -0.76% -0.25% -0.58% -0.48% -0.14% -0.46%

(0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.0004)** (0.001)***st(drug store) 1.41% 1.02% 1.49% 0.86% 0.65% 1.32%

(0.002)*** (0.001)*** (0.002)*** (0.001)*** (0.001)*** (0.002)***st(mass merchant) 0.03% -0.2% -0.38% -0.02% -0.16% -0.35%

(0.002) (0.001)* (0.002)* (0.001) (0.001)** (0.001)**st(club store) 3.6% 2.65% 6.34% 1.65% 1.57% 5.57%

(0.014)* (0.012)* (0.022)** (0.006)** (0.005)** (0.021)**st(supercenter) -2.38% -0.94% -1.7% -1.58% -0.54% -1.29%

(0.001)*** (0.0003)*** (0.001)*** (0.0004)*** (0.0002)*** (0.0005)***case size cover 4.66% 1.43% 8.57% 1.76% 0.11% 6.62%

(0.0005)*** (0.0001)*** (0.0004)*** (0.0001)*** (0.000001)*** (0.0001)***supply chain age 0.18% 0.13% 0.2% 0.15% 0.1% 0.17%

(0.0001)*** (0.00004)*** (0.0001)*** (0.0001)*** (0.00003)*** (0.00006)***shelf life -0.12% -0.05% -0.11% -0.08% -0.03% -0.1%

(0.0001)*** (0.00005)*** (0.0001)*** (0.00004)*** (0.00004)*** (0.00007)***si(1) 4.04% 1.69% 3.55% 3.04% 0.67% 1.65%

(0.016)** (0.006)** (0.013)** (0.012)* (0.002)** (0.006)*si(2) -0.33% 0.03% -0.06% -0.14% -0.11% -0.11%

(0.005) (0.003) (0.004) (0.005) (0.001) (0.003)si(3) -0.48% 0.25% 0.13% -0.16% 0.24% 0.19%

(0.002)* (0.001). (0.002) (0.002) (0.001)* (0.002)si(4) -0.37% -0.41% 0.18% -0.35% -0.11% 0.37%

(0.005) (0.002). (0.006) (0.003) (0.002) (0.006)si(5) 0.03% 0.4% 0.32% 0.2% 0.41% 0.57%

(0.004) (0.002)* (0.003) (0.002) (0.001)*** (0.003)*si(6) 0.84% 0.44% 0.96% 0.5% 0.08% 0.36%

(0.004)* (0.002)* (0.004)** (0.002)* (0.001) (0.003)si(7) 0.01% -0.17% -0.02% 0.06% -0.22% -0.36%

(0.003) (0.002) (0.003) (0.002) (0.001)* (0.002)si(8) 1.48% 0.65% 1.34% 1.16% 0.32% 0.76%

(0.003)*** (0.001)*** (0.003)*** (0.003)*** (0.001)*** (0.002)***si(9) 0.59% -0.27% -0.52% 0.26% -0.19% -0.35%

(0.005) (0.002) (0.004) (0.003) (0.001). (0.003)forecasting task complexity 0.01% 0.01% 0.02% 0.01% 0.01% 0.02%

(0.00002)*** (0.00002)*** (0.00003)*** (0.00002)*** (0.00001)*** (0.00002)***demand variation 1.12% 0.62% 0.61% 0.94% 0.65% 0.26%

(0.008) (0.003). (0.007) (0.004)* (0.002)*** (0.006)minimum order rule cover 0.8% 0.23% 0.46% 0.26% 0.002% 0.23%

(0.0003)*** (0.0001)*** (0.0001)*** (0.0001)*** (0.000001)*** (0.00004)***rotation -0.11% -0.22% -0.07% -0.11% -0.14% -0.07%

(0.001) (0.001)*** (0.001) (0.001) (0.0004)*** (0.001)

Notes. .,*, **, *** Statistically significant at p=0.10, 0.05, 0.01,0.001 respectively. Standard errors are clustered according to the

appropriate cluster for each variable.


Table 8 Marginal effects and standard errors across samplesoriginal sample alternative sample 1 alternative sample 2 alternative sample 3

intercept -8.35% -8.06% -8.29% -8.5%(0.00076)*** (0.00062)*** (0.00061)*** (0.00062)***

st(gas station or convenience store) -0.54% -0.48% -0.22% -0.34%(0.00042)*** (0.00036)*** (0.00034)*** (0.00034)***

st(other grocery) -0.43% -0.48% -0.25% -0.42%(0.00048)*** (0.00040)*** (0.00041)*** (0.00040)***

st(dollar discount) -0.76% -0.9% -0.67% -0.7%(0.00051)*** (0.00038)*** (0.00041)*** (0.00041)***

st(drug store) 1.41% 1.23% 1.46% 1.34%(0.00068)*** (0.00055)*** (0.00057)*** (0.00055)***

st(mass merchant) 0.03% 0.31% 0.19% 0.67%(0.00058) (0.00051)*** (0.00049)*** (0.00055)***

st(club store) 3.6% -0.27% 0.39% -1.06%(0.01051)*** (0.00221) (0.00269) (0.00186)***

st(supercenter) -2.38% -2.3% -2.08% -2.28%(0.00021)*** (0.00018)*** (0.00019)*** (0.00019)***

case size cover 4.66% 4.55% 4.34% 4.63%(0.00049)*** (0.00039)*** (0.00038)*** (0.00040)***

supply chain age 0.18% 0.17% 0.18% 0.17%(0.00005)*** (0.00004)*** (0.00004)*** (0.00004)***

shelf life -0.12% -0.12% -0.12% -0.12%(0.00001)*** (0.00001)*** (0.00001)*** (0.00001)***

si(1) 4.04% 3.98% 3.7% 3.81%(0.00137)*** (0.00112)*** (0.00107)*** (0.00109)***

si(2) -0.33% -0.54% -0.41% -0.44%(0.00080)*** (0.00060)*** (0.00062)*** (0.00062)***

si(3) -0.48% -0.43% -0.46% -0.44%(0.00044)*** (0.00037)*** (0.00035)*** (0.00037)***

si(4) -0.37% -0.13% -0.24% -0.31%(0.00116)** (0.00103) (0.00097)* (0.00100)**

si(5) 0.03% 0.06% 0.03% 0.07%(0.00035) (0.00029)* (0.00028) (0.00029)*

si(6) 0.84% 0.78% 0.74% 0.77%(0.00052)*** (0.00042)*** (0.00041)*** (0.00042)***

si(7) 0.01% -0.01% -0.03% 0.01%(0.00033) (0.00027) (0.00026) (0.00027)

si(8) 1.48% 1.48% 1.38% 1.45%(0.00037)*** (0.00031)*** (0.00030)*** (0.00031)***

si(9) 0.59% 0.3% 0.45% 0.1%(0.00167)*** (0.00124)* (0.00126)*** (0.00118)

forecasting task complexity 0.01% 0.01% 0.01% 0.01%(0.00001)*** (0.00001)*** (0.00001)*** (0.00001)***

demand variation 1.12% 1.11% 1.03% 1.18%(0.00109)*** (0.00089)*** (0.00086)*** (0.00090)***

minimum order rule cover 0.8% 0.81% 0.88% 0.74%(0.00034)*** (0.00027)*** (0.00028)*** (0.00026)***

rotation -0.11% -0.29% 0.05% 0.01%(0.00041)** (0.00033)*** (0.00033) (0.00034)



Table 9 Marginal effects and standard errors by store and product typesbaseline small format stores only SKU category A SKU category B

intercept -9.47% -9.74% -19.41% -4.46%(0.00113)*** (0.00138)*** (0.00303)*** (0.00344)***

st(gas station or convenience store) -0.44% -0.63% 1.08% -0.33%(0.00043)*** (0.00066)*** (0.00110)*** (0.00067)***

st(other grocery) -0.33% -0.36% 0.88% -0.15%(0.00050)*** (0.00067)*** (0.00147)*** (0.00080).

st(dollar discount) -0.39% -0.7% -0.42% -0.03%(0.00059)*** (0.00068)*** (0.00144)** (0.00092)

st(drug store) 1.65% 1.46% 3.1% 0.14%(0.00074)*** (0.00094)*** (0.00173)*** (0.00086).

st(mass merchant) 0.12% 1.34% 0.24% 0.33%(0.00060). (0.00187)*** (0.00159) (0.00089)***

st(club store) 4.02% 3.91% 5.49% -1.13%(0.01162)*** (0.01129)*** (0.02444)* (0.00037)***

st(supercenter) -2.2% -2.26% -4.26% -0.92%(0.00021)*** (0.00120)*** (0.00058)*** (0.00037)***

case size cover 4.31% 4.33% 6.39% 2.1%(0.00052)*** (0.00055)*** (0.00156)*** (0.00074)***

supply chain age 0.54% 0.32% 1.47% 0.13%(0.00025)*** (0.00025)*** (0.00099)*** (0.00024)***

shelf life -0.1% -0.11% -0.18% -0.04%(0.00001)*** (0.00002)*** (0.00005)*** (0.00003)***

si(1) 6.37% 7.63% 4.3% NA(0.00247)*** (0.00298)*** (0.00242)*** NA

si(2) -0.81% -0.34% NA NA(0.00068)*** (0.00094)*** NA NA

si(3) -0.32% -0.16% -0.76% NA(0.00048)*** (0.00064)* (0.00096)*** NA

si(4) -0.52% -0.55% NA NA(0.00106)*** (0.00136)*** NA NA

si(5) -0.39% -0.17% 0.05% 0.32%(0.00043)*** (0.00053)** (0.00100) (0.00053)***

si(6) 1.29% 2.01% 0.31% NA(0.00091)*** (0.00128)*** (0.00119)** NA

si(7) 0.76% 0.92% 0.18% NA(0.00054)*** (0.00063)*** (0.00077)* NA

si(8) 1.84% 2.72% 2.3% 0.64%(0.00043)*** (0.00060)*** (0.00097)*** (0.00205)**

si(9) -1.55% -1.53% NA 0.02%(0.00101)*** (0.00147)*** NA (0.00238)

forecasting task complexity 0.01% 0.02% 0.03% 0%(0.00001)*** (0.00001)*** (0.00002)*** (0.00001)

demand variation 0.78% 0.05% 1.66% -0.33%(0.00114)*** (0.00148) (0.00282)*** (0.00406)

minimum order rule cover 1.02% 0.84% 2.02% -0.01%(0.00042)*** (0.00037)*** (0.00080)*** (0.00041)

rotation -0.22% NA -0.71% -0.06%(0.00041)*** NA (0.00098)*** (0.00062)

vhat -0.41% -0.21% -0.93% -0.13%(0.00025)*** (0.00026)*** (0.00101)*** (0.00025)***



Table 10 Results for two-stage control function estimation1st stage OLS model 2nd stage ZINB model

marginal effect standard error marginal effect standard errorintercept 3.11 (0.01520)*** -9.467% (0.00113)***st(gas station or convenience store) -0.22 (0.01167)*** -0.44% (0.00043)***

st(other grocery) -0.09 (0.01408)*** -0.331% (0.00050)***st(dollar discount) -0.62 (0.01732)*** -0.39% (0.00059)***st(drug store) -0.42 (0.01359)*** 1.649% (0.00074)***st(mass merchant) -0.01 (0.01532) 0.116% (0.00060).st(club store) -0.71 (0.17158)*** 4.022% (0.01162)***st(supercenter) 0.06 (0.01689)*** -2.2% (0.00021)***case size cover 0.06 (0.00178)*** 4.306% (0.00052)***supply chain age NA NA 0.541% (0.00025)***shelf life 0.02 (0.00016)*** -0.104% (0.00001)***si(1) -1.99 (0.01901)*** 6.365% (0.00247)***si(2) 1.91 (0.01641)*** -0.806% (0.00068)***si(3) -0.37 (0.01319)*** -0.316% (0.00048)***si(4) 0.48 (0.01698)*** -0.515% (0.00106)***si(5) 0.98 (0.00999)*** -0.392% (0.00043)***si(6) -1.29 (0.01463)*** 1.289% (0.00091)***si(7) -1.18 (0.00843)*** 0.762% (0.00054)***si(8) -0.46 (0.00824)*** 1.84% (0.00043)***si(9) 5.03 (0.05994)*** -1.548% (0.00101)***forecasting task complexity 0.00 (0.00022)*** 0.012% (0.00001)***demand variation -0.56 (0.03122)*** 0.785% (0.00114)***minimum order rule cover -0.01 (0.00220)*** 1.021% (0.00042)***rotation -0.03 (0.01129)* -0.223% (0.00041)***v hat NA NA -0.40777 (0.00025)***forecast error 0.24 (0.00154)*** NA NA


Online Appendix

Data Preparation

This section lists seven datasets we receive from AlphaCo’s data warehouse containing different information and

explains our data preparation efforts.

1. Yearly delivery and returns due to expiration by store-SKU for 2011. This data is the dependent variable in our

analysis. The dataset also includes the shipping warehouse, store type (large format versus small format), retailer

type (e.g., supermarkets, gas stations and convenience stores) and route information.

2. Monthly delivery data by SKU for 2011. We use this information to determine at which month a product is

introduced or discontinued. For example, if the first 3 months have no delivery, we conclude that the product is

new and introduced at month 4. We eliminate SKUs that do not have positive delivery for less than 11 months of

the year.

3. Yearly shipment data by SKU - from warehouse - to warehouse. Based on this data, we construct the supply

chain, which we use to calculate the cumulative days-of-supply (our supply chain aging measure).

4. Daily warehouse count data by warehouse-SKU. We aggregated this data at the year level to calculate the average

days-of-supply for each warehouse-SKU. In addition to the count data, we calculate total outgoing shipments by

summing total yearly shipments to other warehouses (from #3) and delivery to retail stores (from #1).


5. Daily order data by store for the first quarter of 2011. This data only includes the order size across different

products (i.e., order id - store id - order quantity). We used this data to calculate the percentage of orders where

order size is equal to the minimum order size to construct our minimum order rule measure. Since this data is

daily at the store level for all US, the dataset is very ; therefore AlphaCo only provided us the first quarter of

2011.

6. Products master file, which includes SKU ID, case size, shelf life, SKU category information.

7. Monthly delivery data by SKU-warehouse for 2011. We use this data to calculate the coefficient of warehouse

demand, our demand variation variable.

Rotation Measure

This section explains our instrument for shelf rotation.

Ideally, we need a measure representing compliance to the shelf rotation rule for all store-SKU combinations.

However, such data does not exist because compliance cannot be continuously monitored without incurring prohibitive

cost. Therefore, we analyze data from an audit study conducted by AlphaCo in 2010 to construct an instrument for

rotation.

The audit involved recording all incidences of unsaleable products in sampled store visits. When found, auditors

recorded a reason code associated with each occurrence of unsaleables, also indicating whether the shelf or the back

room inventory was rotated for each store and product. For stores, Customer IDs are recorded. However, products are

identified by imprecise product descriptions rather than SKU ID or UPC codes which limits our ability to match them

with products master table to extract any further information about them. Therefore, we aggregate the audit data at the

store level for analysis. The following table summarizes the count of stores by rotation record and by store type.

rotation issue no rotation issueLarge format 423 2686 3109Small format 1362 4430 5792

1785 7116

Accordingly, out of 1785 stores in which at least one unrotated product was found, 1362 are small format stores

(generating 76% of the problem) and 423 are large format stores. Furthermore, 1362 small format stores out of 5792

(23%) exhibit rotation problems compared to 423 out of 3109 (13%) large format stores. A chi-squared test rejects the

null hypothesis that rotation issues are evenly distributed across small and large format stores at p < 0.001 suggesting

that rotation problems and store types are correlated. We conclude that store type indicates rotation problems, therefore

we use store type as an instrument for rotation in our analysis.

Cross Sectional vs Time Series Analysis Using Seasonality

Due to data issues, we choose to perform our analysis based on annually aggregated cross sectional data as opposed to

granular data incorporating time series aspect of expiration. This section discusses these data issues.

The first issue is that return data does not map into its corresponding delivery/order. For a return transaction,

AlphaCo only captures a date, return type (expiration, damage versus saleable return), quantity, SKU ID, and store ID

and does not capture a delivery ID. In other words, AlphaCo’s systems link orders and deliveries, but not returns and


deliveries8. Then, in binomial terms, we do not know the number of successes (i.e., returns) associated with a given

number of trials (i.e., deliveries).

The other (and more important) consequence of the missing link between returns and deliveries is that we are not

able to construct an accurate time series, since we do not know how far to lag explanatory variables. According to

perishable inventory theory, the extent of lagging depends on the effective shelf life. Consider two products with

different effective shelf lives, one short and one long. For the short one, expiration amount in a given time period may

depend on deliveries/orders of two and three periods earlier; for the long one, it may depend on five, six, and seven

periods earlier. If we could link deliveries/orders with returns, we would know how far to go back to construct the time

series. An alternative approach is to use the product’s effective shelf life (i.e., shelf life minus aging) to determine the

extent of lagging for explanatory variables. However, aging is also an explanatory variable in our model and is time

variant. Then, lagging becomes complex; also, relationships among variables get complicated.

Suppose we can link deliveries/orders with returns and we perform a simple cross-functional analysis (unit of

measure being product-store-time). Then, we might be able to explain more of the variation in returns. However, a

panel analysis still would have been much more difficult. Suppose we construct a time series of n periods. According

to perishable inventory theory, how many of these n periods impact expiration depends on the effective shelf life. For

a product with a short effective shelf life, maybe only 3 periods of delivery/order are relevant; but for a product with

long shelf life, 6 periods are relevant. In other words, in theory, the impact of different periods varies across analyzed

units. Such variation is in contrast with what our model would predict, which would be an identical impact of a given

period. In short, a time series analysis is very complicated in our setting and absence of link between deliveries/orders

and returns makes time series analysis harder.

The second data issue we encounter is inaccuracies in the timing of return transactions. In practice, expired products

can be returned with a delay. For example, we have seen, in field visits, that it is not uncommon for en expired item to

be returned to the warehouse as late as 6 months after it expires. Return data does not capture this delay since it only

tells us the date of the return. Such delays are examples of incompliant practice and occur due to sales representatives’

negligence. We are not able to infer to what extent the timing of returns is inaccurate; however, there are two reasons

for us to believe that inaccuracy can be considerable. One reason is rotation audits. Expired products are usually

noticed and picked during shelf rotation and audit data tells us that negligence of rotation is not uncommon. Second

reason is the pattern we observe from periodic return data, as shown in the following figure.

8 Example: Suppose 12 cases of product A is delivered to store K on 3/4/2011 and 2 of these items expire in August 2011. Imaginethe sales representative generates a return for those 2 units on 8/30/2011. From the data, we do not know that the return transactionfrom 8/30/2011 is linked to the delivery from 3/4/2011.


0"

2000"

4000"

6000"

8000"

10000"

12000"

1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 11" 12" 13"

Total"returns"

Periods""(A"period"consists"of"4"weeks."Period"1"starts"from"January.)""

As seen in the above graph, winter periods exhibit higher returns. We know for a fact that off-peak winter periods

are chosen for annual planogram reset activities9, which give sales reps an opportunity to clean shelves off expired

items. Therefore, high return activity in winter periods can be partially explained by planogram resets.

Overall, we doubt the validity of a granular data analysis (either through a panel or a time series approach) due to

the data issues discussed above. For this reason, we choose to conduct our analysis with annually aggregated data.

In conclusion, a cross sectional analysis with annually aggregated data is more appropriate for our setting for the

following reasons:

• It alleviates the data problem of delayed returns.

• Incorporating a time dimension in our model is very complicated. This is because a delivery impacts expiration

with a time lag. According to perishable inventory theory, this lag is a function of effective shelf life, which is an

explanatory variable in our model and varies across products-warehouses.

• We expect demand approximation (i.e., net deliveries) to be more accurate with annual aggregation, since back-

room inventory would be more negligible with annual demand compared to quarterly or monthly demand.

• We are seeking actionable results with our analysis (e.g., reduce case size, reduce store visit frequencies) and

incorporating a time dimension in our analysis does not serve this purpose. For example, re-changing case sizes every

season would not be economical for a manufacturing system. Therefore, we believe that a granular analysis does not

add value to our research.

9 A reset activity involves cleaning the shelf off all products and re-stock the shelf in accordance with centrally establishedplanograms. Resets are needed since sales representatives may deviate from planograms over time, typically due to new productintroductions and promotions. Since sales reps are a lot more busy during summer times due to high sales volume and in-storedisplay activities, winter periods are preferred for annual reset activities.

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Drivers of Product Expiration in Retail Supply Chains - INSEAD

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