INSIGHTS ON HOW TO IMPROVE LOGISTICS PERFORMANCE FROM AN INTERACTIVE SIMULATION GAME OLIVER SCHNEIDER (1, MATTHIAS J. SCHNETZLER (2 Center for Enterprise Sciences (BWI), ETH Zurich, 8092 Zurich, Switzerland 1) oschneider@eth.ch 2) mschnetzler@eth.ch ABSTRACT The paper describes a comprehensive evaluation of experiences with a production simulation game, the Logistics Game, conducted over more than 10 years with students and practitioners at the ETH Zurich. The objective of the game is to give insights on how to improve logistics performance. After an introduction to the theoretical background, the game is described and results of 72 groups are discussed. In particular, the effects and the combinations of measures are investigated. The results show that improvement strategies that are in accordance with lean production, in particular with justin-time, enables best logistics performance. The game is suitable to get familiar with interrelations of logistics targets and measures in an intuitive way. INTRODUCTION In the search of continuously improve their competitiveness and performance, enterprises are trying to capitalize on improvement potentials such as opening up new markets, reducing costs and investments, since they are facing fierce competition as well as increasing complexity, risk and dynamics of markets and customer demands. Many of these improvement potentials are founded on logistics management, i.e. the effective and efficient management of day-to-day activity in producing the company s output. Logistics management and, similarly, operations management deals with the management of the flow of materials and information within an enterprise and between its suppliers and customers. The performance of a company is comprised of the achievement of objectives in the target areas of quality, delivery, flexibility, and costs (Schönsleben, 2004). Logistics has a significant influence on company objectives in all four target areas. The objective of the Logistics Game, an interactive simulation, is to establish awareness of the influence of logistics on the achievement of company objectives and the understanding of how to improve logistics performance. On a regular basis, the logistics game is played since 1993 with students at the department of management, technology, and economics at the ETH Zurich as an introduction to the lecture on logistics, operations, and supply chain management. Moreover, the game is often played with practitioners. The Logistics Game has been described by Schierholt and Brütsch (1998) with the focus on implementing a process-oriented supply chain. In the view of further experience gained with playing the game, this paper examines how the Logistics Game provides insights on improving the logistics performance in a sustainable way. In particular, two questions are evaluated: First, are certain improvement strategies more successful than others? Second, are principles of Lean Production applicable to the game? The paper is organized as follows: First, an introduction to the theoretical background of the Logistics Game is given. The target areas and the so-called Sand Cone Model are discussed, as well as strategies and measures for the improvement of logistics performance. Next, the Logistics Game is
described. After that, results of past years are presented and discussed as well as implications drawn. Finally, the conclusions of the paper will be derived. IMPROVING LOGISTICS PERFORMANCE: THEORETICAL BACKGROUND The target areas of logistics and the Sand Cone Model As mentioned in the introduction, logistics has a significant impact on the target areas. In general, the target areas are quality (meeting higher customer requirements), delivery reliability (punctuality, delivery reliability rate), delivery lead time (time to delivery, fill rate, availability), flexibility (coping with changes in demand and uncertainties), assets (inventory, infrastructure), and costs (information and materials flows, inventory management etc.) (Schönsleben, 2004; Schnetzler et al., 2006). This sequence of target areas is not arbitrary, it can be well supported through the Sand Cone Model of Ferdows and De Meyer (1990). First, to involve customers, a sufficient degree of quality must be assured, not too less or too much both would be waste. Once consistent quality is ensured, delivery reliability can be optimized. Insufficient quality would be a poor precondition. For example, defective parts or poor processes can induce interruptions in the production and, as a consequence, cause fluctuations and impair the punctuality. When a certain degree of reliability of processes is established (i.e., low fluctuations), lead times can be reduced and flexibility optimized. It would be inefficient to optimize processes, which are not well controlled, towards speed and flexibility since, for instance, short lead times can not be sufficiently guaranteed. In this way, preconditions are created for optimizing assets (inventory, infrastructure) and costs. Reducing them first can cause serious problems regarding quality, reliability, and flexibility. This sequence does not mean that, for instance, quality should a priori be given the highest priority, but rather that decisions, efforts, and the implementation of measures should be first for the target area of quality and so on. This sequence according to the Sand Cone Model is consistent with empirical work in literature (Ferdows and De Meyer, 1990; Filippini et al., 1998). As a consequence, strategies for improving logistics performance should follow the Sand Cone Model in order to be sustainable. The contribution of logistics to the success of a company In order to understand, how the achievement of these target areas contribute to the success of a company, the concept of economic value added (EVA) can be used. EVA is a measure that represents business success by qualifying the value added a company generates in an economical sense. In simplified form, it can be calculated as the difference between net operating profit after taxes (NOPAT) and capital charge, which is dependent upon total invested capital and weighted average cost of capital (WACC) (Ehrbar, 1998; Copeland et al., 2000). In other words, a positive EVA means that the profit of a company exceeds its total costs of capital for equity and debt. Such concepts of value-based management found broad acceptance and are highly popular. A good logistics performance contributes to a high EVA in the following way (Schnetzler et al., 2006): The achievement of customer requirements in terms of quality, delivery reliability, delivery lead time, and flexibility enables customer satisfaction and, thus, a high sales revenue. As a result, ceteri paribus, the net operating profit and, consequently, the EVA is increased. The net operating profit can also be increased by reducing operational costs through more efficient processes relating to the material and information flows as well as to management and stocking. Likewise, logistics has a positive impact on EVA by reducing the capital charge through lower inventory levels and less capital intensive logistics infrastructure (e.g. warehouses). Lean Production In order to identify adequate measures for the improvement of logistics performance, principles of Lean Production can be applied. The idea is to prevent any kind of non-value added operations, i.e. to eliminate any waste. There are several types of waste in business, manufacturing and logistics processes: overproduction, waiting, unnecessary transport, unnecessary movement, overprocessing or incorrect processing, excess inventory, and defects (Ohno, 1988; Liker, 2004). One pillar of Lean Production is just-in-time (JIT), which is aiming at reducing lead times and lowering work-in-process.
This means that, in a flow process, the right part or component arrives at the right point and right moment where and when it is used in the right amount, of course (Ohno, 1988). (The second pillar is the automation with a human touch.) Lean Production originates from Toyota (Toyota Production System) and is described in detail in Womack et al. (1990), Womack and Jones (2003), and Liker (2004). Regarding logistics, the most important methods and techniques of JIT are the following (Schönsleben, 2003): lead time reduction through setup time reduction and batch size reduction, production or manufacturing segmentation, cellular manufacturing, standardization of production infrastructure, flexible capacities, structuring assembly processes, complete processing as well as line balancing (harmonization of the content of work). The ultimate goal of just-in-time is to create an efficient and continuous process flow, that is, in the ideal case, an one-piece-flow (Liker, 2004). Beyond these measures, comprehensive concepts and measures are required: quality assurance, motivation and empowerment of employees, flexibility of employees, supplier development as well as collaboration with suppliers and customers, among others. Improving logistics performance Concluding, in order to improve the logistics performance in a sustainable way, the sequence of measures according to the Sand Cone Model has to be considered. Adequate means can be identified using the principles of lean manufacturing, in particular just-in-time. In order to implement just-intime, first, quality and reliability have to be ensured and employees have to be trained and empowered. In the following, the paper examines whether and how this can be simulated by the Logistics Game. DESCRIPTION OF THE LOGISTICS GAME The Logistics Game is already comprehensively described by Schierholt and Brütsch (1998), but there with the focus on how to use the game to promote process orientation and thinking to students and practitioners. However, in order to understand the results of this paper, the setup and procedure of the game are described again, with strong reference to the previously mentioned authors. Afterwards a detailed description of the link of the game with logistics improvement aspects is provided. Setup of the game and procedure The Logistics Game simulates the production process in a make-to-stock manufacturing environment of a company producing only one product. The initial layout of the factory is shown in Figure 1. Shipping WP 3 WP 1 Sales Rework Stock Customer Quality Control WP 2 WP 4 Direct Internal Transport External Transport Figure 1: Initial layout of the Logistics Game There are four workplaces needed to assemble the product. Every workplace consists of one table with a storage for incoming parts or modules, a storage for outgoing modules, and working instructions on how to perform his work. The product is made of toy bricks which can be stuck together in different ways. After the final assembly in workplace 4, the product is transferred to the
quality control. In the case of a wrongly assembled product, it is handed over to the rework, which has to correct the production errors and probably needs to rebuild parts or the complete product. If the quality control is satisfied, the product is moved to the finished goods stock at the shipping department, which is then responsible for fulfilling the customer orders and sending the products through the external carrier. The contact to the customer is maintained only through the sales department, which receives the orders directly from the customer, and through the external carrier, who delivers the ordered products. The external carrier also transports the internal delivery order forms from the sales department to the shipping department. All transports of parts, modules and final products from the stock via the workplaces to the quality control are performed by the internal transport service. The number of employees that are directly involved in the production and order process adds up to 12: four workers, two internal transport persons, plus each one quality controller, reworker, sales person, shipping responsible, stock manager (inventory of raw materials) and external carrier. Besides these employees, there are two non-productive persons involved to observe the manufacturing process and work out suggestions for improvements: the general manager and the production engineer. The moderator of the game plays the role of the customer. In regular intervals, he orders a given number of products. The production company does not know in advance, how many parts it would have to deliver. When a delivery arrives, the customer checks the products and either accepts or rejects them, depending on their quality. Summarizing, the production structure at the beginning of the game shows a very old-fashioned, tayloristic production scheme (job shop, division of labor). The customer contact is only used for determining the necessary quantity of products to be shipped. Customer information does not provide any input to the production planning process itself. This push-system produces independently of customer orders and the finished goods stock inventory level. This situation is worsened by an unorganized layout and by batch sizes that are not harmonized between the workplaces. It is assumed that very specialized workers perform the tasks. They only have instructions to perform their own work. Mistakes can only be corrected by the reworking. Communication between the workers is kept at a minimum, only the communication between worker and the internal transport service is allowed. Playing the game The game simulates the production over 3-4 periods. In every period, a number of 10-12 orders are passed from the customer to the sales department in one minute intervals. The players do not know in advance how many orders he will receive during a period. The number of ordered products varies and is usually kept quite low in the first game period to allow the production to start up. Products have to be delivered 30 seconds after the order, late shipments are not accepted. Delivered products are then checked on their correctness. The difference between the number of ordered products and the number of good products is added as backorder to the next order. When the period is over, the production is stopped and the present state of the production is evaluated with the several performance measurement indicators. One indicator is the production lead time, which is measured by giving a marked part into the production from stock and counting the time until the product with the marked part arrives at the customer. The other indicators focus on costs. The fixed costs consist of the personnel costs (100 units per productive person) and the costs of unfinished and finished goods currently in production or on stock (5 units per part at the workplaces, 20 units for modules and 50 units for finished goods in quality control or on stock; raw material stock is not included). The fixed costs are almost independent on the number of ordered products in a given time period. The variable costs then describe quality and delivery ability aspects. Each rejected product is counted with 100 cost units (penalty), whereas each ordered product not delivered counts for 50 cost units per period of late delivery. Together with the players the moderator of the game calculates the total costs and the costs per good product. Usually these numbers are not very good after the first period, but now the players get the chance to think of improvement measures. In a fixed timeframe they discuss their experiences of the last period. The goal of the workshop is to find improvement potential in the production process and measures for improving those. These actions can range from changing the batch size or the layout
to dismissing employees or introducing new organizational concepts such as group work. The players do not have a catalogue of possible actions; they are almost absolutely free on what to change. The only things that may not be changed are the product itself, the market structure (introducing more customers), and decisions on whether or not to outsource parts of the production. Due to a limited budget for restructuring measures, they are allowed to implement two measures after each period played. In the following, the typically chosen measures will be shortly introduced and their effects will be discussed. Improvement measures Although the vast majority of participants of the Logistics Game had no structured introduction into the area of logistics and according improvement strategies before playing the game, there are only a few different measures to be identified, which were agreed on by the groups. Figure 2 shows the list with numbers of measures, which were taken by 72 groups playing the game between 2001 and 2005. 6% 4% 2% 21% 7% 36% Training Layout Reduce Batch Size Eliminate Internal Transport Dismissal of Employees Quality Training Prognoses 24% Figure 2: "Hit list" of chosen improvement measures (72 groups) The mostly taken measure is the training of personnel, which in the game counts as a half measure per employee. Training means that personnel learn how to work in another role, resulting in double occupation of capacity places. This is often combined with an elimination of a role, e.g. because an internal transport employee learns to work on a production workplace. This measure is mostly taken in order to cope with bottlenecks in production, or to avoid unnecessary steps in the quality assurance and rework area. The next widely chosen measure is a change of the production layout. There were only two groups not changing the layout, and one taking this measure two times. Considering the complex and nontransparent initial layout, this is not surprising. Interesting is though, that intuitively the new layouts were very much oriented at the flow of materials, including a move of the shipping department close to the customer. Figure 3 shows an exemplary layout, where also the internal transport was eliminated, so that the only real transport is from the shipping department to the customer. Stock WP 2 WP 1 WP 3 WP 4 Quality/ Rework Shipping Sales Customer External transport Direct transfer Figure 3: Exemplary final layout of one group With the new layouts usually the groups also significantly improve punctuality of their deliveries and reduce their lead times.
Another measure taken by a great majority of the groups is the reduction and harmonization of the batch sizes, in most cases down to one part. Having this low batch size allows for a way more flexible production and a more harmonized flow of materials (one-piece-flow), especially to the finished goods stock. It is clear that in a real business environment the last two mentioned measures in most cases are not easy to implement. A change of production layout results in investments in the facility and its assets. Probably the different work places are not even located at the same ground. A reduction of the batch size also is to be considered well, especially when machines are used for several different products, resulting in set-up times needed with every batch of a different kind. Therefore, set-up times have to be reduced. However, in this gaming environment these measures are perfectly fine, also considering the goal of the game to promote process orientation and thinking, resulting in a reduction of waste in terms of waiting times and not streamlined transportation ways. The last discussed aspect is also true for the next measure chosen by some groups, the elimination of the internal transport service. In a real business environment this can only be done when it is possible to install an assembly line with an automatic transport. In the game people can just move or even throw their part to next station, so this measure is easy to implement, at least when the group has changed their layout according to the flow of materials. Again, the goal of this educational game is reached, as the group is sensitized to the need of reducing waste in terms of unnecessary transportation resulting in a shorter lead time. When the groups think that they have their production processes under control and are easily able to meet the customer orders, they start thinking on how to reduce their fixed costs, namely the personnel costs. So they dismiss workers they think they do not need anymore, in most cases these are the people initially coming from the internal transport service. The learning curve of the players regarding the correctness of the product usually is very steep, so that already during the first period the workers know quite well how to stick together the parts and modules at their workplace. So there is a real training-on-the-job. But sometimes a group does only deliver some parts correct by chance, some even no correct product at all during the first period. So some of the groups choose the improvement measure to perform a quality training for the production workers, right after the first period. This leads to a more reliable production process, less rework is needed and fewer defective products are delivered to the customer. Hence, the choice of this measure leads to a reduction of waste in terms of less work on the same product and higher customer satisfaction. The last measure to discuss is the choice of prognoses about the customer order sizes, either on the basis of the full period or every round of the next period. The idea behind this is to control the production process such that not too few and not too many goods are in the production process or even in the finished goods stock at the same time, because these numbers at the end go directly into the yearly performance indicators. In the language of Lean Production both cases, having too few or too many goods in the production pipeline, are considered as waste. In order to be able to make statements about how the described measures improve logistics performance, it has to be understood, which target areas of logistics are impacted by the individual measures. Based on the detailed descriptions, Table 1 provides an overview on the typically chosen measures and the impacted primary target areas. Measure / Target Area Quality Delivery Delivery Lead Assets and Reliability Time/ Flexibility Costs Training (X) X (X) Layout change X (X) Batch size reduction X (X) (X) Prognoses X Elimination of internal transport X (X) Dismissal of employees X Quality training X Table 1: Improvement measures and the impacted target areas of logistics RESULTS
After the typically chosen improvement measures and their impact on the target areas of logistics were presented, the question arises how the groups performed in the periods after the implementation. This section will discuss the improvements realized by implementing a specific set of measures after the first period and in the next step provide indications about the combined impact of measures after the second period. Improvement strategies and performance Because the playing groups can decide on two measures they want to implement, the specific sets have to be considered. There can be identified 8 combinations of measures, which were taken by several groups. Table 2 presents these sets and the resulting costs per unit in period two of 68 groups with available data. Peer Group # Measure Combinations # of Groups Percentage Average costs per unit Main target area of impact 1 Layout Batch size red. 17 25% 45 Quality 2 Batch size red. Training 13 19% 62 Reliability 3 Training Training 5 7% 85 Lead times & flexibility 4 Training Elim. int. transp. 2 3% 94 Assets and costs 5 Layout Training 22 32% 95 6 Layout Q-Training 3 4% 104 7 Layout Hiring 2 3% 143 8 Layout Elim. int. transp. 4 6% 151 Total 68 80 Table 2: Measure combinations after first period and the resulting costs per unit in period two It can be seen that three sets are more popular than others: layout plus training, layout plus batch size reduction and batch size reduction plus training. Considering the impacted target areas, this means that the first and last combinations (peer groups 5 and 2) focus on improving the delivery reliability plus lead time and flexibility. The second set (peer group 1) concentrates fully on the delivery reliability. There is only one set containing an improvement measure aiming at quality, namely layout plus quality training (peer group 6). This means that the vast majority of groups are confident to have their quality under control, which is also the experience of the moderators. Nevertheless, quality varies often widely in period one. After the first round, no group already tried to reduce assets and costs. Apparently the students are not yet in the position to think about that, because there is first the need to ensure a correct delivery to meet the customer demand. Two identified sets of combinations focus fully on the area of lead time and flexibility (peer groups 3 and 4), the two remaining peer groups 7 and 8 also are a combination of impacting reliability and lead time. When it comes to the performance of the groups after implementing their measures, there are several interesting results. The first general result is a remarkably improvement of the costs per unit (good product). Coming from a total average of 2077 cost units in the first period, the average of the total set of groups in period two is 80 units. Nevertheless, there are differences on the performance improvement dependent on the measure combinations chosen. 25% of all groups belong to peer group 1, which chose the combination of layout change plus reduction and harmonization of batch sizes. Therefore, they fully concentrated on improving delivery reliability. They performed in total better than any other peer group, with a total average of 45 cost units per good product. Moreover, this group also is the most harmonic, having a standard deviation of only 17. Only one single group in this peer group had costs slightly higher than the average costs of the complete set of groups, having costs of 84 compared to the total average of 80. The second peer group performing better than the total average is peer group 2 (32% of all groups) that combined batch size reduction and training. They wanted to improve delivery reliability and flexibility, as described, through increasing capacities at specific workplaces. However, the variance of results in this peer group is higher than in the first, with a standard deviation of 33. The third major peer group, peer group 5, took the measure combination of layout change plus training, therefore also wanted to improve reliability and flexibility. The average costs per good product of this peer group were 95 units, with a standard deviation of 90. So this major peer group
performed slightly worse than the total average. This is also true for the other peer groups, details can be found in Table 2. Even more interesting is a look on the performances of the groups after they decided on improvement measures after the second period of the game. Of interest is in general, that almost every group changed the layout and reduced batch size in the first two periods, so it seems to be intuitively appropriate to implement both of these measures. The average costs per unit of all groups of period three is 42, which is significantly better than period two, with a standard deviation of 35. Thus, the performance of all groups is more homogeneous. In the following, we take a closer look on the performance of the three major peer groups 1, 2 and 5 in period three. In period three, the best two groups overall belong to peer group 1. They concentrated on reducing assets and costs and achieved costs per unit of 14 and 16 cost units. The average costs per unit of the three major peer groups in period three are different and depend on their main focus of their improvement measures after period 2, see Table 3. Peer Group # Main target area of measures Average costs per unit Lowest cost per unit 1 Assets and costs 37 14 Lead time and flexibility 58 22 2 Assets and costs 33 18 Lead time and flexibility 32 19 3 Assets and costs 27 17 Lead time and flexibility 29 19 Table 3: Measures and resulting costs in period 3 of major peer groups Peer group 5 achieved the lowest average costs per unit. As discussed, this peer group changed the layout and did some training after period one. Those groups of peer group 5, which then focused on reduction of costs and assets (e.g. dismissal of workers, prognoses) after period 2 performed best, while groups that further improved lead time and flexibility had slightly higher average costs per unit. In peer group 2, the results of both sub-groups are nearly the same. In peer group 1, these differences are quite high. It is interesting that after three periods, peer group 5 seems to pursue the best improvement strategy on the whole, compared to the results after period two where peer group 1 was best. As it seems, the training activities of peer group 5 after period 1 pay also in period 3 (more capacity and flexibility) and ensure quality as well. Nevertheless, the best groups overall belong to peer group 1. A closer examination show, that they controlled quality very well from the beginning and both primarily reduced assets and costs after period 2. Therefore, it could be assumed that they were very advanced in improving performance. DISCUSSION OF RESULTS AND IMPLICATIONS Experience with the game shows, that a high level of quality, which is mainly enhanced by training on the job, is a precondition for improving logistics performance. Moreover, this applies to delivery reliability as well, improved by process oriented layouts and reduction or harmonization of batch sizes. The results of the peer groups 1, 2, and 5 as the best performing groups support this, since they all concentrated on these areas. Beyond, this is an argument that principles of JIT enable a high logistics performance. It is important to keep in mind that in reality changes of layout and a reduction of batch sizes often require high investments in infrastructure (low batch sizes call for set-up time reduction). This is not simulated in the game in an adequate manner. Lead times are difficult to measure during the game, in particular during period one, since often production is congested or produces too less good products. However, the relation between lead time and the level of inventories and work in process can be experienced. The results of successful groups show, that flexibility (e.g. by training of additional workers, complete processing) is needed in order to successfully cope with changing customer demand. In the game, this is particularly true for period three, when customer orders are way higher than in the first two periods, which surprises many groups. The best groups overall controlled quality right from the beginning and improved delivery reliability first. In doing so, they achieved short lead times and high flexibility in the same time. Next,
they reduced assets and costs. Besides this successful improvement strategy, other strategies are equally successful. They have in common that they concentrate on reliability. This is in accordance with the aforementioned Sand Cone Model and the procedures of implementing effective logistics using principles of JIT as described in literature (e.g., Schönsleben, 2004). Consequently, the principles of JIT can be applied in the Logistics Game and lead to a high logistics performance. It is important to mention that the cost structure given by the set-up of the game plays an important role for the impact of measures on costs per unit. In the game, costs of employees are relatively low compared to costs of defective products, delays, and inventories. In another set-up or in practice, the cost effectiveness of measures is different. For instance higher labor costs improve cost effectiveness of layoffs. Therefore, the results may vary in the context of a different cost structure. Furthermore, the rigorous implementation of lean production may increase vulnerability as a result of disruptions, and therefore causes risks (Sheffi, 2005). If, for example, inventories and work in process are minimized, and a problem in the production occurs, this may not be cushioned and could cause a disruption that directly deteriorates delivery reliability and lead times. The principles of JIT discussed above are only a part of lean production. The philosophy also comprises long-term thinking, people and partners, as well as problem solving capabilities (Liker, 2004). Therefore, in order to implement lean production, these elements have to be considered as well and not only the technical ones of JIT. Being a game, the Logistics Game makes several simplifications (e.g., neglecting set-up costs, unlimited availability of raw materials). This has to be always considered. On the other side, it could serve as a basis for a profound knowledge and understanding of the more complex reality. The following implications for students and managers can be drawn: (1) It is necessary to understand the interrelations of the target areas of logistics and improvement measures in order to improve performance in a sustainable way. (2) A successful improvement strategy takes into consideration that quality control is a prerequisite and delivery reliability has to be ensured. Then, an effective and efficient logistics can be achieved by concentrating on lead times and flexibilities. Furthermore, a optimization of assets and costs is required. (3) Measures should be in accordance with these considerations and should be balanced as well. CONCLUSION The Logistics Game is a suitable means for students and managers to get familiar with production logistics and to gain a feeling and understanding of logistics interrelations. It could be successfully used as a starting point for lectures in the topic of logistics and lean production. In particular, one can reference to it when teaching and explaining logistics. The game enables students and managers to experience interrelations of logistics targets and means in an intuitive way. In our experience and according to the feedback of the players, this is very appreciated and considered as of greater value than solely explaining the corresponding theories. The feedback of students and managers, who played the game, was entirely positive and most of them really enjoyed it as well. As a main result, it makes sense, also in an intuitive way, to improve logistics performance by first controlling quality and ensuring reliability. Then lead times and flexibility can be improved, and, last but not least, assets and costs. In particular, the principles of JIT facilitate the achievement of a high logistics performance in an effective and efficient way. References Copeland, T.; Koller, T.; & Murrin, J. (2000), Valuation Measuring and managing the value of companies, 3rd ed., John Wiley, New York. Ehrbar, A. (1998), EVA economic value added the real key to creating wealth, John Wiley, New York. Ferdows, K. & De Meyer, A. (1990), Lasting improvements in the manufacturing performance In search of a new theory, Journal of Operations Management, Vol. 9 No. 2, pp. 168 184.
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