Bottani, balanced scorecard for food supply chain

Performance measurement in the food supply chain: a balanced scorecard approach Barbara Bigliardi and Eleonora Bottani Department of Industrial Engineering – University of Parma, Parma, Italy Abstract Purpose – The primary objective of this paper is to develop a balanced scorecard (BSC) model that is designed and delimited for performance measurement in the food supply chain. Design/methodology/approach – The research methodology is a combination of literature analysis, Delphi technique and case study-based research. Starting from the literature concerning performance measurement and metrics, the food industry and the BSC model, the relevant financial and non-financial indicators, suitable to be used for companies belonging to the food industry, were first identified. Indicators were submitted to a panel of experts, which operated following the Delphi technique, to gather possible suggestions or amendments. In its final form, the resulting BSC model was tested on two companies operating in the food industry, for a final validation. Findings – Results of the case studies show that the companies examined have a similar view for three of the four perspectives of the BSC, which can be thus considered as validated. Conversely, some diverging results were observed concerning the learning and growth perspective. Research limitations/implications – By focusing on the specific context of the food supply chain, this study completes previous works, which proposed a generic BSC model for supply chain management. On the other hand, the fact that a specific industry field was examined could be seen as a limitation of the work as the results presented are not suitable to be generalized or extended to other contexts, although some extrapolations can be made. Originality/value – The paper provides a structured performance measurement system tailored for the food supply chain. The BSC model developed could serve as a reference for the food industry, to establish applicable performance appraisal indicators, and it is believed that both researchers and practitioners would benefit from the tool developed. Keywords Balanced scorecard, Supply chain management, Performance management, Food industry Paper type Research paper 1. Introduction Supply chain management (SCM) describes the discipline of optimising the delivery of goods, services and related information from supplier to customer. It is concerned with the effectiveness of dealing with final customer’s demand by the parties engaged in the provision of the product as a whole (Cooper et al., 1997). A supply chain consists of different levels, namely supplier, manufacturer, distributor, and consumer, and it is a network of companies which influence each other and affect one another’s performance. Hence, an important issue in SCM is the development of integrated performance measurement systems (PMS). PMS serve different functions in supply chain and operations management. These are formal devices to control, formulate and communicate the company’s strategy, and, as such, they primarily serve higher-level managers. But PMS can also support operational managers, to motivate and enable them to improve operations. A performance measurement framework assists in the process of performance measures building, by The current issue and full text archive of this journal is available at www.emeraldinsight.com/0263-2772.htm PM in the food supply chain 249 Facilities Vol. 28 No. 5/6, 2010 pp. 249-260 q Emerald Group Publishing Limited 0263-2772 DOI 10.1108/02632771011031493 clarifying measurement boundaries, specifying performance measurement dimensions or views and may also provide initial intuitions into relationships among the dimensions (Rouse and Putterill, 2003; Chan, 2003). On the basis of the above considerations, this study aims at developing a PMS, based on the well-known balanced scorecard (BSC) framework, suitable to be implemented in the food supply chain. The reason for choosing this context is that the supply chain of food products has received a great deal of attention in the last decade, due to issues related to public health. It has become apparent that in the near future the design and operation of food supply chains will be subject to more stringent regulations and closer monitoring, in particular those for products destined for human consumption. This implies that the traditional supply chain practices and the corresponding performance measurement should be subject to revision and change (Ahumada and Villalobos, 2009). As a further reason, the local industry of Parma region (where our study was based) mainly encompasses companies directly operating in the food industry, or related to this field, and offering a wide variety of products both in the areas of first- and second-stage processing and all featuring a noteworthy specialization in production techniques and high-quality finished products. Not by accident the province of Parma is known as Italy’s “Food Valley”. As an indication of the importance, it should be pointed out that the food industry represents the 35 per cent of the total of industrial sectors, reaching a turnover of e6,500 million out of a total of e19,200 million (Unione Parmense degli Industriali, 2007). Taken overall, food manufacturing also has a significant impact on the overall Italian production. The “Parma Alimentare” consortium is active in promoting this sector both within Italy and abroad, because of the significant number of well-known “typical” and “designation of origin” products. This sector has grown in recent years thanks to major investments in research and new technologies and careful attention to safety and quality, two fundamental aspects to food production in the province. Pertinent to this context is the establishment in Parma of the European Food Safety Authority, the major European agency that deals with identifying and evaluating potential risks within the food chain, from production to sale to the consumer. 2. Literature analysis Over the years, many methods and techniques have been suggested to evaluate the performance of a firm in general; well-known financial measures such as return on investment (ROI), internal rate of return, net present value and payback period have been the most studied in literature. At present, research in the field of supply chain performance measurement is receiving increasing attention by the scientific community, due to the need of developing integrated PMS, taking into account all the partners (i.e. the immediate supply network as well as the total supply network) with which a company interacts. Comprehensive studies describing both quantitative and qualitative performance metrics for SCM are provided by Chan (2003), Gunasekaran et al. (2004) and Bhagwat and Sharma (2007). Based on those recent works, a summary of the performance indicators suitable to be used for SCM is presented in Table I. Unfortunately, several authors agree that currently available PMSs suffer from two main limitations. First, often performance measurement follows financial accounting principles without any forward-looking perspective and measurement is restricted to directly measurable oraccountable indicators (Horvath, 1996). Suchsystems arenot fully F 28,5/6 250 suitable to be implemented in modern SCM applications, where a performance measurement system has to take into account a wider range of controlling targets (Bhagwat and Sharma, 2007). Specifically, complex supply chains seek to provide customers with a wide range of benefits, including intangible ones. Furthermore, most of these systemslack of system thinking, in which a supply chain must be viewed as a whole entity and the measurement system should span the entire supply chain (Chan, 2003). The BSC first appeared in the results of a research developed in 1990 by Kaplan and Norton (1992), involving many companies, moved by the growing dissatisfaction with traditional financial measures as a sole (or main) measure for company’s performance. The BSC is a tool for aligning business activities to the vision and strategy of the organization, improving internal and external communications, and monitoring organization performance against strategic goals. It includes various performance indicators, namely customer perspective, internal-business processes, learning and growth and financials (Kaplan and Norton, 1993, 1996, 2001a, b). Given its structure, the BSC can be appropriate to be implemented as a tool for measuring and evaluating supply chain performance. The BSC distinguishes four different perspectives of performance measures: (1) Customer. Recent management philosophy has shown an increasing realization of the importance of customer focus and customer satisfaction in any business. These are leading indicators: if customers are not satisfied, they will eventually find other suppliers that will meet their needs. Poor performance from this perspective is thus a leading indicator of future decline, even though the current Supply chain process Performance measures Plan Order entry method (Gunasekaran et al., 2004) Order lead-time (Christopher, 1992) Customer order path Source Supplier selection Buyer-supplier relationship Manufacturing Product cost, quality, speed of delivery, delivery reliability, flexibility (Mapes et al., 1997; Slack et al., 1995) Range of product and services (Mapes et al., 1997); Capacity utilization (Slack et al., 1995) Effectiveness of scheduling techniques (Little et al., 1995 Delivery Delivery performance (Stewart, 1995) Number of faultless notes invoiced; flexibility of delivery systems to meet particular customer needs (Novich, 1990) Total distribution cost (Thomas and Griffin, 1996) Customer Product development cycle time; machine/toolset up time; economies of scope (Christopher, 1992) Number of inventory turns; customer query time Post transaction measures of customer service Overall chain Total supply chain costs (Cavinato, 1992) Total cash flow time ROI Total cost of inventory (Stewart, 1995; Christopher, 1992; Slack et al., 1995; Lee and Billington, 1992; Levy, 1997) Information processing cost (Stewart, 1995) Table I. A list of performance measurement for supply chain management PM in the food supply chain 251 financial picture may look good. In developing metrics for satisfaction, customers should be analysed in terms of kinds of customers and kinds of processes for which we are providing a product or service to those customer groups. (2) Internal processes. This perspective refers to internal business processes, and aims at satisfying shareholders and customers by excelling at some business processes. Metrics based on this perspective allow the managers to know how well their business is running, and whether its products/services conform to customer requirements. (3) Learning and growth. Its main objective is to determine the infrastructure that allows reaching the objectives of the other three perspectives, in order to create a long-term growth of the company. This perspective includes employee training and corporate cultural attitudes related to both individual and corporate self-improvement. (4) Financial. This perspective reflects the traditional need for financial data. Timely and accurate funding data will always be a priority, and managers will do whatever necessary to provide it. In fact, often there is more than enough handling and processing of financial data. With the implementation of a corporate database, it is hoped that more of the processing can be centralized and automated. But the point is that the emphasis on financials leads to the “unbalanced” situation with regard to other perspectives. Hence, additional financial-related data, such as risk assessment and cost-benefit data, are often included in this category. Figure 1 shows a scheme of the BSC model originally proposed by Kaplan and Norton (1996). The BSC has been widely investigated in literature, but little attention has been paid by researchers to its adoption in the food industry: among these, it is possible to cite Cardemil-Katuranic and Shadbolt (2006), which adopted a case study methodology to research how an agricultural (kiwifruit) co-operative in New Zealand could implement a Balanced Scorecard. However, to our knowledge, no specific studies address the development of a BSC model for the food supply chain. Moreover, in the context of SCM, there are only few applications of BSC for performance measurement. Among these, Brewer and Sfeh (2000) introduced a modified version of the BSC that can be used for measuring the performance of a supply chain, and provided examples of possible measures. Bullinger et al. (2002) designed an adapted supply chain performance analysis approach based on the BSC model. Their study aims at supporting logistics organizations in deriving specific opportunities from the potentials of the SCM. A similar study has been performed by Park et al. (2005). More recently, Bhagwat and Sharma (2007) developed a BSC for SCM, that measures and evaluates day-to-day business operations following four perspectives: finance, customer, internal business process, and learning and growth. In this paper, we start from the work by Bhagwat and Sharma (2007) to develop a BSC framework for performance measurement, tailored for the food supply chain. 3. The research methodology The research methodology followed in this study consists of three steps. First, a detailed literature analysis, concerning SCM, performance measurement and food F 28,5/6 252 supply chains was performed. The aim of the literature review was to analyse the currently available PMS for supply chains, as well as to punctually examine specific issues of the food supply chains, to identify key performance indicators (KPIs) to be used in the industry investigated. In particular, the four dimensions of the BSC proposed by Bhagwat and Sharma (2007) has been the starting point of our study. As a result of this step, a set of KPIs suitable to be adopted in the context of food companies emerged. Such indicators were used as a guideline during the second phase of the research, where they were examined and validated by an appropriate panel of experts, which operated according to the Delphi technique (Linstone and Turoff, 1975). The Delphi technique is a systematic, interactive forecasting method, which allows obtaining forecasts from an independent panel of experts, over two or more rounds. Normally, an administrator provides an anonymous summary of the experts’ forecasts and their reasons for them after each round. The process stops when experts’ forecasts change slightly between rounds, and final round forecasts are combined by averaging (Rowe and Wright, 1999). We adopted the Delphi technique with the aim of obtaining a high degree of consensus on the KPIs to be included in our model. An appropriate multidisciplinary panel of about 20 experts was set up to this purpose. The panel encompassed few academics from the Industrial Engineering Department of the University of Parma, chosen among people whose research studies were mainly focused on SCM, performance measurement and food industry issues. Figure 1. The balanced scorecard model PM in the food supply chain 253 Moreover, 15 managers from a major Italian company operating in the food industry were included in the panel. Panel members were selected among people reporting directly to the firm’s top management and operating in SCM, logistics, procurement, production, information technology, planning and control and finance. KPIs resulting from the literature were structured into an appropriate questionnaire, submitted to the panel members. Then, a two-round Delphi was carried out to refine the proposed indicators. In the first round, the panel members were asked to express their agreement with regard to the suitability of each KPI to be adopted in the food industry. Moreover, panellists could indicate the need for further specifications of KPIs (if required), as well as the main strengths and weaknesses of each indicator identified. The results of the first round of Delphi led to several modifications to the list of KPIs originally proposed. Hence, a second questionnaire was organized, incorporating additional indicators proposed by the panellists and removing non-relevant ones, and submitted to the panel members during the second round of Delphi. Again, panellists were asked to refine each indicator emerged in the first round, as well as to identify additional indicators suitable to be implemented in the food industry. A general agreement was reached at the end of the second round. Then, the panel members were involved in a final roundtable discussion, to confirm the agreements on the results of the second questionnaire. The final result of the Delphi technique is a BSC model for performance measurement in the food supply chain. During the third step of the research, the BSC model was tested on two companies, operating in the food industry, along with as many exploratory case studies (Yin, 1984). The case studies had the primary aim of validating the model developed, by providing empirical evidence of “theoretical assumptions” emerged during the Delphi rounds (Creswell, 1994). They were carried out with a series of semi-structured interviews, with the top management operating in the in SCM business functions of each company, over a three-week period during April and June 2009. Interviews lasted between one and two hours, and were taped and transcribed. A primary research question was formulated to explore the structure and characteristics of the company, in terms of the way each company measures its performance (i.e. whether the company adopted a specific performance measurement system or not). Then, we proposed the BSC model obtained from the previous steps, in order to validate its contents by means of a ranking method. Specifically, we asked the company’s managers to rate the indicators proposed in the model on a six-point scale (where 1 ¼ “not important at all” and 6 ¼ “extremely important”), as well as to prioritise each perspective by assigning a score ranging from 0 to 10. 4. The case studies 4.1 Companies description Company 1 (the names of both companies are omitted in the paper, due to confidentiality) was founded in 1966 in Parma, and is a consortium of 110 associated enterprises, working in the dairy, cheese and meat sectors. The dairy and cheese fields, in particular, represent the primary markets of the company. In such contexts, the company has gained a relevant market share, as a producer of top quality milk and excellent national cheeses, with protected designation of origin. The company is one of the first Italian producers of butter, as well as in leading position as manufacturer and distributor of local cheeses in Italy and Europe. Company 1 also operates as a manufacturer of pork meat products; in this sector, it slaughters 15 per cent of the total F 28,5/6 254 number of animals processed in northern Italy and intended for the local meat market. The company’s main customers include large companies operating in food and fast moving consumer goods retailing. In 2008, the company counts about 1,030 employees, distributed on 17 production sites located in Italy. Additional relevant figures of the company for 2008 are an aggregate turnover of about e620 million, 12 per cent of which generated by exportations in foreign countries. Company 2 was established in 1866 and at the beginning operated in Switzerland, as a milk producer. Since the first years, the company has expanded its activity in other countries, namely US, UK, Spain, Germany and Italy. Over the last two decades, the company experiences a period of strong growth, that translates into the acquisition of new enterprises, operating as manufacturers of ice cream, beverages, dietary foods, and, more recently, health and beauty care products. The company currently has manufacturing sites in more than 80 countries, and covers a leading position in the food industry in the US market. In Italy, Company 2 has reached e1,500 million annual turnover in 2008, and is considered as one of the most important companies operating in the food industry. The company manufactures more than 3,900 food products, which are commercialised under 70 well-known brands. Company 2 also involves about 3,300 employees, most of whom operate in the company’s headquarter in northern Italy. For the purpose of this study, we have considered a subsidiary of Company 2, established in Parma in 1904 and specialised in the manufacturing of chocolate, bakery, desserts and cakes. 4.2 Balanced scorecard model for Company 1 and Company 2 During the case studies, the indicators included in the general model of BSC were ranked on the basis of the opinion of executives and managers collected during the interviews. Subsequent reflections on these meetings provided a useful form of content and context analysis and was helpful in linking specific scorecard issues with remarks made by managers. For Company 1, the financial and customer perspectives are perceived to be the most important ones, followed by internal processes and learning and growth perspectives. As for the financial perspective, managers judged the indicators “Information carrying cost” and “Supplier cost saving activities” as the most important, followed by “Variations against budget” and “Cost per operation hour”. The typical financial indicator ROI was perceived to be less important, while the “Net profit vs productivity ratio” turned out to be not important at all. As far as the customer perspective is concerned, the indicators perceived as most relevant are “Customer query time”, “Order lead time”, “Distribution lead time”, “Distribution performance”, “Delivery reliability”, “Effectiveness of Distribution Planning Schedule”, and “Quality of delivered goods”. The remaining indicators assumed a rank variable from 4 to 5, and were then considered in the final BSC model. As far as the internal processes perspective is concerned, “Purchase order cycle time”, “Effectiveness of Master Production Schedule”, “Supplier rejection rate”, “Total inventory cost”, and “Frequency of delivery” are perceived as the most important indicators. On the contrary, “Supplier lead times against industry norms” and “Product development cycle time” assumed low importance; consequently, these indicators were excluded from the final BSC model. Finally, the most important indicators belonging to the learning and growth perspective are all related to the collaboration with supply chain players, that is “Supplier assistance in solving technical problems”, “Supplier ability to respond to quality problems” and “Buyer-supplier collaboration in problem solving”. “Order entry PM in the food supply chain 255 methods” and “Level of information sharing” are, on the contrary, perceived as the less important indicators. As such, they were not included in the final model. On the basis of the considerations described above, the BSC model depicted in Figure 2 was derived. As far as Company 2 is concerned, its perspectives and indicators evaluation is quite similar to the one from Company 1, providing validation of most of the KPIs proposed. Customer perspective is perceived to be the most important one, at the same level of the internal processes perspective. As regards to the indicators, managers suggested that “Supplier cost saving activities” is the most important one when considering the financial perspective, followed by “Variations against budget” and “ROI”. Also for Company 2, the “Net profit vs. productivity ratio” resulted to be not important at all. As far as the customer perspective is concerned, all the indicators proposed were perceived as important, assuming a rank variable from 5 to 6, and were then considered in the final BSC model. As far as the internal processes perspective is concerned, “Accuracy of forecasting techniques”, “Purchase order cycle time”, “Planned process cycle time”, and “Total inventory cost” are perceived as the most important indicators; conversely, “Total cash flow time” and “Supplier rejection rate” assumed a low importance, and were thus excluded from the final BSC model. Finally, we obtained contrasting opinion with regard to the learning and growth perspective. In fact, for Company 2, the most important indicators are “Order entry methods” and “Level of information sharing”, that turned out to be of scarce importance for Company 1. Conversely, “Suppliers’ procedures of order collection” and “Suppliers involvement in company’s activities” were perceived as the less important indicators, together with “Supplier assistance in solving technical problems”. The BSC model for Company 2 is schematically depicted in Figure 3. 5. Conclusions The main result of the research is the development of a set of KPIs embodied into a BSC-based tool for performance measurement in the context of a food supply chain. The model developed has been validated by both a panel of experts operating in the food industry, and by means of two case studies, referring to as many food companies. As a result of the case studies, it emerged that the two companies interviewed perceived the BSC as composed by the four perspectives, those suggested by Kaplan and Norton, with a number of measures that vary from 2 to 10. Moreover, outcomes of the case studies provide validation of most of the KPIs proposed for the BSC model, which can be thus considered as suitable for use as a tool for performance measurement in the food supply chain. The learning and growth perspective represents an exception in this regard, as some diverging results were observed between the two companies. This is a current limitation of the study, and suggests the need of better investigating this perspective in future research activities. We recognize that ranking is not new in the food industry: many retailers, for example, use this approach in their assurance schemes, such as Leaf UK that use a score sheet in their audit for Leaf Marque, or the Global Good Agricultural Practice (formally known as EurepGAP) that utilises a scoring method as compliance criteria for farm assurance (Globalgap, 2009; Leaf UK, 2009). An additional limitation refers to the research methodology adopted, which grounds on a limited sample of case studies. Yin (1984) justifies the use of a single case study where a rare or unique event is explored, to probe the how and why questions in greater detail. Furthermore, the application of data from just one particular industry clearly reduces the number of F 28,5/6 256 Figure 2. The balanced scorecard model for Company 1 PM in the food supply chain 257 Figure 3. The balanced scorecard model for Company 2 F 28,5/6 258 [...]... test its suitability of adoption for food companies PM in the food supply chain 259 References Ahumada, O and Villalobos, J.R (2009), “Application of planning models in the agri -food supply chain: a review”, European Journal of Operational Research, Vol 195 No 1, pp 1-20 Bhagwat, R and Sharma, M.K (2007), “Performance measurement of supply chain management: a balanced scorecard approach”, Computers &... (2000), “Using the balanced scorecard to measure supply chain performance”, Journal of Business Logistics, Vol 21 No 1, pp 75-93 ` Bullinger, H.J., Kuehner, M and Van Hoof, A (2002), “Analysing supply chain performance using a balanced measurement method”, International Journal of Production Research, Vol 40 No 15, pp 3533-43 Cardemil-Katuranic, G and Shadbolt, N (2006), “The Balanced Scorecard as a spontaneous... value model for supply chain competitiveness”, Journal of Business Logistics, Vol 13 No 2, pp 285-91 Chan, F.T.S (2003), “Performance measurement in a supply chain , International Journal of Advanced Manufacturing Technology, Vol 21 No 7, pp 534-48 Christopher, M (1992), Logistics and Supply Chain Management, Pitman Publishing, London Cooper, M.C., Lambert, D.M and Pagh, J.D (1997), Supply chain management:... (2005), “A framework for designing the balanced supply chain scorecard , European Journal of Information Systems, Vol 14 No 4, pp 335-46 Rouse, P and Putterill, M (2003), “An integral framework for performance measurement”, Management Decision, Vol 41 No 8, pp 791-805 Rowe, G and Wright, G (1999), “The Delphi technique as a forecasting tool: issues and analysis”, International Journal of Forecasting, Vol... R.S and Norton, D.P (1993), “Putting the balanced scorecard to work”, Harvard Business Review, Vol 71 No 5, pp 134-47 Kaplan, R.S and Norton, D.P (1996), “Using the balanced scorecard as a strategic management system”, Harvard Business Review, Vol 74 No 1, pp 75-85 F 28,5/6 260 Kaplan, R.S and Norton, D.P (2001a), “Transforming the balanced scorecard from performance measurement to strategic management:... Harrison, A and Johnston, R (1995), Operations Management, Pitman Publishing, London Stewart, G (1995), Supply chain performance benchmarking study reveals keys to supply chain excellence”, Logistics Information Management, Vol 8 No 2, pp 38-44 Thomas, D.J and Griffin, P.M (1996), “Co-ordinated supply chain management”, European Journal of Operational Research, Vol 94 No 3, pp 1-15 Unione Parmense degli... Kaplan, R.S and Norton, D.P (2001b), “Transforming the balanced scorecard from performance measurement to strategic management: part 2”, Accounting Horizons, Vol 15 No 1, pp 147-60 Kraft, K (1990), “Are product and process innovations independent of each other?”, Applied Economics, Vol 22 No 8, pp 1029-38 Lee, H.L and Billington, C (1992), “Managing supply chain inventory: pitfalls and opportunities”,... international supply chain , Sloan Management Review, Vol 38 No 2, pp 94-102 Linstone, H.A and Turoff, M (1975), The Delphi Method – Techniques and Applications, Addison-Wesley, London Little, D., Kenworthy, J., Jarvis, P and Porter, K (1995), “Scheduling across the supply chain , Logistics Information Management, Vol 8 No 1, pp 42-8 Mapes, J., New, C and Szwejczewski, M (1997), “Performance trade-offs... J.D (1997), Supply chain management: more than a new name for logistics”, The International Journal of Logistics Management, Vol 8 No 1, pp 1-13 Creswell, J.W (1994), Research Design: Qualitative & Quantitative Approaches, Sage Publications, Thousand Oaks, CA Gunasekaran, A., Patel, C and McGaughey, R.E (2004), “A framework for supply chain performance measurement”, International Journal of Production... (Eds), Neue Organisationsformen im Unternehmen Ein Handbuch fur das moderne Management, Springer, Berlin, pp 937-9 Globalgap (2009), available at: www.globalgap.org/cms/front_content.php?idcat¼2 (accessed August 2009) Leaf UK (2009), available at: www.leafuk.org/leafuk/ (accessed August 2009) Kaplan, R.S and Norton, D.P (1992), “The balanced scorecard – measures that drive performance”, Harvard Business . on the well-known balanced scorecard (BSC) framework, suitable to be implemented in the food supply chain. The reason for choosing this context is that the supply chain of food products has received. the number of F 28,5/6 256 Figure 2. The balanced scorecard model for Company 1 PM in the food supply chain 257 Figure 3. The balanced scorecard model for Company 2 F 28,5/6 258 observations,. structured performance measurement system tailored for the food supply chain. The BSC model developed could serve as a reference for the food industry, to establish applicable performance appraisal