Impact of Rising Fuel Cost on Perishable Product Procurement

8/4/2019 Impact of Rising Fuel Cost on Perishable Product Procurement

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JOURNAL OF BUSINESS LOGISTICS, Vol. 30, No. 1, 2009 223

IMPACT OF RISING FUEL COST ON PERISHABLE PRODUCT PROCUREMENT

by

Ram N Acharya

New Mexico State University

Albert Kagan

Arizona State University

and

Mark R. Manfredo

Arizona State University

INTRODUCTION

One of the primary objectives of purchasing is to ensure a consistent supply of high quality inputs that areessential for effective business operations at optimal cost. Since supplies constitute a major component of operatingcosts, purchasing related activities consume a substantial share of firm resources. For example, recent estimatesindicate that the average revenue share pertaining to purchasing, generally measured as the cost of goods sold,ranges from 52 % for manufacturing operations to more than 70 % for retailing (De Boer 2001; U.S. Census Bureau2006). Moreover, the impact on profitability derived from a dollar saved in the procurement phase is much higher

than a dollar generated through increased sales (Bozarth and Handfield 2006). For these reasons, businesses oftenfocus on procurement practices to control operating costs and to gain competitive advantage (Ellram 1996; Ellram etal. 2002; Fine 1998; Gimenez and Ventura 2003; Hendric 1997; Maltz and Ellram 1997; Zeng and Rosetti 2003).

Recently, the ability to ensure a consistent supply of high quality products at competitive prices has become aserious challenge for procurement managers. While some of these challenges relate to product quality andavailability, rising fuel costs have become a major obstacle given their impact on transportation costs, and ultimatelyproduct prices. This problem is particularly complicated when substitute products can be sourced from variousregional markets. It is generally understood that as transportation costs increase, procurement strategies to minimizedistance should be employed. However, this tradeoff is not obvious particularly given the often complex interactionsbetween input prices, such as fuel, and prices at and between regional markets. This problem may be even moreexacerbated if the productive capacity at one market location is superior to others, or if major infrastructure andcapacity enhancements would need to take place at a particular location if it were to truly be seen as a permanentalternative market source. Indeed, a better understanding of the dynamic interactions between input costs, such asfuel, and subsequent product prices at different marketing channels would make it easier for procurement managersto anticipate the likely impacts of price or cost shocks, and allow them to develop alternative product sourcing plansto minimize these costs and avoid supply disruptions.

Given this, the objective of this research is to develop a framework for examining the dynamic interactionsbetween product prices at different regional markets and fuel costs, through which cost-price threshold points can beestimated. These cost-price threshold points can then be used to strategically evaluate alternative product sourcingstrategies in the face of rising input costs. In doing this, a vector autoregressive (VAR) model is presented toestablish the empirical relationship between cost and price variables. VAR models are widely used in establishingthe direction of causality and the magnitude of impact from a shock to the system (market). For instance, a numberof studies have applied VAR models to examine how prices at different marketing channels react to a particular


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price or cost shock, which is commonly referred to in the VAR literature as innovations (Badinger 2006; Dopke andPierdzioch 2006; Kim and Coulson 1999; Lastrapes 2006; Pan and Jarrett 2007).

While the VAR modeling approach presented is general enough to be applied to a number of different product

sourcing situations, the specific empirical application demonstrated in this paper is of the U.S. leafy green industry namely iceberg and romaine lettuce. First, the empirical relationships between production level (shipping point)supply of iceberg and romaine lettuce, fuel (diesel) price, and lettuce prices observed at various shipping points andwholesale markets are estimated with the VAR model. Next, the estimated parameters are used along withhydroponic production cost data to derive the relationship between fuel cost and the prices (cost-price thresholds)for field grown lettuce varieties shipped from California and locally grown hydroponic lettuce varieties at threemajor wholesale marketsLos Angeles, Atlanta, and New York. Indeed, two of these markets are East Coastmarkets and involve considerable transportation expense from major production centers located in California.Currently, field grown lettuce varieties dominate these markets. However, as fuel costs continue to escalate,procuring lettuce from high cost local venders, such as hydroponic producers, is likely to be more economical thansourcing from traditional low cost field operations located in California.

This research makes important contributions on several fronts. First, the VAR modeling approach presented can

be used in virtually any situation where products can be sourced from multiple locations, provided that adequateprice and quantity data is available for the specific markets and products examined. Second, the empirical resultsfrom the leafy greens (lettuce) example should provide insight to purchasing managers who handle the lettuceprocurement as well as other perishable commodities that may be sourced from varying and/or distant locations.Finally, and probably most important, both the general applicability of the modeling framework, coupled with theinsights from the empirical results, contributes to the body of academic literature related to procurementmanagementa field of study which many scholars still consider to be less well developed compared to otherclosely related business disciplines (Das and Handfield 1997; Heijboer 2003; Morlacchi et al. 2001; Olsen andEllram 1997; Pooler et al. 2004).

The remainder of the paper is presented as follows. A brief review of literature is first presented whichhighlights applications in purchasing management, and places this study in the context of the extant literature. Next,the empirical VAR model for the leafy green (lettuce) example is developed, followed by a discussion of the lettucequantity and price data, as well as the diesel fuel price data, used in the empirical example. Empirical results are then

reported and discussed, with particular attention placed on the estimated price-cost threshold pointspoints atwhich levels of fuel costs may prompt consideration to change product sourcing options. Finally, summary andconclusions are presented, with specific considerations of the model and results for managerial uses, as well asfuture avenues for research.

LITERATURE REVIEW

While the importance of purchasing has long been recognized (Chandler 1962), the academic literature relatedspecifically to procurement management is less well developed compared to that of other closely related businessdisciplines (Das and Handfield 1997; Heijboer 2003; Morlacchi et al. 2001; Olsen and Ellram 1997). This isparticularly true since most studies in purchasing or supply chain management are either qualitative in nature, or arebased primarily on survey data that reflect respondents perceptions rather than actual business transactions (De

Boer 1998; De Boer et al. 2001; Evers 1997; Morlacchi et al. 2001; Rabinovich 2005; Sachan and Datta 2005). Ingeneral, studies in supply chain and purchasing management have lagged behind other business disciplines, such asmanagement and marketing, in terms of developing new theories, deriving testable hypothesis, applying advancedtools in analyzing empirical data, and testing hypothesis against real world observations (Dunn et al. 1994; Mentzerand Kahn 1995; Samuel 1997).

However, a number of recent developments including increasing integration of global markets, rapid growth inelectronic business activities, and a substantial increase in outsourcing are bringing purchasing management into theforefront (Zheng et al. 2007). Consequently, a wide range of analytical tools that are amenable to hypothesis testingand promote theoretical development are being used to address an increasing number of purchasing managementissues (Bolumole et al. 2007; Chin et al. 1992; De Boer 2001; Degraeve et al. 2000; Gentry et al. 1992; Heijboer2003; Sachan and Datta 2005).

224 ACHARYA, KAGAN, AND MANFREDO


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In terms of methods for selecting suppliers and making final procurement decisions, De Boer et al. (2001)provide a comprehensive literature review related to the topic, and develop a classification of the procurementprocess. They classify the procurement process into four stagesproblem formulation, formulation of criteria,qualification, and final selectionand present a list of qualitative and quantitative decision tools applicable in each

stage of the selection process. In particular, they suggest conducting a qualitative analysis using visual andbrainstorming tools in the first two stages of the vendor selection process, and recommend the use of quantitativemethods such as data mining, multi-criteria decision analysis, and optimization in the last two stages. Recent studiesshow that a wide range of quantitative tools are being applied particularly in supporting the final stages of thedecision making process (De Boer 2001; Degraeve et al. 2000; Sachan and Datta 2005).

For example, Degraeve et al. (2000) examined the relative efficiency of supplier selection decision models froma total cost of ownership perspective. This approach accounts for all costs incurred throughout the products lifecycle in calculating the total cost of ownership. Using this approach, the selection model with the least cost is themost efficient method for making the procurement decision. In particular, Degraeve et al. (2000) compared theperformance of single item and multi-item models and reported that single item methods failed to account forinterdependencies among products and performed poorly when compared to the multi-item optimization models.

In considering the specific empirical application proposed for this study, logistics management applications

have also offered a number of innovative approaches to develop optimal shipping and product distribution strategiesin managing the fresh produce supply chain (Abshire and Premeaux 1991; Bardi et al. 1989; Carter et al. 2000;Crum and Allen 1990; Dooley and Akridge 1998; Johnson 1998; Olavarrieta and Ellinger 1997; Saddle CreekCorporation 2007; Taylor 2005). For instance, to minimize the cost of delivering smaller and more frequentshipments, many firms are using a variety of supply chain management options including consolidationwarehousing, cross-docking, and multi-vendor aggregation (Perosio et al. 2001; Saddle Creek Corporation 2007;Sobeck and Frost 2005). However, the effectiveness of these alternatives depends primarily on the availability ofsupplies from multiple manufacturers/producers with common destinations. Given the inherent climatic advantageover other production regions, as well as the availability of an efficient produce distribution network, California hasbecome the major production region for many produce commodities.

However, if these market conditions are not maintained, California producers could loose the relative advantagein marketing their products particularly in distant markets. For instance, if the current trend in energy prices

continues, the cost of shipping fresh produce from California to Chicago, Atlanta, New York, and other distantmarkets may become prohibitively high and produce buyers in these markets may need to look for alternativesources of supply such as local suppliers with seasonal field operations or local operations which incorporategreenhouse or other controlled environment technology in growing produce commodities.

EMPIRICAL VAR MODEL

A clear understanding of the dynamic interactions between product prices and fuel cost is essential to evaluatingthe economic viability of procurement sourcing options. In this regard, the relationship between observed productprices and supply costs should be analyzed using a system approach because fuel costs may have a direct andindirect effect on prices. In general, a cost change (shock) or any other disruption in the product distribution systemmay affect the price of a particular product directly by increasing processing and/or shipping costs for the product,and indirectly by raising the price of other inputs used in producing it (McKinnon 2006). In the empirical application

presented (leafy greens) a rise in fuel cost would increase wholesale prices directly by increasing the shipping cost,and indirectly by increasing the price(s) for other inputs such as labor, equipment operations, as well as the costs ofother inputs. Thus, modeling the primary and secondary impacts of rising fuel costs on product prices using asystems approach is essential. A vector autoregressive (VAR) model that accounts for these direct and indirectinteractions among regional market prices and fuel costs can be used to analyze the U.S. leafy green industry.

VAR models are commonly used for forecasting economic systems and for analyzing the dynamic impacts ofrandom shocks (innovations) on a system of equations. In this modeling approach, every endogenous variable isdefined as a function of lagged values of all of the endogenous variables in the system and other importantexogenous variables, i.e.,

ttptptt BxyAyAy !++++= ##11 (1)


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whereyt is a vector of endogenous variables,xt is a vector of exogenous variables, and the is a vector of interceptsthat account for the possibility of observing nonzero means. A1, , Ap and B are matrices of coefficients to beestimated and t is a vector of error terms, which are often referred to as white noise or innovation processes(Ltkepohl 2005). The system has kequations, each containingp lags on all kvariables.

A number of steps are involved in deriving an empirical model from the pth order VAR system depicted inequation (1). First, an appropriate lag length that adequately measures the interactions among the variables ofinterest should be determined. One of the most common approaches used in determining the lag length is Akaikeinformation criteria (AIC). This approach is based on the concept of entropy and attempts to find the best fittingmodel that explains the data with a minimum number of free parameters. The test procedure involves calculating theAIC values for all relevant lag lengths and selecting the model with the lowest AIC value. In addition to the AIC,numerous other approaches such as likelihood ratio (LR) test, final prediction error (FPE) criteria, Schwarz criteria(SC), and Hannan-Quinn criterion (HQ) are also used for determining the optimal lag length.

Second, since most time series data are non-stationary, each variable included in the system should be tested forstationarity. This issue is important because models based on non-stationary variables may lead to spuriousregression (finding an evidence of a relationship when none exists). Generally, the roots of the characteristic

polynomials are analyzed to determine whether the variables included in the system are stationary. The VAR systemis considered to be stationary if all roots associated with the VAR system fall inside a unit circle (Ltkepohl 2005).If all variables included in the model are stationary the system can be estimated using observed variables in levelform (i.e., without applying any other transformation techniques required to convert non-stationary series intostationary ones).

Third, variable exogeneity tests such as Wald statistics are implemented to determine whether the variables ofinterest are truly endogenous to the system being analyzed. If not, they are either treated as exogenous variables orconsidered as insignificant factors and removed from the model. Most studies follow these three steps very closelyin deriving the empirical model from thepth order VAR system depicted in equation (1). The empirical VAR systemderived from this process can be estimated using an ordinary least squares method, without any loss of efficiency, ifthe lag operators included in the model are identical. However, if different lag lengths are involved, a moregeneralized estimation procedure is required (Hafer and Sheehan 1989; Ltkepohl 2005).

Estimating Equations

In the U.S. leafy green industry, production level supply and prices (product price and quantities measured atshipping points immediately after harvesting), shipping costs, and the prices at regional wholesale markets areclosely related. In particular, a supply shortage is likely to increase both firm level as well as the regional wholesalemarket price(s). On the other hand, an increase in the price of fuel, which is one of the primary sources of energyused in shipping perishables, is likely to increase transportation costs and subsequently wholesale prices in the shortrun. If the increase in fuel cost persists in the longer-term, it may affect both firm and wholesale level prices,however, the magnitude and direction of firm and wholesale level impacts may differ. While rising transportationcosts are likely to increase the consumer price, retail demand, mainly in distant markets where prices are prone toescalate faster than in local markets, is likely to fall. This increase in wholesale and retail prices and decrease in totalproduct demand may have a ripple effect on shipping point prices and production levels. This dynamic interactionbetween production level supply and price, transportation cost associated with fuel prices, and leafy green prices at

regional wholesale markets can be effectively modeled by using a VAR approach.

Moreover, unlike other manufacturing goods, there may not be a two-way relationship between total supply andmarket prices in the case of fresh leafy greens in the short-run. The total supply for a specific week are fixed becauseproduction decisions are made months in advance and the current production must be utilized because it cannot bestored for future use. Moreover, since the leafy green distribution system is a relatively negligible component of theU.S. freight industry, any changes in the leafy green market is not likely to have a significant impact on fuel prices.Thus, both leafy green shipments and the fuel (diesel) cost might be exogenous variables in the VAR systempresented in equation (1). However, this is an empirical issue and specific variable exogeneity tests should beconducted to determine whether these two variables are truly exogenous to the leafy green system.


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In the case of the fresh leafy green market, the endogenous variables may include total weekly shipments(production level supply), shipping point prices (production price), regional wholesale market prices (Los Angels,Atlanta, and New York), and the average U.S. fuel price. However, as described above, the VAR order, stability,and variable endogeneity are empirical issues and should be settled by conducting a number of statistical tests. Once

the empirical system is determined through these testing procedures, a trend variable and intercept terms are addedto the model to account for the effect of time trend and the possibility of a nonzero mean. Assuming that a) a secondorder VAR system adequately approximates the relationship among leafy green industry variables and the fuel costand b) both product supply and the fuel costs are exogenous, the VAR system in (1) can be expressed as productionand wholesale level price relationships:

Production Level Price Relationships for Iceberg (Ipp,t) and Romaine (Rpp,t) Lettuce

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Wholesale Level Price Relationships for Iceberg (Iwpj,t) and Romaine (Rwpj,t) Lettuce

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where j indexes regional wholesale markets (1=Los Angeles, 2=Atlanta, and 3=New York) and the endogenousvariables are defined as average production level prices for iceberg, Ipp,t, and romaine lettuces, Rpp,t; wholesaleprices for iceberg lettuce at Los Angeles, Iwp1,t, Atlanta, Iwp2,t, and New York, Iwp3,t, markets; and wholesaleprices for romaine lettuces at Los Angeles, Rwp1,t, Atlanta, Rwp2,t, and New York, Rwp3,t, markets. The

exogenous variables are the weekly supply of iceberg, Iq,t, and romaine lettuce, Rq,t; average U.S. fuel (diesel) costper gallon, Fp,t, and a linear trend variable used to account for the effect of a time trend on the leafy green pricesystem, T. The VAR system (2) contains eight equations reflecting the dynamic interactions among production andwholesale level prices, weekly supply of leafy greens (iceberg and romaine lettuce), and fuel cost. As discussedearlier, fuel cost is specified as an exogenous variable and a squared term is included in the model to test whetherthe interaction among leafy green system variables and the fuel cost are nonlinear. Although, the relationshipbetween fuel cost and lettuce prices could be linear, log linear, quadratic, or any other nonlinear form, the modelingeffort experiments with linear, log linear, and the quadratic form in order to conduct statistical tests to determine theappropriate functional form.

DATA

Data used in estimating the empirical relationship specified in the VAR system (2) were gathered from variousreports published by the Agricultural Marketing Service, USDA. In particular, two price series, weekly productionlevel prices and wholesale prices in major regional markets for both iceberg and romaine lettuce were obtained fromthe Fruit and Vegetable Market Reports and the production level supply data were gathered from various issues ofWeekly Shipment Reports (AMS/USDA). The weekly highway diesel prices were downloaded from the Bureau ofLabor Statistics website (www.bls.gov). The sample period ranges from November 20, 1999 to August 20, 2005which covers 301 weeks.

Production level prices are weekly prices for iceberg and romaine lettuce varieties observed at their respectiveshipping points. Most fresh produce commodities go through initial screening and packaging process immediatelyafter being harvested. Then, the packaged products are shipped directly to different regional wholesale markets.Therefore, the production level prices reflect production and initial packing costs. On the other hand, the wholesale


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market is the link between retail outlets and produce growers. Oftentimes farm products are shipped in bulkquantities from production fields to regional markets and repackaged before they are shipped to retail outlets. Thus,the wholesale price reflects shipping point prices, transportation charges, grading and repackaging costs, and othercosts incurred at wholesale level.

Among four major categories of leafy greens (lettuce)iceberg, romaine, red leaf, and green leafmarketed inthe U.S., iceberg has been the dominant product for many years. However, the increasing awareness amongconsumers that iceberg lettuce is relatively less nutritious than other leafy varieties has been pushing the demand forromaine and other leafy varieties up at the expense of iceberg lettuce (Cook 2001). For instance, while the per capitautilization of romaine lettuce has increased by more than 1000 percent from 0.73 lbs. per capita in 1985 to 8.1 lbs.per capita in 2004, iceberg utilization decreased by approximately 5 percent from 23.7 lbs. to 22.5 lbs per capitaduring the same time period. Due to the strong and rising demand, romaine lettuce draws relatively higherproduction level prices than iceberg lettuce (see Table 1).

TABLE 1

SAMPLE SUMMARY STATISTICS

Variable Mean Maximum Minimum Std. Dev.

Production Level Supply

Iceberg (1,000 Cwt.) 735.10 1023.00 401.00 99.00

Romaine (1,000 Cwt.) 212.62 336.00 20.00 49.18

Diesel Cost ($/Gallon) 1.60 2.59 1.14 0.30

Production Level Price

Iceberg ($/Lb.) 0.17 0.93 0.01 0.12

Romaine ($/Lb.) 0.22 0.99 0.08 0.13

Wholesale Price: Iceberg

Los Angeles ($/Lb.) 1.15 2.19 0.74 0.24

Atlanta ($/Lb.) 1.17 2.01 0.81 0.17

New York ($/Lb.) 1.45 2.84 0.92 0.29

Wholesale Price Romaine

Los Angeles ($/Lb.) 1.02 2.07 0.62 0.23

Atlanta ($/Lb.) 1.25 1.85 0.89 0.15

New York ($/Lb.) 1.19 2.62 0.52 0.25

Number of Observations 301.00

However, the gap between romaine and iceberg prices disappears at the wholesale level. Since leafy varietiessuch as romaine have a relatively shorter shelf life than the head lettuce (iceberg), retailers prefer a local supplier(when possible) than those from California. The relatively lower average price for romaine than for iceberg lettuce

in the New York market reflects the higher costs of shipping iceberg lettuce from the west coast.

EMPIRICAL RESULTS

A number of statistical tests were conducted to derive the final estimating equation system (2) from thepth orderVAR specified in (1). In particular, order, stability, and variable exogeneity were tested before selecting the finalmodel specification (Ltkepohl 2005). The likelihood ratio (LR), final prediction error (FPE) criteria, Akaikeinformation criteria (AIC), Schwarz criteria (SC), and Hannan-Quinn criterion (HQ) were used to determine theorder of the VAR (Table 2). All order tests, except for SC (which supports order one), corroborate that the empiricalleafy green system is of order 2 (VAR(2)). Next, the inverse roots of the characteristic polynomials were estimated


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and the results showed that all inverse roots lie inside the unit circle implying that the selected VAR(2) process (i.e.,the VAR equation system in 2) is stable (Figure 1).

TABLE 2

VAR LAG ORDER SELECTION RESULTS

Lag LogL LR FPE AIC SC HQ

0 1302.922 NA 3.04e-14 -8.422294 -7.826789 -8.183919

1 2068.822 1459.837 2.73e-16 -13.13304 -11.74353* -12.57683

2 2223.503 286.5223* 1.49e-16* -13.74163* -11.55811 -12.86759*

3 2263.716 72.33028 1.75e-16 -13.58199 -10.60446 -12.39011

* Indicates lag order selected by the criterion. The column headings are defined as LR: sequential modified Loglikelihood Ratio test statistic (each test at 5% level); FPE: Final prediction error; AIC: Akaike information criterion;SC: Schwarz information criterion; HQ: Hannan-Quinn information criterion.

FIGURE 1

Wald test statistics, which determine the Granger causality and block exogeneity, were also calculated toexamine whether the variables included in the model are exogenous to the overall VAR system. The estimated 2statistics for iceberg lettuce, romaine lettuce, and fuel cost, which measures the joint significance of all other laggedendogenous variables in an individual equation, are 21.18, 21.96, and 10.98, respectively. In all three cases, theseestimated values are less than the critical 2 value of 26.29 at a 5 percent level of significance with 16 degrees offreedom implying that all of these variables are exogenous to the leafy green pricing system. On the other hand, theestimated 2statistics for production level prices for iceberg (39.55) and romaine lettuce (59.23), wholesale levelprices for iceberg lettuce in Atlanta (43.77), Los Angeles (96.47), and New York (129.03), and wholesale prices for

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romaine lettuce in Atlanta (36.77), Los Angeles (128.41), and New York (99.04) are highly significant (p


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TABLE 3

MODEL RESULTS

Production LevelPrice Iceberg Wholesale Price Romaine Wholesale Price

Variable

Iceberg RomaineLos

AngelesAtlanta

NewYork

LosAngeles

AtlantaNewYork

Ipp, t-1 0.7403** -0.0589 0.2471** 0.0346 0.0363 -0.0219 -0.0688 0.1270

(11.23) -(0.76) (2.41) (0.37) (0.27) -(0.26) -(0.83) (0.99)

Ipp, t-2 -0.1760** 0.0440 0.4251** 0.3045** 0.6879** -0.0856 0.1427 0.0713

-(2.47) (0.53) (3.84) (2.98) (4.67) -(0.92) (1.59) (0.52)

Rpp, t-1 0.0606 0.7556** -0.0812 -0.0536 0.1102 0.1859** 0.0866 -0.0117

(1.12) (11.91) -(0.96) -(0.69) (0.99) (2.64) (1.27) -(0.11)

Rpp, t-2 -0.0300 -0.0828 -0.0393 -0.0805 0.0462 0.4249** 0.0280 0.4461**

-(0.52) -(1.22) -(0.44) -(0.97) (0.39) (5.62) (0.38) (3.96)

Iwp, LA, t-1 -0.0650* 0.0427 0.2929** -0.0140 0.2279** 0.0180 0.0030 0.0856

-(1.71) (0.96) (4.95) -(0.26) (2.90) (0.36) (0.06) (1.16)

Iwp, LA, t-2 -0.0206 -0.0397 0.2656** 0.0688 -0.0717 -0.0234 -0.0185 -0.0085

-(0.56) -(0.92) (4.64) (1.30) -(0.94) -(0.49) -(0.40) -(0.12)

Iwp, AT, t-1 -0.0352 -0.0581 -0.0370 0.3658** -0.0990 -0.0784 0.0513 -0.0360

-(0.88) -(1.24) -(0.59) (6.37) -(1.20) -(1.50) (1.01) -(0.46)

Iwp, AT, t-2 0.0129 0.0510 0.0091 0.2696** 0.0475 0.0740 -0.0350 -0.1207

(0.33) (1.12) (0.15) (4.85) (0.59) (1.47) -(0.71) -(1.60)

Iwp, NY, t-1 0.0576* -0.0079 0.0579 0.0052 0.3170** 0.0607 0.0375 -0.0124

(1.96) -(0.23) (1.27) (0.12) (5.23) (1.59) (1.01) -(0.22)

Iwp, NY, t-2 0.0831** -0.0157 0.0330 0.0214 0.0357 0.0106 -0.0606 0.0145

(2.89) -(0.47) (0.74) (0.52) (0.60) (0.28) -(1.67) (0.26)

Rwp, LA, t-1 0.1312** 0.1429** 0.2097** 0.0277 0.0112 0.6300** -0.1568** 0.1851*

(2.93) (2.72) (3.01) (0.43) (0.12) (10.82) -(2.78) (2.13)

Rwp, LA, t-2 -0.0988** -0.1357** -0.0583 0.0399 0.1294 0.1573** 0.0983* -0.0235

-(2.23) -(2.61) -(0.85) (0.63) (1.41) (2.73) (1.76) -(0.27)

Rwp, AT, t-1 -0.0642 -0.2502** -0.1088 0.0085 0.1032 -0.2501** 0.4702** 0.0048

-(1.36) -(4.53) -(1.49) (0.13) (1.06) -(4.08) (7.91) (0.05)

Rwp, AT, t-2 0.0500 0.3254** 0.0832 -0.0554 -0.0502 0.1305* 0.1750** 0.1026

(1.07) (5.92) (1.14) -(0.82) -(0.52) (2.14) (2.96) (1.13)

Rwp, NY, t-1 -0.0041 0.0535 -0.1014* -0.0434 0.0415 -0.0885* 0.0737* 0.3042**

-(0.13) (1.48) -(2.12) -(0.98) (0.65) -(2.21) (1.89) (5.09)

Rwp, NY, t-2 -0.0495 -0.0456 -0.0531 -0.0434 -0.0388 -0.0740* -0.0319 0.1530**

-(1.65) -(1.30) -(1.14) -(1.01) -(0.63) -(1.90) -(0.84) (2.63)

Iq,t -0.0002** 0.0000 -0.0003** 0.0000 0.0000 -0.0002** -0.0001 -0.0001

-(4.76) -(0.69) -(3.41) -(0.46) (0.09) -(3.21) -(1.56) -(0.91)

Rq,t 0.0002 -0.0002 0.0002 -0.0002 -0.0003 0.0001 0.0001 0.0002

(1.41) -(1.49) (0.92) -(0.81) -(1.13) (0.28) (0.56) (0.62)

Fp,t -0.1599 -0.0528 -0.0492 0.2375 -0.9048** -0.3083 -0.5275** -0.0881

-(1.05) -(0.30) -(0.21) (1.09) -(2.89) -(1.56) -(2.75) -(0.30)

Fp,t* Fp,t 0.0314 0.0100 -0.0188 -0.0332 0.2890** 0.1027* 0.1714** 0.0214

(0.71) (0.19) -(0.28) -(0.53) (3.19) (1.80) (3.09) (0.25)

R2 0.68 0.61 0.81 0.65 0.76 0.85 0.66 0.72

LLF 375.22 327.82 243.31 267.70 158.48 296.58 305.45 177.16

Note: t-statistics are in parenthesis. The intercept and trend variable coefficients are excluded to save space.**, * denote significance at 1% and 5% level.


10/20


FIGURE 2

Note: The impact and response variables are defined as follows: WICLA=wholesale price for iceberg lettuce in LosAngeles, PICB=production level (shipping point) price for iceberg lettuce; WICAT=wholesale iceberg lettuce pricein Atlanta; and WICNY=wholesale iceberg lettuce price in New York.

-.02

.00

.02

.04

.06

2 4 6 8 10 12 14 16 18 20

Response of WICLA to PICB

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20

Response of WICAT to PICB

-.04

-.02

.00

.02

.04

.06

.08

.10

2 4 6 8 10 12 14 16 18 20

Response of WICNY to PICB

Response to Cholesky One S.D. Innovations 2 S.E.


11/20


FIGURE 3

Note: The impact and response variables are defined as follows: WROLA=wholesale price for romaine lettuce inLos Angeles, PROM=production level (shipping point) price for romaine lettuce; WROAT=wholesale romaine pricein Atlanta; and WRONY=wholesale romaine price in New York.


-.02

.00

.02

.04

.06

.08

2 4 6 8 10 12 14 16 18 20

Response of WR OLA to PROM

-.010

-.005

.000

.005

.010

.015

.020

.025

2 4 6 8 10 12 14 16 18 20

Response of WR OAT to PROM

-.02

.00

.02

.04

.06

.08

2 4 6 8 10 12 14 16 18 20

Response of WRONY to PROM


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FIGURE 4

Note: The impact and response variables are defined as follows: WICLA=wholesale price for iceberg lettuce in LosAngeles, WROLA=wholesale price for romaine in Los Angeles, WROAT=wholesale romaine price in Atlanta,WRONY=wholesale romaine price in New York; WICAT=wholesale iceberg price in Atlanta; WICNY=wholesaleiceberg price in New York; and PICB=production level (shipping point) price for iceberg lettuce.


-.03

-.02

-.01

.00

.01

.02

2 4 6 8 10 12 14 16 18 20

Response of WROAT to WROLA

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20

Response of WRONY to WROLA

-.005

.000

.005

.010

.015

.020

.025

.030

2 4 6 8 10 12 14 16 18 20

Response of WICLA to WROLA

-.010

-.005

.000

.005

.010

.015

.020

2 4 6 8 10 12 14 16 18 20

Response of WICAT to WROLA

-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20

Response of WICNY to WROLA

-.010

-.005

.000

.005

.010

.015

.020

2 4 6 8 10 12 14 16 18 20

Response of PICB to WROLA


13/20


Significantly negative coefficients of iceberg shipments in the production level and wholesale price equationsre-enforce the negative price quantity relationship as postulated by demand theory. Although the coefficient is notsignificant, a similar relationship is observed between romaine supply (shipments) and the shipping point price. Onthe other hand, a significantly negative relationship between the weekly shipments of iceberg lettuce and the

wholesale price of romaine in the Los Angeles regional market shows that iceberg and romaine lettuce might besubstitutes in this market. Moreover, none of the iceberg wholesale price coefficients is significant in the romainewholesale price equations but two of the romaine wholesale price coefficients contained within the icebergwholesale price equations are significant (p


14/20


Although U.S. fuel prices are less likely to reach the level currently observed in many European countries, it hasbeen rising much faster than in the past. For instance, the average weekly fuel cost has increased by more than 360percent from $0.956 on March 1, 1999 to $3.44 per gallon on February 4, 2008 (Energy Information Administration2008). Since this rising trend may continue in the future, an objective analysis of the likely impact of rising fuel coston regional market prices would help procurement managers to develop an effective sourcing plan. One of thepossible alternatives to address the rising transportation cost would be to shift production closer to major markets.However, in many regional markets such as New York, producing leafy greens particularly during the wintermonths is not feasible without developing weather controlled production facilities.

Although the cost of producing certain leafy green varieties under controlled environment conditions is muchhigher than field production, a substantial increase in transportation cost may make greenhouse production moreeconomical particularly in distant markets such as New York. Recent estimates indicate that the average cost ofproducing lettuce in a greenhouse environment is $0.72/head (Donnell et al. 2005; OARDC/Ohio State University2006). Moreover, the greenhouse lettuce production cycle (35 days) is much shorter than the field production cycle

(90-120 days), which may provide more flexibility in matching supply with the changing market demand. Onaverage, a harvest ready hydroponically produced lettuce head weighs 5 ounces (CEA/Cornell University).Although, there are various controlled environment production systems available and the average cost and weight ofan individual lettuce head may vary, these two figures can be applied in developing a tentative production costestimate.

Applying these two cost and weight measures, the average cost of producing one pound of lettuce undercontrolled environment would be $2.34/lb. Assuming that all field grown leafy green products are produced inCalifornia and transported to markets throughout the U.S. and field grown and hydroponic lettuce are perfectsubstitutes, a fuel cost of $3.41/gallon would make hydroponic lettuce more attractive than field-grown lettuce inNew York wholesale markets. Likewise, hydroponics would be more economical than field grown in Atlanta andLos Angeles as the fuel cost rises beyond $4.00 and $5.00 per gallon respectively.

FIGURE 5

FUEL COST AND LEAFY GREEN PRICE THRESHOLDS: CONSTANT HYDROPONIC

PRODUCTION COSTS

0.0

2.0

4.0

6.0

8.0

10.0

12.0

1.0 2.0 3.0 4.0 5.0 6.0 7.0

Fuel Cost ($/Gallon)

Icebergand

RomainePrices($/Lb)

Iceberg Wholesale Price in New York

Romaine Wholesale Price in Los Angeles

Romaine Wholesale Price in Atlanta

Cost of Hydroponic Lettuce

3.41 3.98 5.02


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Simulated results under the second scenario are reported in Figure 6. This scenario is exactly the same as thefirst one except for the variation in the cost of producing hydroponic lettuce as fuel costs rise. This change in themodel scenario only alters the cost-price thresholds beyond which hydroponic lettuce becomes a more economicaloption. In particular, the switching point changes from $3.41, $3.98, and $5.02/gallon to $3.77, $4.63, and

$6.18/gallon for New York, Atlanta, and Los Angeles, respectively.

Empirical relationships between fuel cost and leafy green prices displayed in Figures 5 and 6 are based on anumber of simplifying assumptions and should be used with caution. First, the study is based on the assumption thatthere is adequate greenhouse production capacity to increase the supply and satisfy the rising demand forhydroponic lettuce as fuel cost continues to escalate. Although demand and supply forces are expected to maintain abalance in the long run, factors such as periodic fluctuations in fuel prices, inadequate supply of production capacity,and other market imperfections are likely to increase volatility in the short run making it difficult to sustain a smoothtransition from field grown to hydroponic lettuce as depicted in the graphs (Figures 5 and 6). Future studies shouldaddress these issues for a better understanding of this adjustment process and to avoid negative impacts of thesemarket imperfections. Second, a rise in the diesel price will also increase the cost of all fuels and other factor inputssubsequently increasing the cost of hydroponics. Third, an improvement in greenhouse production technology mayreduce the production costs, making hydroponics a more attractive option much earlier than depicted in Figure 6 forleafy green production as opposed to growing lettuce in the traditional field setting.

FIGURE 6

FUEL COST AND LEAFY GREEN PRICE THRESHOLDS: VARIABLE HYDROPONIC

PRODUCTION COSTS

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

2.0 3.0 4.0 5.0 6.0 7.0

Lettuce

Price

($/Lb)

New York Iceberg Price

Atlanta Romaine Price

Los Angeles Romaine Price


4.63 6.183.77


0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

2.0 3.0 4.0 5.0 6.0 7.0


LettucePrice($/Lb)





4.63 6.183.77

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

2.0 3.0 4.0 5.0 6.0 7.0


Lettuce

Price

($/Lb)





4.63 6.183.77


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SUMMARY AND CONCLUSIONS

Purchasing managers ability to obtain and ensure a consistent supply of quality products at competitive pricesremains, and likely will continue to be a critical management challenge. This is particularly true in the presence of

rising fuel costs, given the direct and indirect impact that fuel costs have on transportation costs and product pricesrespectively. When substitute products can be sourced from alternative market locations, procurement managersmust decide on which location to source from. In the presence of rising fuel costs and subsequent price differentials,the choice of which market to source products from is not immediately obvious. Therefore, this research attempts toprovide a framework for better understanding the interactions between input costs, namely fuel price, and productprices among alternative markets. Specifically, this study presents a vector autoregression (VAR) model to establishthe empirical relationship between cost and price variables, and establish cost-price threshold points which can beused to evaluate product sourcing from alternative markets in the presence of rising fuel costs. This processdemonstrate the use of a VAR model for the leafy greens market (lettuce), an important perishable producecommodity, which is primarily grown domestically in California and Arizona, yet shipped to various pointsthroughout the U.S.

In addition to illustrating the VAR modeling approach, the empirical results provide practical guidance to

procurement managers that work with perishable commodities such as lettuce. For instance, if fuel prices continue toescalate beyond $3.77/gallon, produce managers in New York and Atlanta wholesale markets, who currentlyprocure leafy greens and other perishable products primarily from California, may need to look for alternativesources such as local suppliers with seasonal field production or those with greenhouse operations to meet consumerdemand and maintain a smooth transition from field grown to hydroponics based products. In this context, the fuelcost thresholds estimated in this study provide a reference point for fresh lettuce buyers in identifying non-traditional suppliers as well as in developing alternative sourcing plans.

The VAR modeling framework presented here is flexible enough to be used in estimating and examining thecost-price interactions for products beyond that of leafy greens, provided that an adequate series of product price andquantity data is available. Since most produce commodities are seasonal, produce managers need to evaluatenumerous supply sources and make optimal procurement decisions for each season on a regular basis. For instance,fresh market oranges are procured from global market sources including Australia, South Africa, DominicanRepublic, and Mexico, particularly during the summer months (June-September) when domestic supply is limited.

However, as fuel costs continue to escalate, the cost of procuring oranges from distant sources such as Australia andSouth Africa may become relatively more expensive than sourcing from the Dominican Republic or Mexico. AVAR model, similar to the one developed in this study, can be used to derive the cost price thresholds that mayprovide an empirical basis for procuring oranges from the global market.

While the VAR modeling framework used in this paper can serve as a guide for other applications, the exactspecification of the VAR model will likely be different than the one presented here given the idiosyncrasies of thespecific markets and data examined. Nonetheless, this research helps to fill a perceived void in the purchasingmanagement literature by adopting and applying theory and methods from other disciplines, analyzing supply chainmanagement issues from a systems perspective, and in using innovative research tools to analyze secondary data(Sachan and Datta 2005).

NOTES

Abshire, Roger D. and Shane R. Premeaux (1991), Motor Carrier Selection Criteria: Perceptual Differencesbetween Shippers and Carriers, Transportation Journal, Vol. 31, No. 3, pp. 31-35.

AMS/USDA (2006), Fruit and Vegetable Market News Reports, www.ams.usda.gov/fv/mktnews.html, accessedNovember 10, 2007.

Badinger, Harald (2006), Fiscal Shocks, Output Dynamics and Macroeconomic Stability: An EmpiricalAssessment for Austria (1983-2002),Empirica, Vol. 33, No.5, pp.267-284.


17/20


Bardi, Edward J., Prabir K. Bagchi, and T.S. Raghunathan (1989), Motor Carrier Selection in a DeregulatedEnvironment, Transportation Journal, Vol. 29, No. 1, pp. 4-11.

Bolumole, Yemisi. A., Robert Frankel and Dag Naslund (2007), Developing a Theoretical Framework for Logistics

Outsourcing. Transportation Journal, Vol. 46, No. 2, pp. 35-54.

Bozarth, Cecil C. and Robert B. Handfield (2006), Introduction to Operations and Supply Chain Management,Englewood Cliffs, NJ: Pearson/Prentice Hall.

Carter, Craig R., Rahul Kale, and Curtis M. Grimm (2000), Environmental Purchasing and Firm Performance: Anempirical Investigation, Transportation Research Part E: Logistics and Transportation Review, Vol. 36, No. 3, pp.219-228.

CEA/Cornell University (2007), Controlled Environment Agriculture,http://aben.cals.cornell.edu/extension/CEA/indexv4.htm, accessed November 15, 2007.

Chandler, Alfred D. (1962), Strategy and Structure, Cambridge, MA: MIT Press.

Chin, Louis and Thomas E. Vollmann (1992), Decision Support Models for Natural Gas Dispatch, TransportationJournal, Vol. 32, No. 2, pp. 38-45.

Cook, R. L. (2001), The U.S. Fresh Produce Industry: An Industry in Transition. in Postharvest Technology ofHorticultural Crops , Adel A. Kader (ed.), University of California Division of Agriculture and Natural Resources.

Crum, Michael R. and Benjamin J. Allen (1990), Shipper EDI, Carrier Reduction, and Contracting Strategies:Impact on the Motor Carrier Industry, Transportation Journal, Vol. 29, No. 4, pp. 8-32.

Das, Ajay and Robert B. Handfield (1997), Just-in-Time and Logistics in Global Sourcing: An Empirical Study,International Journal of Physical Distribution and Logistics Management, Vol. 27, No. 3/4, pp. 244-259.

De Boer, Luitzen (1998), Operations Research in Support of Purchasing; Design of a Toolbox for Supplier

Selection, Ph.D. thesis, University of Twente, Enschede, The Netherlands.

De Boer, Luitzen, E. Labro, and Pierangela Morlacchi (2001), A Review of Methods Supporting SupplierSelection,European Journal of Purchasing & Supply Management, Vol. 7, No. 2, pp. 75-89.

Degraeve, Zeger, Eva Labro, and Filip Roodhooft (2000), An Evaluation of Vendor Selection Models from a Totalcost of Ownership Perspective, European Journal of Operational Research: Theory and Methodology, Vol. 125,pp. 34-58.

Donnell, Mary, Ted Short, Robert Moore, and Chad Draper (2005), Hydroponic Greenhouse Lettuce EnterpriseBudget Based on Quonset Style House, Columbus, OH: The Ohio State University Extension.

Dooley, Frank J. and Jay T. Akridge (1998), Supply Chain Management: A Case Study of Issues for BioAg,

International Food and Agribusiness Management Review, Vol. 1, No. 3, pp. 435-441.

Dopke, Jorg and Christian Pierdzioch (2006), Politics and the Stock Market: Evidence from Germany, EuropeanJournal of Political Economy, Vol. 22, No. 4, pp. 925-943.

Dunn, Steven C., Robert F. Seaker, and Mathew A. Waller (1994), Latent Variables in Business LogisticsResearch: Scale Development and Validation,Journal of Business Logistics , Vol. 15, No. 2, pp. 145-172.

Ellram, Lisa M. (1996), A Structured Method for Applying Purchasing Cost Management Tools, InternationalJournal of Purchasing and Materials Management, Vol. 32, No. 1, pp. 20-28.


18/20


19/20


OARDC/Ohio State University (2006), Hydroponic Greenhouse Lettuce Enterprise Budget, www.oardc.ohio-state.edu/hydroponics/Economics/economics.htm, accessed December 15, 2007.

Olavarrieta, Sergio and Alexander E. Ellinger (1997), Resource-Based Theory and Strategic Logistics Research,

International Journal of Physical Distribution and Logistics Management, Vol. 27, No. 9/10, pp. 559-587.

Olsen, Rasmus F. and Lisa M. Ellram (1997), Buyer-Supplier Relationships: Alternative Research Approaches,European Journal of Purchasing & Supply Management, Vol. 3, No. 4, pp. 221-231.

Pan, Xia and Jeffrey Jarrett (2007), Using Vector Autoregressive Residuals to Monitor Multivariate Processes inthe Presence of Serial Correlation,International Journal of Production Economics, Vol. 106, No. 1, pp. 204-216.

Perosio, Debra J., Edward McLaughlin, Sandra Cuellar, and Kristen Park (2001), Fresh Track 2001: Supply ChainManagement in the Produce Industry, R. B. 2001-05, Ithaca, NY: Cornell University.

Pooler, Victor H., David J. Pooler, and Samuel D. Farney (2004), Global Purchasing and Supply Management:Fulfill the Vision, Boston, MA: Kluwer Academic.

Rabinovich, Elliot (2005), Consumer Direct Fulfillment Performance in Internet Retailing: EmergencyTransshipments and Demand Dispersion, Journal of Business Logistics, Vol. 26, No. 1, pp. 79-112.

Sachan, Amit and Subhash Datta (2005), Review of Supply Chain Management and Logistics Research,International Journal of Physical Distribution and Logistics Management, Vol. 35, No. 9, pp. 664-705.

Saddle Creek Corporation (2007), 2007 Food Logistics Industry Report, White Paper Series, Lakeland, FL: SaddleCreek Corporation.

Samuel, D. (1997), Research Methods Explored: An International-based Approach, Proceedings of the LogisticsResearch Network Conference, Huddersfield, UK.

Sobeck, T. and D. Frost. (2005), Chill Challenge: Capacity Shortages and Smaller Shipment Sizes are Creating a

Meltdown in the Cold Chain, According to an Exclusive Food Logistics Magazine Survey, Frozen Food Age, Vol.53 (July), p. 29.

Taylor, David H. (2005), Value Chain Analysis: An Approach to Supply Chain Improvement in Agri-FoodChains,International Journal of Physical Distribution and Logistics Management, Vol. 35, No. 10, pp. 744-762.

U.S. Census Bureau (2006), Statistics for Industry Groups and Industries: 2005: Annual Survey of Manufacturers,M05(AS)-1 November 2006, Economics and Statistics Administration, U.S. Department of Commerce.

Zeng, Amy Z. and Christian Rosetti (2003), Developing a Framework for Evaluating the Logistics Costs in GlobalSourcing Processes: An Implementation and Insights,International Journal of Physical Distribution and LogisticsManagement, Vol. 33, No. 9, pp. 785-803.

Zheng, Jurong, Louise Knight, Christine Harland, Stuart Humby, and Ken James (2007), An Analysis of Researchinto the Future of Purchasing and Supply Management, Journal of Purchasing and Supply Management, Vol. 13,No. 1, pp. 69-83.


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ABOUT THE AUTHORS

Ram N. Acharya (Ph.D. Auburn University) is an Assistant Professor of Agribusiness Management at NewMexico State University. His current research interests include issues related to product marketing, ecommerce, andsupply chain management. Dr. Acharyas publications have appeared in numerous journals including theJournal of Economics and Business, e-Service Journal, Applied Economics Letter, American Journal of AgriculturalEconomics, Journal of Internet Commerce, Applied Economics, Communications in Statistics: Simulation andComputation, andEducational and Psychological Measurements.

Albert Kagan (Ph.D. Iowa State University) is a Professor of Management at Arizona State University. Hisresearch activities are in the area of entrepreneurial design, and business processes of procurement. Representativepublications have appeared in: Entrepreneurship: Theory and Practice, Journal of MIS, Journal of Marketing Research, Applied Economics, Journal of Economics and Business among others. Professor Kagans fundedresearch projects have totaled over $11.0 million. He has edited one book and is on the editorial board of a businessinnovation journal.

Mark Manfredo (Ph.D. University of Illinois at Urbana-Champaign) is an Associate Professor in the Morrison

School of Management and Agribusiness at Arizona State University. His research interests include commodityprice analysis, forecasting, forecast evaluation, futures and options markets, and financial risk management. He haspublished in the Journal of Supply Chain Management, Energy Economics, American Journal of AgriculturalEconomics,Applied Financial Economics, andJournal of Public Policy and Marketing.

Contact author: Ram N. Acharya, E-mail: [email protected]

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Impact of Rising Fuel Cost on Perishable Product Procurement

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