Chapter 9: Value chain analysis of the UK food sector – Delivering Performance in Food Supply Chains

9

Value chain analysis of the UK food sector

K. Zokaei,     Cardiff University, UK

Abstract:

The chapter reports on the findings from a major research project into the UK agri-food industry commissioned by the Department for Environment Food and Rural Affairs over a period of four years. Several researchers were involved in this project which mapped 33 extended supply chains in detail and produced a portfolio of findings across four primary food sectors that is cereals red meat horticulture and dairy. This chapter puts forward the data collection protocol deployed during this extensive project which should serve as a practical step-by-step guide to analysis of supply chains for readers. It is explained that there is a dearth of methodologies for supply chain analysis and improvement. Therefore one of the key contributions of this chapter is to put forward this successfully applied chain improvement guideline. Moreover the chapter explains the subtle difference between chain analysis for improving efficiency and effectiveness. The proposed ‘value chain analysis’ method is distinctive in that it emphasises improvement in chain effectiveness while also delivers considerable efficiency gains. Finally the method and its subtle differences are explained through an explanatory case study.

Key words

supply chain management

value chain

value stream mapping

lean thinking

UK food sector

supply chain improvement

9.1 Introduction

Analysing and improving food supply chains have been topical issues over the past decade. Globalization of trade, sophistication of ever more demanding customers and various animal disease outbreaks have greatly contributed to this trend. Surprisingly there have been few methodologies documented in the literature for implementing successful food value chain improvement initiatives. This chapter reports the findings of a major research project and puts forward a tested data collection protocol for food value chain analysis (VCA) which should serve as a practical step-by-step guide for the reader. The proposed VCA method and data collection protocol was developed and adapted in several primary agri-food sectors in the UK.

In discussing the concept of value chain analysis, this article emphasises that ‘doing things right’ in the chain should become subordinated to ‘doing the right things’. Many value chain improvement initiatives focus on efficiency improvements while falling short of addressing the overall objective of the system. Some initiatives forget that the value chain is no more than a channel for delivering what the end consumer demands. This chapter looks at the evolution of supply chain management theories from ‘supply chain’ to ‘demand chain’ to ‘value chain’ and explains their differences. Finally a case study is provided to explain how the proposed VCA method is deployed in practice to improve both efficiency and effectiveness of chains.

The notion that key processes across the supply chain form a value chain and the method of analysing the value chain for competitive advantage was introduced by Michael Porter of Harvard Business School (Porter, 1985). Subsequently, VCA has been developed in the management accounting literature (Shank, 1989; Shank and Govindarajan, 1993) and more recently in the operations management literature (Rainbird, 2004, Zokaei and Simons, 2006) following on from previous claims that supply chain management should go beyond a narrow focus on efficiency management to deliver superior value to the end consumer (Christopher, 2005). Value chain analysis refers to a structured method of analysing the effects of all core activities on cost and/or differentiation of the value chain. According to Dekker (2003, p 5) VCA analyses where in the supply chain the ‘costs can be reduced or differentiation can be enhanced’.

Therefore, in an operational sense VCA is a subset of supply chain management (SCM). The essence of the VCA methodology developed by the author and colleagues at Cardiff Business School is to produce a systemic map of the value chain and a systematic method of analysing each strategic activity in relation to the consumer value. In this sense, the proposed VCA method draws extensively upon business process re-engineering (BPR) (Hammer and Champy, 1993), lean thinking (Womack and Jones 1996), value stream mapping (Hines and Rich, 1997; Rother and Shook, 1998; Jones and Womack, 2000) and Porter’s value chain model. A key attribute of the proposed method is that analyses and metrics are based on determinant attributes such as quality and time, not on output financial attributes (Fitzgerald et al., 1991). The advantages proposed for deployment of operational measures are that they are the leading indicators of financial attributes and that from a change management perspective operational measures are more easily shared across company boundaries than sensitive financial data. The application of the proposed VCA method is explained in detail in the following. But first the evolution of the SCM body of knowledge is discussed to show the importance of the concept of VCA.

9.2 From supply chain management to value chain management

Value Chain Analysis (VCA) is a different approach from the conventional supply chain improvement approaches in that it emphasizes the concept of consumer orientation and consumer value. Whereas, conventional supply chain improvement initiatives predominantly focus on waste elimination and cost reduction (i.e. chain efficiency), VCA is concerned with differentiation and value enhancement in the supply chain as well as cost reduction.

Supply chain management (SCM) is a fairly new concept which only started to make a significant appearance in the management literature in the 1980s (Oliver and Webber, 1982; Houlihan, 1985; Stevens, 1989) and has since been popularized by several authors as an independent field of study (Cooper and Ellram, 1993; Davenport, 1993; Christopher, 2005; Mentzer et al., 2001; Gibson et al., 2005, Cousins et al., 2006). Nonetheless, much of the underlying thinking dates back several decades. In fact, the roots of SCM can be traced to systems dynamics and analysis (Forrester, 1958), integrated logistics management (Bowersox et al., 1959) and the idea of forming cooperative relationships with suppliers (Farmer and Macmillan, 1976).

Arguably, SCM is not developed enough to be regarded as an independent discipline. But the general consensus amongst academics is that SCM is a general problem domain represented by a significant – yet diverse – body of knowledge in the literature (Special Issue, 2006).

Table 9.1 provides a rounded understanding of the evolution of SCM concepts from logistics to supply chain to demand chain to value chain. It reviews some of the most frequently cited definitions of chain management presented in chronological order. The review illustrates that although there is little consensus on the scope and meaning of the value chain or value chain management, an evolutionary trend is evident in the literature. The latter chain management contributions are much more strategic and broader in scope in conceptualizing chain management.

Table 9.1

Overview of some key contributions concerning the value chain and value chain management

The above literature review confirms that there is little consensus on the scope or meaning of SCM or VCM. It also explains that chain management has evolved from a narrow focus on physical aspects to a broad multifaceted theory. The early conceptions of SCM (Houlihan, 1985; Stevens, 1989) emphasize the importance of a holistic approach as opposed to single firm optimization. In fact, chain management theory begins by showing the potential which lies beyond the boundaries of a single firm. The original supply chain contributions largely focus on the physical aspects in the supply chain, for example the dynamics of information and material flows (Forrester, 1958) and inventory management and transportation (Jones and Riley, 1985). The narrow focus of the early SCM literature has inspired several authors to compare and contrast SCM with integrated materials and logistics management (Cooper, Lambert and Pagh, 1997; Hewitt, 1994; Houlihan, 1985). These authors have generally come to the same conclusion that SCM is a much broader concept encompassing issues beyond the boundaries of the logistics sub-system.

The accounts of chain management in Table 9.1, however, transcend this narrow focus by taking account of broader issues such as long-term performance of the whole chain (CLM, 1998), supply chain competitiveness (Christopher, 2005), consumer enrichment (Ross, 1998) and new product development (Womack and Jones, 1996). Therefore, it is concluded that over the past two decades, SCM has evolved from a one-dimensional subject with a rather narrow focus on logistics and physical aspects of material flow into a multi-faceted theory encompassing a broad range of subjects. For example, Hewitt (1994, p. 7) contends that modern chain management simultaneously addresses ‘all aspects of the operation of the supply chain, including work practices, information flows and authority/ decision making structures’. Modern SCM can be more appropriately described as value chain management since it encompasses value enhancement and strategic differentiation as well as cost reduction and operational efficiency improvements. Internationalization of trade, sophistication of technology and markets, increased global competition and the rise and dominance of the Japanese production philosophies (Womack and Jones, 1996; Hines, 1994) have contributed immensely to the evolution of SCM and its core concepts.

VCM is best described in Ross (1998) as the synchronization of competencies along the whole chain to create unique, innovative and individualized sources of consumer value. Another notable conjecture by Womack and Jones (1996) describes ‘value stream management’ as the integration of the problem solving and new product development task, management of the information task, and the physical transformation and transportation task. Christopher (2005) is more direct in describing SCM as ‘demand chain management’ to reflect the market orientation required in today businesses.

Chain management is a domain where efficiency improvements are the prime objective perhaps because of its origin in logistics and operations management, for example time-based competition (Stalk and Hout, 1990; Womack and Jones, 1996; Christopher, 2005), cash-to-cash time (Bowersox and Closs, 1996), quality-based competition (Womack et al., 1990) and costbased competition (Shank and Govindarajan, 1993). Few recent publications, however, emphasize the importance of enhanced consumer satisfaction in the context of chain management (Zokaei and Hines, 2007; Zokaei and Simons, 2006; Hines et al., 1998). In this context, understanding consumers’ attributes and jointly striving for augmentation of consumer satisfaction are imperative to successful VCM. Value chain analysis is a methodological approach both for identifying best solutions to add superior value to the end customer and eliminating operational waste. The following section provides a step-by-step practical guide to VCA.

9.3 Methodology for value chain analysis

9.3.1 Ten days data collection protocol

As mentioned earlier, the following VCA method and data collection protocol was developed during the Food Chain Centre’s VCA programme which looked at four key sectors within the UK agri-business industry, dairy, cereals, red meat and fresh produce. This major piece of research consisted of in-depth analysis of 33 value chains from primary production to the point of consumption covering different routes to market and different raw materials (e.g. pork, beef and lamb in the case of red meat industry). Each one of the chains was studied independently as a separate project facilitated by a lead researcher and on average took three to five months to complete.

Following a recommendation by the Policy Commission on the Future of Farming and Food (also known as the Curry Commission), the Food Chain Centre (FCC) was established in 2002 to ‘. . . bring together people from each part of the food chain’. FCC then linked up with several industry and levy bodies in the UK fast moving consumer goods (FMCG) and agrifood sectors such as the Institute for Grocery Distribution (IGD), Efficient Consumer Response (ECR), the National Farmers Union (NFU), Home Grown Cereals Authority (HGCA), Dairy Industry Forum (DIF) and Red Meat Industry Forum (RMIF) to launch an extensive research initiative widely known as the VCA programme. FCC received several million pounds joint funding from Department for Environment, Food and Rural Affairs (DEFRA) and the Department for Trade and Industry (DTI) to improve supply chain performance and vertical collaboration in the UK agri-business sector and, subsequently, commissioned the Lean Enterprise Research Centre (LERC) at Cardiff Business School to analyse 33 value chains covering different routes to market as well as different raw materials (e.g. pork, beef and lamb in the case of red meat).

A standard data collection protocol was developed during the VCA programme referred to as the ‘ten days VCA method’ (Zokaei and Simons, 2006; Francis, 2004). The ten days VCA protocol is rooted in lean thinking (Womack and Jones, 1996) and, similar to lean value stream mapping techniques, it begins with the selection of a single product family for analysis. A product family is defined as products or stock keeping units (SKU) that flow through similar processes in their value chains. The method requires establishment of a value chain team to follow the flows of the selected product across firms and functions.

Generally, a cross-company team of various stake holders (e.g. primary producer, processors, retailer, food service, etc) were pulled together in each project and on average 8–12 contact days were spent with the team members in the field. All data was treated as confidential unless cleared by companies and a confidential report was published for each project containing a detailed analysis of the selected product. There were also various case studies and sector specific final reports published for the sponsors (see Table 9.2).

Table 9.2

Overview of value chain analysis programme activities

Broadly, the data collection protocol consisted of four stages:

1. Team building and introduction: At this stage, the team was familiarized with the mapping and analysis techniques deployed during the project and also the basic principles of SCM. At least one representative from each participating firm was committed to ‘walk’ the whole value chain from farm to the end point of sales or point of consumption depending on the project. A benefit-share agreement was put in place to ensure that the potential gains were fairly shared. Early team building activities had a great impact on the overall success of the projects and were recognized as significant events later on during the project.

2. Inter-firm and intra-firm data collection: During this stage a current state map of the physical and information flows along the whole supply chain was constructed with a specific focus on the time data (Rother and Shook, 1998). Rother and Shook were first to document the lean mapping technique for a single firm which was later extended to the whole supply chain by Jones and Womack (2000) both heavily borrowing from Toyota’s approach to value chain analysis. The team walked the whole value chain and collected the necessary information over a period of on average 3–4 months. Some common units of analysis during the mapping stage are time, delivery and quality recorded for each echelon along the chain. Also, the team looked at operations and logistics efficiency measures such as demand amplification, on-time/in-full delivery performance, lead times and defective parts. Financial data were not investigated to ensure maximum buy-in from all participant companies. Mapping of the end-to-end supply chain and collection of the current state data often required 4–5 days in the field depending on the size and complexity of the chain.

3. Evaluation of the current state and suggestions for the future state of the supply chain: Having gained a clear understanding of the current state, the team debated the data to identify potential improvement opportunities both at the whole chain and individual firm levels. Also, there was an opportunity to compare and contrast the current state against the team’s understanding of the consumer value. The VCA was an opportunity for the team members to connect their role in the chain with the ultimate satisfaction of consumers. Clearly, consumer satisfaction can either come at a basic level where known requirements are met or it can come at a much more advanced level where consumer expectations are exceeded.

    Normally various improvement opportunities were identified ranging from easily achievable fruits to very difficult.

4. Action planning: In the final stage of the project an action plan was developed to take the supply chain from the current state to the future state based on the immediacy of the actions, the size of the prize, availability of change resources and the relevance of the identified improvement opportunities to the consumer needs. The VCA project did not go further into a detailed implementation phase.

The data collection protocol (i.e. the ten days activity plan) is described in Table 9.3.

Table 9.3

Value chain analysis: the ten days activity plan

Session Event Activities
1 Initial team building workshop Explanation of key concepts such as flow, pull, demand amplification, etc.
Explaining value stream mapping techniques and tools deployed during VCA, e.g. process activity mapping, product variety funnel, etc.
Explaining principles of collaboration along the chain.
Discussing a benefit sharing agreement.
Identifying the core team members.
2 Workshop: current state Selecting a suitable product group for mapping. For example, choosing the largest mutual flow or a product with the biggest potential for improvement.
Creating a generic big picture (current state) map of the value chain.
3, 4, 5, 6 On-site mapping Creating detailed current state maps for individual firms along the chain, e.g. farm, food processor, distribution centres and retail store. The current state maps cover both the physical and information flows. Also, the current state maps bear all the relevant operational (determinant) performance indicators.
Identifying internal operational improvement opportunities at each facility.
7 Workshop: whole chain ideal state map Discussing and creating an ideal state map so that the whole team can aspire towards a single shared vision, e.g. an ideal lean value chain.
Identifying, discussing and categorizing consumer value.
Identifying key performance indicators (KPIs) for the whole chain.
8, 9 Workshop: future state map Discussing and creating a future state for the whole chain. The ideal state is a vision whereas the future state is an achievable target. At this workshop the ideal state map is rationalized to the future state map.
Identifying key projects towards the future state.
Linking key projects (opportunities for improvement) with the measures of consumer value to identify the vital few projects for improvement.
Creating a clear action plan where all key stake holders and people responsible for implementation are identified.
10 Presentation of final results Team presentation of recommendation for improvement and findings to top tier management of all companies involved.
Confirm proposal with all stake holders and various project owners.
Discussions concern benefit allocations and milestones.
Final decisions taken about which improvement projects to progress.

9.3.2 Food value chain analysis programme: generic findings

There were a number of generic findings across the four sectors. The following compiles the findings in different sectors into a single table providing comparisons and discussions. Interestingly, the VCA project in different sectors identified remarkably similar results. Also, it is evident that the emphasis of the VCA work has predominantly been on efficiency factors such as transport, quality, demand management and inventory levels. What’s more, the VCA reports, in all four sectors, call for greater supply chain integration and collaboration. Clearly supply chain collaboration is required for improvement of both efficiency and effectiveness of supply chains. The following table demonstrates common themes across different sectors (where findings have been similar they have been bundled under one single theme).

Six key concerns can broadly be identified as common themes amongst all 33 chains in the four sectors analysed during the VCA programme (see Table 9.4). The other three issues each appear in at least two sectors. Five of the six common concerns directly relate to supply chain efficiency, whilst the sixth one is the lack of understanding of consumer value which is related to supply chain effectiveness.

Table 9.4

Cross-sector findings from the UK food VCA programme

Identification of consumer value is the first principle of lean and the VCA project created a rare opportunity for the team members to connect supply chain activities with the actual requirements of the end consumers. Although all the sector specific reports (even if only briefly) touch on a lack of understanding of consumer needs, none puts forward a practical solution for improving consumer alignment along the chain, let alone enhancing consumer experience (FCC, 2007). More recently, the Food Chain Centre published its completion report reiterating the same problem: ‘It is rare to find an understanding of consumer needs shared through a product chain but when achieved, it helps greatly to promote trust and innovation’ (FCC, 2007 p. 19). Yet again, the report does not fully explain the issue or to show how realignment can be achieved and why it boosts chain performance and competitiveness.

9.3.3 Food value chain analysis: a case study

This case study looks at the entire supply chain from a medium size pig farm through a consolidated abattoir and processing plant to a public sector canteen. The fieldwork for this case study was conducted as part of the VCA programme in the UK. The companies involved were a farm, a meat packer, a food service company and the catering department of a public sector company. A team of senior company representatives and two academic facilitators followed pork legs and loins from farm to canteen. Table 9.5 lists the companies involved and representatives from each company.

Table 9.5

Supply chain improvement core team members

No. Company Representative
1 Farmer No representation in the core team
2 Abattoir and meat packer General sales manager
3 Food service company Director of ‘public catering’ supply Senior buyer – fresh foods
4 Public organisation catering department Operations manager
5 Cardiff University The author

The mapping exercise showed that the UK canteens have a relatively steady demand owing to almost a fixed number of people being catered for daily with occasional spikes in demand. Notably, the main reason for the team members participating in the VCA improvement was to improve logistical efficiency and that the chain tightly adheres to the target budget. So, at the outset, the VCA participants were predominantly focused on improving whole chain efficiencies and reducing cost. The food service company supplied a food range of around 1600 products across three temperature bands, ambient, frozen and chilled, delivered to around 1000 delivery points, making 150 000 deliveries and assembling 21 million food items per year. The total value of this catering contract in 2005 was just less than £100 million per annum, where approximately £15 million was spent on red meat procurement only (including pork).

This case looks at the supply of frozen pork loins and legs from a meat packer plant in East Anglia. The farm has an integrated system of cereals, potatoes and pigs located in Lincolnshire with a long-term relationship with the meat packer. The following shows how the supply chain team was able to identify the disconnectedness of consumer value both with the product attributes and the supply chain activities. Also, there are discussions about how processes along the supply chain were potentially realigned with the consumer requirements and why supply chain effectiveness was partially improved. This is followed by a description of the subsequent efficiency gains.

Current state findings and analysis

As explained above, varieties of mapping techniques are deployed during the VCA such as a quality filter map, a delivery adherence map and demand amplification (Hines and Rich, 1997). The relevant tools were introduced to the team members at the outset of the project. The most basic tool deployed was process activity mapping (PAM). PAM is a means of recording every step along the chain and a platform for creating current state maps. It captures the details of all the tasks required for completion of each process including time taken to complete each task, distances moved and the number of times operators touch the product during each task. A separate PAM sheet was created for every part of the chain and activities were categorized as value adding (VA) and non value adding (NVA) along the process. Only a fraction of activities were considered to be value adding. The aim was to increase value adding operational time where possible. In the lean approach the ultimate arbiter of value was the end consumer and the yardstick for determining VA and NVA activities was the consumers’ willingness to pay for the service.

It must be noted that some NVA are necessary given the technical and practical constraints. For example, if a product is waiting in stock it is recorded as NVA in the lean approach; nonetheless a certain amount of inventory is inevitable in any supply chain. For example, in this case study the total time at the meat cut operation was 15 178 s (or ∼ 253 minutes), of which only 49 s were value adding, in other words 0.3% VA time.

Subsequently, all PAM data were pulled together to create a current state map of the physical flows for the whole chain and then the information flows were added to generate the current state map as illustrated in Fig. 9.1 (map of loin). The current state map shows the physical flows, the information flows, total lead time and percentage value adding time. In this case it shows that the total lead time for loin is 276 days and 11 hours, of which 233 days are spent at the farm (animal breeding and rearing). So lead time excluding the time at the farm is 43 days and 11 hours, whereas the value adding time was just less than 25 hours (i.e. 24 hours cooling at the meat packer, 15 minutes value adding operation in slaughter and cutting, 15 minutes value adding operation in the distribution centre and 20 minutes value adding time during cooking in the canteen’s kitchen), that is 2.4% of the total lead time excluding time spent in animal rearing.

Fig. 9.1 Current state map for pork loins from farm to canteen.

The analysis of the chain identified several opportunities for improvement ranging from quick fixes to long-term changes. Value stream mapping often leads to the exposure of several ‘low hanging fruits’ which are in most cases dealt with during the course of the project. During the future state mapping sessions, a full list of all opportunities were generated and they were ranked through discussion and consensus. Discussions centered on identifying the cost/benefit of implementing each improvement opportunity. The team identified the following five key opportunities to be taken forward:

1. Review of the product specifications: the product specifications were not revised since established in 1963 and were by and large outdated.

2. Setting up electronic data interchange (EDI) between the food service company and the public sector organization. A telesales system was in operation with 20 staff dedicated to the telesales department at the food service company.

3. Backhaul opportunities between the supplier and the distribution company: both the processor and the food service company operated their own fleet. The team established that, in addition to this value stream, plenty more opportunities existed for backhaul to and from the central warehouse through better planning with various suppliers.

4. In-house improvement opportunities at the processing plant (such as an improved layout, work balance and packing equipment performance).

5. Work standardization at the farm: for example, reducing the variance in the performance of stockmen. Historic records showed that the skilled stockmen achieved a piglet mortality rate four times lower than the poor stockmen. It was endeavoured to standardize the skilled stockmens’ operations when training new staff.

Generating a future state map

Following analysis of the current state map, the team worked towards generating a collective vision of a supply chain that operates as an integrated entity focusing on the enhancement of the supply chain value proposition and elimination of all non-value adding activities. The shared point amongst all team members was the satisfaction of the end consumer. Also, as already mentioned, consumer value is the first principle of lean thinking. The team members brainstormed various attributes of the consumer value, categorized them and related them to a set of supply chain key performance indicators (KPI) as illustrated in Table 9.6. Through consensus, five factors were identified as the key constituents of value, reflecting the requirements set by the public sector organization, the chefs and the actual consumers. The brainstorm session was a rudimentary way of capturing and discussing ‘voice of customer’. The outcome of the discussion session was largely influenced by and depended on the public organization sharing their knowledge of the consumer needs acquired through focus groups and direct contact with chefs.

Table 9.6

Translating consumer value into supply chain KPIs

Consumer value Supply chain KPIs
Cost efficient distribution Total cost to serve for the whole job
Quality and consistency Produce to exact product specifications as required by the final customer
Value for money Top 40 products’ buying effectiveness (examined by independent third party company)
Delivery on-time/in-full 99.7% right quantity delivered (substitution allowed)
97% perfect order (measured by comparing credit notes with invoices – no substitutions)
Strategic reserve 21 days to feed

At this stage it was obvious that, even though the foodservice company and the public sector organization were separately measuring and analysing consumer satisfaction, they had never coherently linked together the requirements of the consumers, the product features and the supply chain activities. The future state workshop provided the opportunity to link the three together for the first time. Altogether, a lack of consistent understanding about the consumer requirements was observed suggesting that, from a supply chain effectiveness perspective, the chain was in a state of ‘unconscious incompetence’.

In contrast, the efficiency levels along the supply chain were good. For example, the distribution service achieved 99.7% lines delivered in full against lines ordered (substitutions permitted). In the abattoir, a state of the art slaughter line was observed, which had excellent ergonomics leading to better consistency in quality and the first Autofom application in the UK that ultrasonically scanned each carcass immediately after slaughter. A three-dimensional picture was built up of the muscle and fat present, which allowed accurate payment to the producer for the actual meat delivered, when fully implemented.

Table 9.6 shows different aspects of value in the ranking of importance as agreed upon by the team members. The team’s perception was that the most important feature of value was cost efficient distribution. This was linked into an overall measure for the total cost of delivering the pork product. There was a cost-plus contract in operation between the food service company and the public sector organization whereupon any saving in the cost of distribution was to be shared equally between the two parties. The team’s perception was that the aim of the VCA project is to deliver cost savings and an obvious area for cost saving – which could be equitably shared – was the distribution cost. The second most important facet of value was the quality and consistency of the product which already was being measured through rigorous methods such as customer direct feedback to the suppliers and random quality checks. The evidence acquired from the end customer suggested that the supply chain consistently met the specifications and the quality criteria. However, this was later proved to be wrong owing to the lack of understanding of consumer needs. The third attribute of the consumer value was the cost efficient purchase of the raw material. This was being measured through independent third-party monitoring of the procurement of the top 40 products (which included the pork lines). The fourth aspect of the consumer value was on-time/in-full (OTIF) delivery into the canteen. The food service company achieved 97% OTIF (calculated by checking the credit notes against the invoices). The measure was closer to 99.7% when substitutions were taken into account (the contract allowed for substitutions within reason). Last but not least, the end customer required a strategic reserve of at least 21 days stock to be kept in the distribution pipeline at anytime (i.e. inventory anywhere between the distribution company’s warehouse and the canteen). It was not clear whether this was actually needed or just a legacy of past systems. However, keeping a strategic reserve was not a big issue since the products were delivered via a frozen chain. Then again, a chilled chain would have meant supply of cheaper, tastier and fresher produce.

The key improvement opportunities and the issues related to consumer value have so far been discussed in this case study. Moreover, value attributes were related to a set of supply chain KPIs. In order to understand the extent to which the key projects deliver against the supply chain objectives, a sanity check against the supply chain KPIs was carried out during the future state workshop.

In Fig. 9.2, each box is scored on a scale of 0–3 where 0 denotes no impact on the relevant KPI and 3 shows very high impact. As illustrated, implementation of the EDI link and product specification review equally had the highest impacts against the supply chain KPIs. The implementation of EDI could result in significant efficiency gains estimated at around £400 000 per year. Nevertheless, it required relatively large capital investment and hence the need for a long lasting cost/benefit sharing agreement between the food service and the public sector organizations. The two companies could not reach an agreement mainly because the remaining length of the contract did not cover the pay back period for the required investment. On the other hand, the review of product specifications required zero investment while potentially improving both effectiveness and efficiency of the chain. The following explains how chain effectiveness was improved and what efficiency gains were obtained as a result.

Fig. 9.2 Key improvement projects impact against supply chain key performance indicators (Scale: 0–3)

The future state discussions revealed that the food service company and the public sector procurement organization were both active in understanding the consumer needs through focus groups. Moreover, the processor and its suppliers took great care to produce to the correct specification. Even though the product was reasonably priced, had good quality and was delivered 99.7% in right quantity (allowing for substitutions), it did not match the customer attributes. The supply chain analysis connected all aspects of the supply chain together and revealed that the product specifications were outdated and did not reflect the true consumer needs. The team identified opportunities for changing the product attributes and supply chain activities as described below:

Boneless loin – The current state map showed that the loin was being supplied with bone. In the kitchen the product needed to be boned out and the disposal of the bone also incurred additional cost. The impact of aligning the chain to the consumer value in this case were twofold:

1. Effectiveness gains: a questionnaire was sent out by the public sector organization following the team’s suggestion to see whether delivering boneless loin is aligned to the customers’ preference. One limitation of the study was in consulting the chefs rather than the actual end consumers about their preferences. Moreover, the telephone interview did not ask exactly why chefs liked or disliked the boneless product; nor did it follow-up when the answer was not specified. This issue increases the possibility of type II error in analysing the results of the questionnaire. Answers were received from 23 chefs responsible for fairly similar size canteens supplied from the same source and through similar channels. In all cases the public sector organization had followed up by telephone and in few cases had even obtained the results through telephone interviews.

    The results showed that the final customer preferred the boneless product since there was no need for boning in the kitchen. Of the 23 respondents, 14 preferred boneless loin, three were indifferent and six said no, that is 61% in favour, 14% indifferent and 25% against boneless loin. The statistical question is whether the proportion of the ‘yes’ answers is significantly higher than would be expected by chance. To find an answer to this question, the researcher used the binomial distribution to identify the probability of finding six or less negative answers in a sample of 20 when the random probability of a ‘no’ in each trial is 50%. The probability of six or less respondents disliking the boneless product from the 20 respondents is 0.057, that is B (20, 6, 0.5) = 0.057. It must be noted that three respondents did not specify their preference and therefore their data were regarded as meaningless. Assuming an alpha level of 0.05, it can be (marginally) concluded that the customers preferred boneless loin.

2. Efficiency gains: realignment of the supply chain with the consumer value (i.e. boning at the cut and pack stage and delivering boneless loin) leads to a number of efficiency improvements. First, the boning operation was more time consuming and labour intensive when carried out at the canteen as opposed to being done at the processor on an industrial scale. The processor produced boneless loin for other customers and could batch products together. Second, there was a small residual value to the bone at the processor. Third, there were logistical savings to be made along the chain. Four bone-in loins were fitted in a box compared with six boneless products after the modification. Therefore 33% fewer boxes and delivery pallets were needed which amounted to 96 full pallet deliveries saved in a single year. Total potential savings were around 1.75% of the final price delivered to the canteen. When potential savings are repeated over time and in a range of products, at some point there may be the potential to redeploy resources to other activities. This is the continuous improvement principle of lean thinking (Womack and Jones, 1996). Total immediate savings were at least 0.51%, equal to about 1% profitability on sales against a backdrop of only 3–5% average chain profitability across the whole red meat sector. The savings relating to labour in the canteen were partly offset by the extra labour required at the processing end to bone-out loins; however this is not included in the above calculations owing to a lack of data.

Case study discussions

There is a tendency for food supply chain improvement efforts to focus solely on the efficiency factors. This chapter shed light on the great need to address consumer value in the context of supply chain management by explaining how and why the above supply chain was disconnected from consumer needs although being reasonably efficient. For example, pork products were being delivered 99.7% correctly; yet a huge amount of waste existed since the product specifications had essentially not been revisited since it was established in 1963. In other words the supply chain was delivering the wrong product (bone-in loins) 99.7% on-time/ in-full.

The above case study shows that efficiency measurement and improvements per se fall short of meeting the consumer requirements. The value chain improvement exercise threw-up many improvement opportunities; the team opted for the ‘review of product specification’ which delivered both supply chain effectiveness and efficiency improvements while requiring almost nil investment. On the other hand, opting for efficiency improvements such as the ‘implementation of EDI’ would have required hefty capital investment while not necessarily securing consumer satisfaction since the same out-of-specification product would have been delivered. One limitation of the study is that the real requirements of consumers were not captured, that is, only a post events questionnaire was sent to chefs and the actual consumers were not surveyed. As explained in Section 9.2, it is imperative for the industry to reconnect with consumer values, to realign processes to deliver the basic requirements and to find ways to enhance consumer value beyond the basic needs at different stages along the value chain.

9.4 Conclusions

This chapter has provided a practical step-by-step guide for implementing a successful value chain analysis project. It also reported on the generic findings of the VCA project in the UK food industry and explained that (consumer) value and system effectiveness should be the starting premise of value chain improvement endeavours. Subsequently, the case study provided insight into the practicalities of the proposed method and the challenges ahead.

In the case study, different participants in the chain had differing opinions of what was meant by value leading to conflicting behaviour and poor overall delivery of value for the end consumers. Also the role of SCM was perceived as limited to delivering operational/logistical services only (i.e. quality, cost and delivery). This perception was countered by obtaining consumer information (capturing VoC) and by steering the supply chain improvement initiative towards greater supply chain effectiveness. Moreover, the case study showed how inter-organizational potential can be leveraged to improve overall supply chain consumer satisfaction.

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