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Abstract Number: 011-0055

Title: Optimal Supply Chain Strategy: A Process Industry Perspective.

Authors:

Albert Munoz, Centre for Business Services Science University of Wollongong Wollongong, New South Wales, Australia 2500 Email: am629@uow.edu.au Ph; +61 4 3727 4639 Dr. Tim Coltman, Associate Professor School of Information Systems & Technology University of Wollongong Wollongong, New South Wales, Australia 2500 E-mail; tcoltman@uow.edu.au Tel: +61 2 4221-3912 Fax: +61 2 4221-4055 Mb: 0417 275 459 Prof. Trevor Spedding School of Management and Marketing University of Wollongong Wollongong, New South Wales, Australia 2500 Telephone: +61 2 4221 4125 Fax: +61 2 4221 4289 E-mail: spedding@uow.edu.au

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POMS 20th Annual Conference Orlando, Florida U.S.A. May 1 to May 4, 2009

Optimal Supply Chain Strategy: A Process Industry Perspective

Keywords: supply chain management, supply chain strategy, lean, agile, continuous replenishment

Abstract

In recent years considerable attention has been given to the importance of generic supply

chain strategies that can take a lean, agile or continuous replenishment orientation. However,

little theoretical and empirical work has been directed towards the relevance of these

strategies in the complex settings that are frequently found in process industries. This paper

addresses these concerns by developing a performance measurement instrument that captures

the way managers orchestrate supply chain strategy in a process industry. We then draw on a

case study illustrate the trade-off that takes place between three factors: (1) inventory levels,

(2) customer satisfaction, and (3) throughput. The interaction between these three

characteristics is critical to supply chain performance and implies that singularly focused

supply chain strategies are far less common than normally assumed.

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1. Introduction

It takes little more than a browsing of the management section of the local bookstore

blazoned with titles such as Lean Thinking (Womack and Jones 1996), Lean Solutions

(Womack and Jones), Creating the Agile Supply Chain (Harrison, Christopher et al. 1999),

and The Toyota Way (Liker 2004) to recognize the importance that publishers and managers

place on operations management philosophies based on lean, agile and continuous

replenishment strategies. In the manufacturing strategy arena, a vast literature exists that

provides the conceptual grounding for each strategic orientation. For example, lean

manufacturing is based on identifying and eliminating waste through a product’s value chain

(Womack, Jones et al. 1990). Agile manufacturing is based on lead time reduction and has

been shown to be effective whenever product life cycles are short and market demand is

unpredictable (Richards 1996; Towill and McCullen 1999). These two important scholarly

contributions have provided the basis for manufacturing strategy over the past two decades

(Yusuf and Adeleye 2002).

More recent scholarly attention has been directed towards a hybrid approach based on the

combination of lean and agile strategies. The “leagile” concept (Naylor, Naim et al. 1999)

has become a popular way to more effectively adapt to changes in the business environment

and to address market and customer needs in a more proactive manner while maintaining

high levels of operational efficiency (Lee 2004). However, despite the popularity of this

hybrid approach, there remains little agreement on what combination of lean and agile

strategy is most optimal. This situation has left many practitioners confused and hesitant to

embrace leagile strategies (Holweg, Disney et al. 2005).

A particular area that has yet to benefit from robust scholarly investigation of different

strategic orientations is the process industry. Process industries provide several unique

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challenges to supply chain strategy due to the strict processing conditions and high capital

investments that are typically required (Wallace 1984; Fransoo and Rutten 1994). What

distinguishes process industries from other industries is that they require the use of numerous

continuous or batch process steps to add value by mixing, separating or forming materials.

Examples of process industry organizations include petrochemicals processing and steel

manufacturing. Process industries often focus on mass production to ensure high levels of

utilization are achieved from expensive capital intensive equipment (Hill 1994). The firms

that operate in process industries have yet to report significant benefits from the many

strategic supply chain management developments seen in discrete manufacturing industries

(Dennis and Meredith 2000; Groover 2007). One reason is that although textbook

manufacturing strategies that have proven to be effective in discrete manufacturing contexts,

are particularly difficult to implement in complex process industry settings (Abdulmalek and

Rajgopal 2006).

Therefore, it is unclear:

1. What strategy or combination of strategies is most optimal, given the unique

characteristics of the process industry? and

2. Whether these strategies, developed primarily for discrete manufacturing, are

appropriate for process industries?

These two questions motivate the research presented in this paper.

A review of the literature reveals that very little scholarly attention has been directed towards

process industry supply chains. This is problematic because process industries are

particularly complex and require a multi-faceted approach to strategy. In this paper, we

develop an instrument to aid supply chain performance measurement that is specifically

aimed at process industries..

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There are a number of common characteristics in process industries that set them aside from

most discrete industries. First, the redesign of material flow will typically incur significant

capital expenditures that are not easily justified. Second, there are usually numerous complex

processing steps which include the transition between continuous and batch flow processes.

Continuous flow in manufacturing is characteristically inflexible with known, stationary

bottlenecks and low information requirements (Hayes and Wheelwright 1984; Hill 1989;

Voss 2007). Finally, all supply chains that rely on production machinery are subject to

uncertainties in operations, such as unplanned machine breakdowns. All of which affect

overall performance.

These characteristics, prevalent in process industries, must be considered in choosing a

supply chain strategy (Beckett, Wainwright et al. 2000; Christopher 2000). But it is naïve to

propose that process industry managers make one dimensional strategic choices between

lean, agile or continuous replenishment orientations. Instead, the choice of strategic

orientation is multidimensional and based on prudent trade-offs between elements of all three

strategies (Gibson and Birkinshaw 2004; Lee 2004; Tushman and O'Reilly Iii 2006). This

choice has recently been supported by testimony from case study practitioners that reveal a

balanced approach is required to optimize efficiency in operations whilst maintaining the

flexibility required to quickly respond to changes in markets (Lee 2004). Even though these

models have been developed and are readily available, the behaviour of experienced

managers remains ad-hoc (Childerhouse and Towill 2000; Geary, Disney et al. 2006). A key

reason for this is that managers are constantly required to trade-off the advantages and

disadvantages of high production costs, against inventory costs and other costs associated

with providing high levels of customer service (Tomlin and Snyder 2006).

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The supply chain strategy trade-off is a key focus of this paper. The remaining sections of

this paper are structured as follows. Section 2 provides a brief literature review on supply

chain management and its more popular strategies in a process industries context. Section 3

presents an overview of the performance measurement instrument and its relationship to

certain strategies. Section 4 provides a case study of a large steel manufacturing firm which

demonstrates the applicability of the measurement instrument. Section 5 is a discussion of the

managerial implications of the use of the instrument and Section 6 discusses the areas of

future research to be explored.

2. Literature Review

Supply chain management theories and perspectives have evolved from a traditional focus on

the movement of materials and goods, to a more strategic emphasis based on opportunities to

provide greater customer value through integration of the supply chain and supply chain

partners (Christopher and Towill 2002; Ketchen and Hult 2007). One common theme to

emerge is that supply chain management recognizes the classic conflict of interest between

achieving high levels of customer service while at the same time, maintaining high levels of

equipment utilisation and low inventories (Towill 1994). Three of the more common

strategic orientations mentioned in the literature are lean, agile and continuous replenishment.

The relevant literature on each is as follows.

a. Lean Manufacturing

Lean manufacturing is focused on ‘doing more with less’ by reducing and eliminating waste

or ‘muda’ throughout the production process. The concept of leanness entails “developing a

value stream to eliminate all waste, including time, and to ensure a level schedule” (Page 108,

Naylor, Naim et al. 1999). The most popular example of lean manufacturing is the Toyota

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Production System, as described by Womack and his colleagues (1990) in their book titled

‘The Machine that Changed the World’. The Toyota production system attributes supply

chain success to their ability to achieve economies of scale in manufacturing and

procurement based on small batch size production units (Holweg 2007). The reduction of

inventory, considered a major form of waste in supply chains, is one of the commonly cited

benefits that arise from the implementation of lean practices (Christopher, Peck et al. 2006).

A critical assumption that underpins lean manufacturing is that demand needs to be

predictable, variety of production should be low and consistent lead times. This is necessary

to ensure that waste is eliminated (Naylor, Naim et al. 1999; Mason-Jones, Naylor et al.

2000). Measurements that track accuracy and predictability are emphasized for lean supply

chains. Such metrics include logistics cost per unit, forecast accuracy and variances, the

utilisation level of storage facilities and so on (Gattorna 2006). From an organisational

standpoint, lean supply chains mainly consider cash-to-cash cycle times are a critical measure

of performance. The longer it takes to convert inventories into cash, the more working

capital is required, and any reduction in this measure will mean the release of working capital

and hence a reduction in cost (Christopher and Gattorna 2005; SCC 2008).

Lean supply chains are found in the automotive industry, where competition among firms has

forced supply chain integration and the implementation of lean manufacturing and just in

time practices to serve customers (Womack, Jones et al. 1990). The exemplar example is the

Toyota Production System that evolved into a producer of higher differentiated automobiles

at comparatively low volumes while retaining competitive prices. However, deeper analysis

has revealed that although car assembly operations are more efficient,the upstream and

downstream partners in the supply chain appear to be holding excess inventory. In other

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words, the Toyota company has simply based on the costs and reduced the efficiency of these

partners (Howleg 1998).

The implementation of lean in a process industry setting is considered to be particularly

difficult even though certain aspects of lean have been argued to be applicable. For example,

Billesbach (1994), Cook and Rogowski (1996), Ahmad et. al. (2005), and Melton (2005)

have examined aspects of continuous production that are amenable to lean techniques and

present a classification scheme for the implementation of lean in process industries

(Abdulmalek and Rajgopal 2006). It should be noted that these studies were selective in the

locations where they applied lean principles, mainly in the batch or non-continuous sections

of the production process. Only partial adoption of lean manufacturing in process industry

supply chains has been successful

b. Agile Manufacturing

Agile manufacturing uses “market knowledge and a virtual corporation to exploit profitable

opportunities in a volatile marketplace” (Naylor, Naim et al. 1999). In situations of

unpredictable demand and long lead times for products with short life cycles it is

recommended that firms postpone the assembly/configuration/distribution of finished goods

by carrying inventory in some generic form until the demand is encountered (Christopher,

Peck et al. 2006). The most common examples are in the fashion industry, where firms such

as Zara, a large Spanish apparel manufacturer and distributor, attribute their competitive

advantage to short product life cycles and quick response to changes in market demand

(Gattorna 2006). The design of Zara’s product delivery system is flexible enough to cope

with sudden changes in demand, opting to produce less than expected sales whilst retaining

significant levels of raw materials and slack in equipment utilisation. Zara views

undersupply as a lesser evil than holding slow-moving or obsolete stock. In some industries,

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this supply chain behaviour can be very profitable, as significant per unit profits far outweigh

the inventory holding costs and costs associated with equipment idle time. In volatile

markets, the ability to meet customer demands at short delivery times and synchronise

operations to meet the peaks and troughs of demand is essential; this high level of

manoeuvrability is commonly termed agility (Christopher 2000).

The key point to agility is that in order to cope with volatile market demand the business

must be responsive with high levels of quality and short product lead times (Mason-Jones,

Naylor et al. 2000). Both agility and leanness require high levels of product quality, a

minimisation of total lead time in alternative ways. Lead time in agility needs to be

minimised to enable fast reaction time to changes in market demand, while in lean

manufacturing lead time must be minimised to reduce excess time and waste (Towill and

McCullen 1999; Mason-Jones, Naylor et al. 2000).

The notion of hybridisation of strategies has also been well explored in literature. Leagility is

the combination of the lean and agile paradigm within a total supply chain strategy by

positioning the decoupling point so as to best suit the simultaneous need to respond to

volatile downstream demand and upstream scheduling requirements (Naylor, Naim et al.

1999; Mason-Jones, Naylor et al. 2000; Christopher, Peck et al. 2006).

Scholarly work has investigated the application of agile manufacturing principles in process

industries. However, one form of applying agile philosophies to process industries is to

refocus thinking from large scale single stream continuous and batch plants to smaller

customised plants located closer to the customers (Ahmad and Benson 1999). Other attempts

to increase the responsiveness in process industries include the use e-business to streamline

business processes, provide windows into operations, integrate the supply chain and plant

systems already in place, increase customer services and streamline distribution (Rao 2002;

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Gunasekaran, Lai et al. 2008). Applications of agile principles based on theoretical concepts

such as delayed differentiation have not been reported in the literature (Caux, David et al.

2006). In common with those applied studies of lean strategy the implementation of agile

strategy has been limited to the discrete portions of the process industry. Agile

manufacturing is for the most part infeasible in process industry, as this industry is

characterised with highly capital intensive machinery, low variability in demand and high

inventory holding costs coupled with the uncertainties in process equipment capacity and

throughput.

c. Continuous Replenishment

Continuous replenishment planning is “the practice of partnering between distribution

channel members that changes the traditional process from distributor-generated purchase

orders based on economic order quantities to the replenishment by the vendor of product

based on actual and forecast data” (ECR 1993). A key principle of this strategy is to create

order forecasts based on shared demand and forecasted production capacity. This is aimed at

reducing inventory costs, to optimise asset utilisation and take advantage of transportation

economies of scale (Rizk, Martel et al. 2006).

A popular example of this strategy is the Campbell soup company, a multinational food

manufacturer that pioneered the use of continuous replenishment strategies in the early

1990s. Electronic Data Interchange (EDI) links with retailers are used by Campbell’s to

monitor stock levels, forecast future demand and determine which products require

replenishment based on upper and lower inventory limits (Fisher 1997). In the event that

product inventory is below a predetermined inventory level, a shipment is made to bring the

product inventory to at least a minimum level. Similarly, programmes such as Vendor

Managed Inventory (VMI) utilise similar schemes, except in VMI the vendor in charge of

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what and when to ship (Andel 1996). This results in higher volumes of throughput for

upstream supply chain partners (Clark and McKenney 1994; Cachon and Fisher 1997).

The need to make efficiency driven decisions under a continuous replenishment programme

(for example, production scheduling) is given considerable importance (Smith-Daniels and

Ritzman 1988). This creates a dangerous environment whereby management constantly seeks

to reach or maintain high levels of asset utilisation, exposing organisations to the perils of

dynamic markets. For example, two studies conducted by Lee and his colleagues (2004) in

the 1990s found that although stock was often readily available in factory stockyards, the

shipment was not sent to the customer until shipping containers were full, a prominent

practice in continuous replenishment strategies. This practice often delayed product delivery,

while retail outlets lost sales due to stockouts. Lee (2004) concluded that efficient supply

chains often become uncompetitive due to their inability to adapt to changes in market

structures. Reasons for these shortcomings are best summarised by Lee (2004);

“Many companies have centralised manufacturing and distribution facilities to generate

scale economies, and they deliver only container loads of products to customers to

minimise transportation time, freight costs, and the number of deliveries. When demand

for a particular brand, pack size, or assortment rises without warning, these organisations

are unable to react even if they have the items in stock.”

d. Summary

In summary, the literature is characterized by three types of strategy: (1) lean, (2) agile and

(3) continuous replenishment. These strategies have not been proven to meet the unique

challenges faced in process industries. As evidenced by the lack of research in the area,

although several authors have contributed by examining aspects of continuous production

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that are amenable to lean implementation in process industries [see Melton (2005) and

Abdulmalek and Rajgopal (2006) for more information]. Research where implementation of

the strategic principles described in this paper are few, Table 1 summarizes the small number

of studies that have examined lean, agile or continuous replenishment principles in process

industry supply chains.

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Table 1 Examples of Process Industry studies for Supply Chain Strategies

Author Method Data and Sample Key Variables Contribution

Lean/Agile/ Continuous

Replenishment (Ahmad, Dhafr et al. 2005)

Single Case Study

Chemicals Manufacturing

Theoretical production rates Model for determining theoretical production targets

Lean

(Billesbach 1994)

Single Case Study

Textile Production Plant

Level of Work in Progress (WIP)

Reduction in WIP by 96% while maintaining constant volume throughput.

Lean

(Cook and Rogowski 1996)

Single Case Study

Chemicals Transportation Value Stream

Forecast accuracy, distribution lead time, lead time variability.

Increase of 25% in forecast accuracy, 25% reduction in distribution lead time, and 50% reduction in lead time variability.

Lean

(White 1993) Survey 1165 surveys from manufacturing firms

Production cycle time More than 78% of respondents indicated that JIT implementation was beneficial to their organisation

Lean

(Caux, David et al. 2006)

Single Case Study

Batch Process Industry

Number of slab types Delayed differentiation was found to reduce the number of required slab types

Agile

(Shaw, Burgess et al. 2005)

Action Research

Various Case Studies of Specialty Chemicals Industries

Set up times, process choice, plant reconfigurability

Corporate culture influences a firm’s ability to adopt new technologies such as process choice or set-up time reduction. Cost barriers to reconfigurability are smaller than previously suspected.

Agile

(Rizk, Martel et al. 2006)

Single Case Study

Large pulp and paper company

Transportation costs, changeover costs

In using integrated flow planning problems, the choice of problem formulation will have significant impacts on solving time.

Continuous Replenishment

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All strategies described in this paper (lean, agile, and continuous replenishment) have a

common goal, that is, to select the most optimal strategy to improve supply chain

performance. As per strategy selection, in addition to simply implementing a single supply

chain strategy from end to end (Womack and Jones 2003), it has been suggested that

practitioners attempt to match supply chain strategies to either the products they are

producing (Fisher 1997) or their customer demand attributes (Christopher and Towill 2002).

The difficulty in satisfying the demands imposed in supply chain management is evidenced

by the lack of best practice in industry, even though models are available to aid organisations

in integrating and managing supply chains (Childerhouse and Towill 2000). For example, the

implementation of lean practices, such as ‘just in time’ delivery, may reduce inventory in one

business leaving its suppliers with increased inventory and transportation costs (Christopher,

Peck et al. 2006). This may arise because a significant portion of this research has evolved

from cases of industries handling discrete units that are fabricated and/or assembled during

manufacturing, resulting in numerous successful strategies for discrete industries (Dennis and

Meredith 2000).

Although there have been applications across many sectors including automotive, electronics

and consumer products manufacturing, these have almost exclusively been applied to discrete

manufacturing applications, with very few examples reported in the continuous process sector

(Abdullah and Rajgopal 2003). The process industries cover a wide range of businesses,

ranging from continuous facilities in the petrochemical industry, to large batch manufacturing

in steel production, to small batch manufacturing in the pharmaceutical industry. While the

process industry is responsible for a considerable portion of GDP in many countries,

researchers have paid little attention to this large group of industries (Van Donk and Fransoo

2006).

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The difficulty in managing process industry supply chains is exacerbated by the inherent

complexity of these systems. Balancing the priorities, tradeoffs and effects associated with

achieving high levels of throughput, low levels of finished inventory and providing high

levels of customer service is at the heart of supply chain management. This argument, has

been expounded by scholars for some time now, notably the use of multidimensional tools

such as the Balanced Scorecard (see Kaplan and Norton (1996) to aid in the implementation

of strategies in organisations and the concept of Minimum Reasonable Inventory in system

dynamics (see Towill (1994)) as a form of balancing the tradeoffs inherent to supply chain

management. Both of these concepts use multidimensional tools to aid managers in finding

an optimal balance of simultaneously competing and interacting priorities which ultimately

govern the performance of an organisation or supply chain.

3. Provisional Measurement Instrument for Supply Chain

Performance

The classic conflict of interest between throughput of material, levels of inventory, and

delivering the right product to the right customer at the right time will be the focus of this

measurement instrument. Our measurement instrument uses a three dimensional figure to

map the performance of a supply chain based on three factors. Each factor is mapped on a

dimensional axis. The Y axis represents the level of customer service a supply chain can

deliver. The X axis being the throughput of material produced, as the total output of the

supply chain independent of whether the product was sold. The Z axis the level of inventory

present, both in the form of work in progress and finished goods inventory which has yet to

be sold (see Figure 1). Each dimension ranges from a high to low levels of performance and

costs.

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Figure 1 Three dimensions of supply chain management

Reaching maximum efficiency in all three axes is not possible in a real world supply chain

that is subject to process constraints. Rather, every dimension has a point of diminishing

returns where the overall net benefit of maximizing a dimension is less than an optimal

overall result. For example, a steel manufacturer cannot maintain maximum asset utilization

without high levels of inventory to maintain adequate buffers. Buffers are necessary to

ensure that bottlenecks in the system do not adversely influence throughput rates (Tan 2001).

The dynamic nature of supply chain behaviour of has been well investigated through the use

of system dynamics in supply chains. System dynamics has been successfully used as a

means of better understanding dynamic behaviour and improved redesign to improve

robustness and operating effectiveness of these systems (Berry, Towill et al. 1994; Towill

1996).

Developing a strategy whereby the supply chain reaches optimal positions on each axis and

minimises the impact on the other two axes is a core capability within the process industry.

Similar situations are reported in the international business literature where firms must make

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strategic choices based on the three dimensions (inventory, customer service, throughput) that

underpin the transnational challenge (Devinney, Midgley et al. 2001). The common issue to

both literatures is that managers work in operationally constrained environments and are

therefore required to make decisions that will result in an improvement in one dimension at

the detriment of another. In addition, the costs associated with achieving higher levels of

performance must be considered.

As the concept of supply chains are adopted by an organisation, the choice of a strategy will

result in levels of performance which can be represented as a location (x,y,z) in Figure 1.

This location will have a cost associated with achieving this level of performance as well as a

level of profitability.

Implementing a supply chain strategy developed in, or for, a discrete process industry setting

implies that managers are concerned about factors such as operational constraints and

machine reliability will inevitably lead to differences in performance. For some managers,

the desire may not be to adopt one particular orientation and hope that the resulting

performance is equal or exceeding other adopters of the strategy. An alternative approach is

to select a particular location within the three dimensional space. This is relevant because the

desired location will require a combination of strategies tailored to the processes of the

organisation. This would allow managers to set goals based on the current performance of the

supply chain and even set a route to achieve the desired location.

In well established organisations, where performance levels at individual dimensions are well

known, the tradeoffs between dimensions may also be known to exist but the extent of the

impact on strategic orientation may not be known.

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4. Case Study – TDH Steel Ltd.

TDH Steel Ltd.1, is a steel manufacturer in the Asia Pacific region, supplying a wide range of

steel products to the building, construction, manufacturing, automotive, and packaging

industries. Over time, the supply chain at TDH Steel Ltd. has evolved into a complex

arrangement of interconnected operating models to support different information systems and

diverse operating rules within historical organizational boundaries. Production at TDH Steel

is subject to numerous operational constraints and uncertainties. Scheduling is extremely

complex and requires a great deal of attention. Synchronisation between product batches as

well as across parallel lines of production is a primary concern amongst the scheduling

management team because numerous production steps and long production lead times inhibit

a ‘make to order’ strategy. Instead, a primarily ‘make to forecast’ strategy and secondary

‘make to stock’ strategies are used. Forecasted sales are scheduled into production to adhere

to the required large batch sizes, however in most cases the forecasted sales and operational

constraints result in some stock being made regardless of whether its forecast to be sold or

not. This practice simultaneously takes advantage of economies of scale and adheres to the

operational constraints. Throughout the processing stages, scheduling in key product

differentiation steps is used to ensure that forecast levels of product mix are maintained.

Some markets demand shorter lead times than those required for production, those which

typically require higher volumes of product are given lead times similar to those of

production, therefore customer orders are commonly matched to current on hand inventories.

Unfortunately, commonly used management tools for supply chain management decision

making at TDH have yet to deliver an effective method for decision support at TDH as

evidenced by high inventory levels and less than ideal customer service. The level of capital

1 The name of the organization has been changed to protect confidentiality

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intensiveness in production equipment is such that high utilisation of equipment must be

maintained, which inevitably produces stock units that may not be required. Furthermore,

pressure to meet performance targets has not provided managers with the opportunity to

evaluate the wider implications of their operational decisions. Instead, broad experience and

lengthy tenure are used to assist decision makers to better appreciate the multidimensional

nature of the supply chain.

TDH Steel has three main performance metrics for their supply chain, each metric focuses on

a dimension as described in the previous section. Customer service is primarily measured as

the ability of the supply chain to deliver the right product to the right customer at the right

time, this is measured as the percentage of deliveries made in full and on time (DIFOT). The

level of inventory held is measured by the amount of time the inventory spends as inventory

before it is sent out to a customer; days of inventory (DOI). The production throughput is

measured as total production per time as measured in TDH as tones per day. This is an

important metric as in a process industry; the quantity of finished products highly influences

the variable costs of production, and the level of equipment uptime.

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Figure 2 Operationalisation of Supply Chain Metrics

The overall complexity of the supply chain and decades of operational experience have lead

TDH to adopt these three performance metrics as the basis for supply chain performance.

With this measurement instrument, TDH can view, in simple terms, (1) how much is being

produced, (2) how long it is remaining in the supply chain and (3) how well the customers are

being served. Qualitative investigations into the operational and organisational nuances of

the processing facilities within TDH have yielded some significant differences in the (x,y,z)

metrics at different portions of the supply chain. The more customer centric, responsive

portions of the supply chain have evolved at the customer end of the supply chain.

Meanwhile, the more capital intensive portions of the supply chain show a high throughput

focus. The interfaces between these portions of the supply chain often experience problems

and require a great deal of consideration and will be examined closely in future stages of this

study.

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5. Limitations and Conclusions

This research highlighted the benefits and shortcomings of single supply chain strategies and

ignoring the tradeoffs between factors which are heavily interrelated in real world supply

chains. The reality of many supply chains is that the applications of single strategies have

provided little benefit given the complexities of real world supply chains which require

significant alterations of the prescribed strategies. A single strategy would still include

tradeoffs, although these would be fixed depending on the strategy and would not allow the

flexibility of reverse engineering. The measurement instrument presented highlights the

importance of taking into consideration the tradeoffs involved in implementing supply chain

strategies into real world situations.

Process industries are subject to complexities that differentiate them from most other

industries. As such, they do not easily conform to a single supply chain orientation. Until

now, managers in process firms have used intuition and judgement to guide strategic decision

making, possibly due to the complexity of the supply chains they manage. The instrument

presented, allows the visualisation of strategic priorities applicable to process industry supply

chains.

The names of the axes provided are very narrow in definition and would require some

alterations in applying them to other industries and organisations. The instrument is also

limited in that universally speaking; other dimensions may be present in certain industries

which may be significant enough to render this instrument inapplicable unless more

dimensions are added mathematically. Future directions of this research will focus on the

development of continuous-discrete event simulation models that will mimic the behavioural

dynamics of process industry supply chains. The instrument presented in this paper will then

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be applied to the simulated supply chain in order to validate the model and develop theories

that will aid process industry supply chains.

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