Irina Harris, Vasco Sanchez Rodrigues, Mohamed Naim and Christine Mumford
IntroduCtIon
This chapter aims to review the current state of knowledge on the struc-tural design of supply chain and logistics systems. Strucstruc-tural issues include physical flows, location of facilities and the use of information in decision-making protocols. In particular we will discuss the implications of dynamic behaviour and uncertainty of supply chain performance. We will determine the extent to which environmental criteria are being utilized in supply chain design vis-à-vis traditional cost and customer service metrics.
We will assess the current strengths and weaknesses of existing supply chain models and redesign methods, with the goal of highlighting future research challenges.
CurrEnt stAtE of knoWlEdgE of trAdItIonAl supply ChAIns Supply chain dynamics
Historically a supply chain has been defined as ‘a system whose constituent parts include material suppliers, production facilities, distri-bution services and customers linked together by the feedforward flow of materials and the feedback flow of information’ (Stevens, 1989). The main features of a traditional supply chain (Beamon, 1999) are illustrated in Figure 5.1, where the black arrows represent material flows and the dotted arrow represents information flow.
When designing a supply chain network, different levels of decisions need to be considered, from strategic through to operational. Strategic decisions typically have a planning period of many years and long-lasting effects. The identification of the number, locations and capacities of serving facilities, such as distribution centres (DCs) and warehouses, in a supply chain network, would normally be regarded as strategic planning. Tactical decisions involve a shorter planning horizon, and they are usually revised monthly or quarterly. Tactical activities include the selection of suppliers, assignment of products to DCs, and determining the distribution channel and the type of transportation mode. Finally, operational decisions, such as scheduling and routing activities, consider the day-to-day flow of products through the network, the amount of the inventory to be held by the facilities and so on. These decisions can be modified easily within a short period of time, for instance on a daily or weekly basis.
The complexity of a given supply chain is related to the number of echelons or cost centres present, where an echelon is identified as a place where inventory is kept (Tsiakis, Shah and Pantelides, 2001). There are
Distribution centres
Suppliers Manufacturers Retailers Customers Information flow
Figure 5.1 Traditional supply chain Source: Based on Beamon (1999).
clear interfaces between each echelon, namely, suppliers/manufac-turers, manufacturers/distributors, distributors/retailers and retailers/
customers. Material, cash and information flow across each interface.
Typically, individual echelons embrace the following characteristics (Towill, 1991):
perceived demand for products, which may be firm orders or simply
• forecasts;
a production, or ‘value-added’, process;
•
information on current performance (which may be high or low
• quality);
‘disturbances’, for example due to breakdowns, delays, absenteeism;
• decision points where information is brought together;
•
transmission lags for both value-added and other activities;
• decision rules based on company procedures, for example, changing
• stock levels, placing new orders and production requirements.
Characteristics such as these can induce uncertainties within supply chains, which may not be due to any actual variations in market place demand. As far back as 1958 Forrester, in describing production–distribution systems, or what we now call supply chains, noted that demand in the marketplace can become delayed and distorted as it moves upstream in a supply chain, from customers through to raw material suppliers. At any one point in time, processes in various companies in the chain may be moving in different directions from each other and from the market, in response to order or production predictions.
Furthermore, traditional supply chains tend to ‘amplify’ marketplace variations. For example, the variations in orders placed on the factory may be much larger in magnitude than the fluctuations in the underlying marketplace demand. Critically, traditional supply chains can introduce
‘periodicities’ or ‘rogue seasonality’ (Thornhill and Naim, 2006), which can be misinterpreted as a consequence of seasonal variations in the marketplace, rather than a property of the supply chain itself. This ‘law of industrial dynamics’ phenomenon (Burbidge, 1984) has more recently been described as the ‘bullwhip’ effect (Lee, Padmanabhan and Whang, 1997a, 1997b) to include other forms of induced variations such as batching effects (Towill, 1997) and promotion activities (O’Donnell et al, 2006).
Bullwhip is an important measure as it is symptomatic of a poorly performing supply chain (Jones and Simons, 2000). It is a surrogate measure of production adaptation costs (Stalk and Hout, 1990) and implies the inclusion of ‘just-in-case’ stock holding to buffer against uncertainties. Evidence in many forms suggests that the bullwhip effect and Forrester’s empirical conclusions are highly applicable to the vast majority of supply chains.
Forrester (1958) noted that attempts to reduce poor supply chain dynamic behaviour can exacerbate the problem. Counter-intuitive behaviour occurs because the causes of the behaviour are obscured from the decision makers in the chain. A recent example of this is given by Disney, Naim and Potter (2004). In a controlled experiment using the well-known MIT Beer Game, they implemented a number of scenarios including a traditional structure, electronic point of sales (EPOS) and vendor-managed inventory (VMI). The three supply chain scenarios researched are summarized in Figure 5.2. A traditional supply chain may be characterized by four ‘serially linked’ echelons in a supply chain.
Each echelon only receives information on local stock levels and sales, and will determine its order on to its supplier using this knowledge, in conjunction with forecasts, work-in-process targets and inventory-holding requirements.
In the EPOS-enabled scenario, the end-consumer sales are transparent to all members of the supply chain. This is equivalent to the situation in grocery supply chains, where the data is available electronically directly from the retailer and can be used by all supply chain members to help in their stock-holding and ordering policies. However, each echelon still has to deliver, wherever possible, the goods ordered by its imme-diate customer. In the VMI scenario, the distributor in the two-echelon structure aims to take a holistic approach and simultaneously manage
Retailer Distributor Warehouse Factory
Traditional supply chain
Customer
Retailer Distributor Warehouse Factory
EPOS supply chain
Customer
Retailer
Distributor Warehouse Factory
VMI supply chain
Customer
Flow of information upstream Flow of materials downstream
Figure 5.2 Three supply chain scenarios Source: Based on Disney, Naim and Potter (2004).
both the retailer’s and the distributor’s stocks. The distributor is given information on the retailer’s sales and stock levels. As a consequence, the retailer does not need to place orders on the distributor, because the distributor is at all times fully aware of the retailer’s stock levels. Instead the distributor dispatches adequate amounts of material to ensure that there is enough stock at the retailer to satisfy customer service levels. The other echelons in this scenario, namely the warehouse and factory, retain the traditional ordering structure.
Disney, Naim and Potter (2004) made some interesting discoveries when investigating these three scenarios. The traditional supply chain behaved much as predicted, and extreme bullwhip was induced along the whole supply chain, resulting in increased inventory and backlog holding costs. In the EPOS scenario two modes were simulated: one in which no communication between players was allowed, and a second in which collaborative planning and replenishment was permitted. They discovered that EPOS, both with and without collaboration, incurred high negative inventory (backlog) costs. Nevertheless, EPOS with collab-oration does well at reducing bullwhip in the supply chain.
The worst-performing scenario proved to be the VMI supply chain, which had both the highest inventory holding costs and the worst bullwhip chain. Although theory suggests that VMI should prove to be highly beneficial, due to its holistic approach to managing the supply chain (for example, see Hosoda et al, 2008), Disney, Naim and Potter (2004) found that the players had problems in implementing the concept even though they were provided with well-documented protocols.
An alternative to the above supply chain structures, which Disney, Naim and Potter (2004) also tested, was the elimination of one or more echelons in the supply chain; for example, goods may be shipped direct to a customer from the factory. Eliminating an echelon removes a decision point and reduces total lead times in one simple step. This approach was found to produce a better dynamic performance, in terms of bullwhip and inventory costs, than any other scenario (Wikner, Towill and Naim, 1991).
Taking the above principles into account, once a supply chain has been designed and implemented, it then has to be managed. Li et al (2005) have developed a list of sub-constructs for supply chain management practices to link them to performance. These constructs are:
Strategic supplier partnership: long-term relationships between
• companies and their suppliers. This is designed to align the strategies of the companies with their suppliers.
Customer relationship: practices aimed at defining how companies can
• manage customer complaints and develop long-term relationships with
them. Third-party logistics providers need to define who their main strategic customers are.
Information sharing: the extent to which strategic information is
• shared with suppliers.
Information quality: relates to how accurate, credible, current and
•
adequate the information exchanged is.
Internal lean practices: the extent to which there are practices of waste
• elimination and value creation within the supply chain.
Postponement: represents the practice of delaying some activities
•
within the supply chain. It has the goal of making the supply network more responsive.
Commensurate with the foregoing, Towill (1999) also developed a framework to enable the effective operations management of supply chains. He stated a number of preconditions that companies need to meet in order to simplify material flow. These preconditions, described as rules, require unbiased and noise-free information flows, compression and standardization of lead times in all work processes and choosing the smallest possible planning horizon.
Supply chain management and transport logistics
Supply chain management is a field that has traditionally been studied more from a marketing and manufacturing perspective, than from a transport point of view. Some recent papers indicate that the emphasis is changing, however. Stank and Goldsby (2000), for example, developed a decision-making framework that positions transport in an integrated supply chain. Meanwhile, Potter and Lalwani (2005) state that there has been little evidence of transport management techniques explicitly addressing supply chain issues, although there is evidence of demand vari-ability, and subsequently, the bullwhip effect on transport operations.
They go on to state that, although there is anecdotal evidence that bullwhip has a negative impact on transport performance, a negative relationship has not been proven. In addition, Potter and Lalwani developed a framework that integrates five main strategic themes, namely coordinated distribution network management, transport cost visibility, exploitation of ICT, collaborative relationships and information feedback.
Managing uncertainty in supply chains and transport
We have noted that serious problems such as bullwhip can arise as a result of uncertainty in supply chains. A considerable amount of research
has been undertaken on uncertainty in supply chain management (Davis, 1993; Mason-Jones and Towill, 1999; Van der Vorst and Beulens, 2002;
Geary et al, 2002; Peck et al, 2003), but in such work transport has typi-cally been regarded as a marginal activity within supply chains (Stank and Goldsby, 2000) and has not been considered explicitly. To start to rectify these shortcomings, it is necessary to determine what forms of uncertainty affect transport operations. According to Van der Vorst and Beulens (2002), supply chain uncertainty refers to decision-making situa-tions in which decision makers do not know what to decide. This inde-cision has many potential causes, including a shortage of one or more of the following: clear objectives, information processing capacity, or infor-mation about the supply chain or its environment. Such situations are hampered by an inability to accurately predict the impact of possible control actions on supply chain behaviour, or more simply, decision makers may lack effective control actions, per se. In response to such uncertainties at a strategic level, supply chain agility is proposed by Prater, Biehl and Smith (2001). This may be achieved through flexibility and speed in the three key activities of sourcing, manufacturing and delivery. Naim, Aryee and Potter (2007) specifically address the issue of transport flexibility as a response to supply chain uncertainties, stating that various types and degrees of transport flexibility are required for different supply chain needs. For example, different solutions may be needed for routine activities (simply moving goods from A to B) than would be appropriate when customized or tailored solutions are required, involving multi-modality, warehousing provision or inventory management. Types of transport flexibility include mix, routing, fleet and vehicle flexibility.
Davis (1993) was the first author to explicitly consider uncertainty as a strategic issue for supply chain performance when he stated ‘there are three distinct sources of uncertainty that plague supply chains: suppliers, manufacturing, and customers. To understand fully the impact on customer service and to be able to improve performance, it is essential that each of these be measured and addressed.’ This work produced a framework that was initiated in Hewlett-Packard in the early 1990s.
Building on the work of Davis (1993), Mason-Jones and Towill developed the Uncertainty Circle Model (Figure 5.3) as a way of defining the different sources of uncertainty that can affect supply chain performance.
They confirmed that uncertainty is a strategic issue in supply chains, and suggested that it originates from four main sources: the supply side, the manufacturing process, the control systems and the demand side (Mason-Jones and Towill, 1999). Hence they extended Davis’s work by adding a further source of uncertainty, the control systems. Moreover, they emphasized that uncertainty initiated in the supply side and/or in
the manufacturing process can be mitigated by the application of lean-thinking principles. Uncertainty caused by control systems and/or on the demand side, on the other hand, requires an understanding of the dynamics of the whole system (Mason-Jones and Towill, 1999).
Another framework that takes uncertainty into account is the SCOR Model (Supply Chain Council, 2008). In a similar vein to Mason-Jones and Towill (1999), this model includes the supplier side as ‘source’, the manufacturing process as ‘make’ and the demand side as ‘deliver’. It considers the logistics network concept by introducing the repetitive source–make–deliver sequences. Furthermore, the model extends the
‘make’ dimension by introducing the concept of ‘value adding’. However, as with the Uncertainty Circle Model (Mason-Jones and Towill, 1999), it does not explicitly consider transport as a strategic supply chain process.
The Uncertainty Circle approach was further developed in research on the automotive industry by Geary et al (2002), where one of the main outcomes was an identification of the main issues associated with different types of uncertainty, examples of which are given in Figure 5.4. An attempt was also made to link the causes and effects of uncer-tainty or supply chain disruption. However, neither Van der Vorst and Beulens (2002) nor Geary et al (2002) considered transport as a strategic component of the supply chain or as a specific source of supply chain uncertainty. Instead they adopted a purely manufacturing perspective.
The recent body of work on supply risk and vulnerability has added an important new dimension of exogenous events to the Uncertainty Circle (Peck et al, 2003). Examples of such events might include terrorism, indus-trial action, disease epidemics or severe weather conditions. Transport operations may be seriously affected either directly through such events, or more indirectly through government regulations and controls aimed at preventing their occurrence or minimizing their impact. Furthermore, businesses may decide to change their strategies, including their supply
Supply
Side Manufacturing
Process Control Systems
Demand Side
Physical Flow Information Flow
Figure 5.3 Uncertainty Circle Model Source: Mason-Jones and Towill (1999).
chain policies, to minimize the future impact of government interven-tions such as taxation changes or new regulainterven-tions that may be looming on the horizon but whose shape is not yet known for certain.
In the view of the present authors, work on the transport perspective of supply chain uncertainty is timely. Recently Prater (2005) developed an uncertainty framework that can be used to determine the causes of supply chain and transport uncertainty. This framework classifies uncertainty at both macro and micro-levels, as shown in Table 5.1. At the macro-level, uncertainty is typified in terms of general variation, foreseen uncer-tainty, unforeseen uncertainty and chaotic uncertainty. Getting down to the more micro-level, general variation consists of variable, multi-goal and constraint uncertainties. Foreseen uncertainty is caused by ampli-fication of the demand from customers on inbound areas of the supply chain and parallel interactions between members of the supply network that are at the same horizontal level such as carriers and/or suppliers.
Unforeseen uncertainty is the consequence of deterministic chaos that disrupts long-term planning, such as that resulting from road congestion.
Finally, chaotic uncertainty is general non-deterministic chaos that cannot be predicted by a mathematical function, for example natural disasters or political problems that disrupt the supply chain flow and cannot be predicted with any accuracy.
Process side
No measures of process performance Reactive rather than proactive maintenance Random shop floor layout
Interference between value streams Short-notice amendments to suppliers Excessive supplier delivery lead time Adversarial supplier relationships No vendor measures of performance No customer stock visibility
No synchronization and poor visibility amongst adjacent processes Adversarial customer relationships
Large, infrequent deliveries to customer
Continuous product modifications – high level of obsolescence Poor stock auditing
Incorrect supplier lead time in MRP logic Infrequent MRP runs
Supply side
Demand side
Control side
Figure 5.4 Examples of bad practices from the four sources of supply chain uncertainty
Source: Geary, Childerhouse and Towill (2002).
Table 5.1 Micro-level types of uncertainty in supply chains
macro-level micro-level
General variation Variable, multi-goal and constraints Foreseen uncertainty Amplification, and parallel interactions Unforeseen uncertainty Deterministic chaos
Chaotic uncertainty General non-deterministic chaos Source: Prater (2005).
Sanchez Rodrigues et al (2008) have extended the Uncertainty Circle Model (Mason-Jones and Towill, 1999) from a manufacturing to a logistics triad perspective (Bask, 2001). The model is intended to provide a framework within which organizations, including logistics providers, can develop a supply chain strategy to mitigate the effects of uncertainty.
By categorizing uncertainty into foreseen, unforeseen, chaotic and so on, organizations may determine where the greatest uncertainties lie, and hence develop a prioritized plan for supply chain re-engineering by initially targeting those uncertainties with the most significant implica-tions for supply chain efficiency. The Logistics Triad Uncertainty Model is outlined in Figure 5.5.
External
Uncertainty External
Uncertainty
External Uncertainty
External Uncertainty
Supplier Carrier
Control Systems
Customer
Physical Flow Information Flow Relationships
Figure 5.5 Logistics Triad Uncertainty Model Source: Sanchez Rodriques et al (2008)
Performance measures for traditional supply chains
To ensure the efficient running of a supply chain, a range of performance measures have been developed over the years. Shepherd and Gunter (2006) presented a taxonomy and critical evaluation of performance measurements and various metrics for supply chains identified from 42 journal articles and books and from online resources published between 1990 and 2005. From their review, it is clear that different research studies classify the measures in very different ways. Nevertheless a taxonomy is presented according to the following classification:
According to processes identified in the SCOR model: plan, source,
•
make, deliver or return (customer satisfaction). This allows the identi-fication of measures that are appropriate at the strategic, operational and tactical levels.
Whether they measure cost, time, quality, flexibility or innovation. It
•
is important to differentiate between cost and non-cost measures such as time and quality because if a supply chain relies only on cost measures, it can produce a misleading picture of supply chain performance (Chen and Paulraj, 2004).
Whether they are qualitative or quantitative measures. Qualitative
• measures, such as customer satisfaction, reflect the happiness of the customers with the service and cannot be measured using a single numeric measure. Quantitative measures, such as cost, flexibility or customer responsiveness can be directly described numerically.
Shepherd and Gunter’s review identified a total of 132 performance measures across different processes in the SCOR model. A very small proportion were related to the process of return or to customer satis-faction (5 per cent), compared with other processes such as plan (30 per cent), source (16 per cent), make (26 per cent) and deliver (20 per cent).
Regarding the cost classification, the major proportion focused on cost (42 per cent) over non-cost measures such as quality (28 per cent), time (19 per cent), flexibility (10 per cent) and innovation (1 per cent). The quantitative measures (82 per cent) substantially exceeded the qualitative (18 per cent). One of the main problems with the all metrics discussed is that they do not capture the performance of the supply chain as a whole.
grEEn supply ChAIns
Beamon (1999a) recognized the essential objective of the green or extended supply chain as the evaluation of the total direct and eventual environ-mental effects of all processes and all products. A fully integrated supply
chain (Figure 5.6) is described by Beamon as a supply chain having all the elements of the traditional configuration (Figure 5.1), but extended to incorporate product and packaging recycling as well as reuse and/or remanufacturing operations within a semi-closed loop. Consequently, it incorporates the elements of a reverse supply chain, reflecting the entire life cycle of the goods. Therefore, the main focus of a green supply chain is reducing energy consumption, emissions and waste, and increasing recycling and reuse.
To help deal with of the additional complexity of the extended supply chain, Beamon identified a new set of potential strategic and operational considerations:
the number and location of facilities for product/packaging collection
•
and re-use;
the effects of traditional supply chain strategies (eg decentralized
• versus centralized facility location) on environmental performance;
simultaneous operational and environmental supply chain
optimi-•
zation;
incorporating environmental and operational goals into traditional
• analysis;
Recycling
Collection W
W W
W
W W
W
W
Customers Retailers
Manufacturers
Distribution centres
Suppliers
W
Remanufacturing/
Reuse
Waste/dispose materials
Figure 5.6 The extended supply chain Source: Based on Beamon (1999).
the level and location of buffer inventories in forward and reverse
•
supply chains;
the impact of the demand and recovery processes (which are not
• completely correlated) on the uncontrollable growth of unwanted parts or the non-availability of the critical parts and other decisions.
Green performance measures
A review of performance measurement systems and metrics under devel-opment for green supply chains is given by Hervani, Helms and Sarkis (2005). The selected list of metrics they identified range from atmospheric emissions to energy recovery. They include measures for on-site and off-site energy recovery, recycling and treatment, spill and leak prevention and pollution prevention. Additional general measures include total energy use, total electricity use, total fuel use, other energy use, total water use, habitat improvements and damages due to enterprise opera-tions, cost associated with environmental compliance, and others.
Hervani, Helms and Sarkis point out that organizations may choose their environmental performance measurements specifically to meet new government regulations on emissions, energy consumption or the disposal of hazardous waste.
From a logistics perspective, Aronsson and Huge-Brodin (2006), in their comprehensive literature review, identified the measurement of emissions as one of the most popular ways of assessing environmental impact. They noted, however, that even though the direct environmental impact can be assessed in terms of emissions, it is the root causes of these emissions that need to be addressed. Exactly what action to take needs to be determined by an appropriate analysis of the supply chain as a whole.
Determining which sustainable measures to use and the difficulty of calculating them has been discussed by several researchers (Aronsson and Huge-Brodin, 2006; Hervani, Helms and Sarkis, 2005; Beamon, 1999).
Some researchers have noted that an improved environmental impact sometimes follows a supply chain redesign exercise based on traditional performance measures such as cost or customer service. In such cases improvements in environmental performance can be viewed as positive side effects of traditional methods, without having a fully integrated green supply chain. The factory gate pricing (FGP) concept, where the retailer is responsible for transportation of the product from the supplier, has been analysed for the UK grocery (Potter et al, 2003) and the Dutch retail industry (Le Blanc et al, 2006). Both studies show that cost reduc-tions have brought significant environmental benefits, such as reduced congestion and transport kilometres/miles. Potter et al (2003) analysed