Revisiting local TOC measures in an internal supply chain: a note

Transcription

1 International Journal of Production Research ISSN: (Print) X (Online) Journal homepage: Revisiting local TOC measures in an internal supply chain: a note M. Gupta & S. Andersen To cite this article: M. Gupta & S. Andersen (2012) Revisiting local TOC measures in an internal supply chain: a note, International Journal of Production Research, 50:19, , DOI: / To link to this article: Published online: 15 Nov Submit your article to this journal Article views: 263 View related articles Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at Download by: [University of Louisville] Date: 13 March 2016, At: 17:09

2 International Journal of Production Research Vol. 50, No. 19, 1 October 2012, Revisiting local TOC measures in an internal supply chain: a note M. Gupta a * and S. Andersen b a Department of Management, College of Business, University of Louisville, Louisville, Kentucky, USA; b Department of Entrepreneurship and Relationship Management, University of Southern Denmark, Kolding, Denmark (Received 25 January 2011; final version received 20 September 2011) While the global theory of constraints (TOC)-based measures of throughput, inventory and operating expense are well known, the literature on local measures of throughput dollar-days and inventory dollar-days (TDDs/ IDDs) is sparse and inconclusive. This paper shows how adherence to these measures naturally puts a non- TOC company on a continuous improvement path by assisting in identification of the constraint and implementation of a drum buffer rope (DBR)-like system. Thus, by simulating a small company, this note highlights the relationship between TDD/IDD measures and a DBR system, which further promotes TOC principles. Ultimately, we provide future research avenues into the role these measures can play in the development of a holistic incentive system and the implementation of a DBR-like system across the supply chain network. Keywords: performance measures; theory of constraints; throughput dollar-days; inventory dollar-days; drum buffer rope; supply chain management 1. Introduction The purpose of any performance measurement system is to provide a way to monitor performance and to guide the actions that lead an organisation towards its goal. When managers have a clear sense of their priorities, finding the right way to measure performance should be high on the list of priorities in order to induce the desired behaviour. A quote by Goldratt (1990, p. 26) from an employee s viewpoint illustrates the importance of providing relevant measures: Tell me how you measure me and I will tell you how I will behave... Neely et al. (1995, p. 80) define performance measurement as the process of quantifying effectiveness and efficiency of actions where effectiveness refers to the extent to which customer requirements are met, while efficiency is a measure of how economically the firm s resources are utilized when providing a given level of customer satisfaction. In a similar sense, Goldratt (1990, p. 146) refers to local performance measures as a way to monitor deviations from an intended plan. He highlights that deviations can happen either by not doing what was supposed to be done (lack of effectiveness) or doing what was not supposed to be done (lack of efficiency). What was later to be known as the theory of constraints (TOC; Goldratt 1988a) was introduced in a business novel called The Goal (Goldratt and Cox 1984). Here, the authors introduced three global measures, throughput (T), inventory (I) and operating expense (OE), for evaluating organisational performance towards the goal of a company, i.e. to make money. These measures were revisited in a non-fictional book by Goldratt (1990), along with new measures: throughput dollar-days (TDDs) and inventory dollar-days (IDDs). Where T, I and OE are global (i.e. company-level operational measures of performance), the TDD and IDD measures serve as local measures and judge only the impact the local area being measured has on the end result (p. 145). In this sense, TDD is the measure of whether customer requirements are being met (effectiveness), whereas IDD is a measure of how efficient the organisation is in terms of not building excessive inventory. In a recent fictional novel, Goldratt et al. (2000) has revived interest in TDD/IDD measures in the context of supply chain replenishment and distribution systems. Given the paucity of relevant literature (discussed later) and the absence of applications of TDD/IDD measures, the purpose of this paper is to answer the question: What roles can TDD and IDD measures play in strengthening the value chain of a company? This paper is organised in the following way. The rest of this section introduces a wellknown example from the TOC literature (Goldratt 1990) called the PQ Company and provides an example of how the TDD and IDD measures are calculated at department levels using an Excel model of the company. *Corresponding author. mahesh.gupta@louisville.edu ISSN print/issn X online ß 2012 Taylor & Francis

3 5364 M. Gupta and S. Andersen Section 2 briefly reviews the pertinent literature, tracing the origin of the TDD and IDD concepts. Section 3 provides a description of the simulation model developed in Microsoft Excel. We briefly discuss the verification and validation of the model. Section 4 outlines the results collected from a set of selected scenarios to highlight the role the TDD/IDD measures play in guiding process improvement efforts. Section 5 discusses the results. Finally, section 6 concludes our paper with directions for further research in the areas of buffer management, reward system design and external supply chain management. 1.1 The PQ Company Goldratt (1990) introduced the PQ Company as a way to demonstrate how our assumptions make optimal decision making difficult (e.g. product-mix, make vs. buy, and investment) when all uncertainty is removed from the data (Figure 1). In a similar fashion, we use this company to gain deeper understanding of the TDD/IDD measures. Figure 1 provides a visual representation of the internal value chain (process flow, operation times, raw material costs, weekly demands and selling prices) of the company. We further assume that the PQ Company employs a set of local measures (TDD/IDD) at each resource pertaining to each product. For example, Figure 1 shows that resource B is used with process (1) RM2 for product P, (2) RM2 for product P and Q, and (3) RM3 for product Q, and, thus, will require computation of three corresponding sets of TDD/IDD measures. 1.2 Calculating TDD and IDD By means of a simple hypothetical example, the calculation of TDD and IDD is illustrated. Originally, TDD had been calculated as the selling price of a late order multiplied by the number of days it is late, which could start from the due date or, better yet, from an internal scheduling point such as a buffer (Goldratt 1990, p. 149). Similarly, IDD had been calculated as either the value of inventory multiplied by days on hand or as the value of inventory in excess multiplied by the number of days it had been in excess. Our model calculates TDD as selling price times days past due date and calculates IDD as inventory at raw material value times days since arrival at the resource. Figure 2 shows the computations of TDD/IDD values for resource B over a period of four weeks when material is released for 100P and 50Q weekly. We note that the TDD/IDD values grow exponentially if corrective actions are not taken. In the example, we point out that it is not the numerical value that is interesting; rather, it is the progression over time. Each line in Figure 2(a) represents a process for resource B. The processing of RM2 by B is denoted by RM2-Q for product Q and RM2-P for product P. Lastly, the processing of RM3 is only for product Q and thus denoted RM3-Q. Releasing 20P and 10Q daily makes resource B fall 120 minutes behind every day; 600 minutes are required to process this amount while B only has 480 minutes available each day. The 20P and 10Q that are released on day 1 Figure 1. The PQ Company.

4 International Journal of Production Research 5365 are due to the customer on day 2; thus, TDD will be calculated if these units are not yet delivered on day 3. Starting with an empty system, B has two days (960 minutes) until orders will start to be late. On the exact minute that day 5 ends, resource B has fallen one day behind. On day 6, B again has 120 minutes of work in excess of capacity, which means that 120 minutes of work on a process that is due on day 6 will expand into day 7. At day 7, B is in the middle of processing 20 units of RM2 for product P, which is now late by one day. TDD is calculated by multiplying these units (20) with the final selling price of product P ($90) and the number of days the units are late (1); so the TDD value is $1,800 for RM2-P on day 7. In Figure 2(b) the lines represent inventory stored by resource B, regardless of whether this inventory is waiting to be processed, is being processed or has already been processed by B but not transferred. When the first day ends, B is busy processing 10 units of RM3 for Q. Calculating IDD at the beginning of day 2, the 10 units raw material value (10 20 ¼ 200) is multiplied with days on hand (1), which yields an IDD of $200 for (RM3-Q). 2. Literature review Despite these local measures being more than 20 years old, most papers on TOC and performance measurement mention only the global measures T, I and OE (e.g. Wahlers and Cox 1994; Lockamy and Spencer 1998; Mehra et al. 2005). Since the first paper conceptualising TDD/IDD measures using a numerical example appeared in a notso-widely available TOC journal (Goldratt 1988b), not much has been said in the refereed journals about these local measures. Goldratt (1990) referred to this paper while explaining the significance of TDD measures. Noreen et al. (1995) did not find any evidence of companies using both of these measures. A search in the EBSCOhost Business Source Complete database for Throughput dollar-days or Inventory dollar-days reveals only one paper investigating a dispatching rule to improve the TDD and IDD measures (Ho and Li 2004). Goldratt et al. (2000) revived these measures in the context of supply chain management. Simatupang et al. (2004) further conceptualised how these measures should be used to manage supply chain network performance. Recently, Schragenheim et al. (2009) conjectured that if DBR implementation can ensure a due date performance close to 100% in a company, then TDD/IDD measures might be redundant. To our knowledge, the relationship between DBR implementation and TDD/IDD measures has not been investigated in the literature. It is one of the main objectives of this paper to perform such an investigation. Using a simulation model, we show that when these local measures are employed at the department levels, decisions are made that lead a non-toc company on a continuous improvement path by assisting in identification of the constraint and implementation of a drum buffer rope (DBR)-like system. 3. Simulation model In order to show the TDD/IDD values in the context of the PQ Company, a simulation model was built in Excel using Visual Basic for Applications (VBA). We selected this platform for its simplicity and widespread use. The basic components of the model consist of: (1) a structure to control the passage of time, (2) an approach to release raw materials and to handle WIP, (3) a mechanism for determining availability of the resource, and (4) a method to prioritise the list of work orders. Figure 3 shows how the model allows users to input parameters representing various scenarios. (b) 10, (a) 10, RM2-Q RM3-Q RM2-P RM2-Q RM3-Q RM2-P Figure 2. TDD/IDD values for resource B. (a). TDD. (b). IDD.

5 5366 M. Gupta and S. Andersen Figure 3. Customising a scenario using an Excel spreadsheet. 3.1 Assumptions and limitations As with any simulation, a number of assumptions have been made in building the model. The PQ Company is a company without any uncertainty. As Figure 3 shows, each department has 480 minutes of capacity each day without any break downs or setups. Demand is fixed with a release of material for 20P and 10Q each day. A department must finish the entire batch of either 20P or 10Q before it can work on a different batch. When a resource is ready for work, a batch will be chosen based on a setting provided by the user. By default, the resource (e.g. A, B, C or D) will look through its list of work and find the first batch in order of arrival with all material present. TDD and IDD measures can vary from minute to minute. In this model, however, a snapshot of TDD and IDD is taken every morning. A configurable warm-up period was built in to prevent any bias from running the system without sufficient material in place. 3.2 Verification and validation of the model Several measures were taken to verify the model and validate the results. A logging system was set up to trace different events that occur during the simulation, allowing for examination of every activity of the model. The model results were compared to the results reported by Goldratt (1990) for three different scenarios: (1) no resource constraint, (2) resource B as a constraint with priority for product Q, and (3) resource B as a constraint with priority to produce product P first. The profits per week verified in the model were $1500, $300 and $300, respectively, for the three scenarios as reported by Goldratt (1990). Validation of the TDD and IDD calculations was done using the logging mechanism and simulation duration of one week. For example, releasing 10Q and 20P requires 600 minutes of processing time for resource B, which has

6 International Journal of Production Research 5367 Figure 4. Scenario one: Progression of TDD/IDD values for products P and Q. (a) TDD values for product P. (b) IDD values for product P. (c). TDD values for product Q. (d). IDD values for product Q. only 480 minutes available in a day. Therefore it is expected that B falls 120 minutes behind schedule every day. Consequently, the model showed a spike in the TDD/IDD values for resource B, as well as TDD values for the subsequent resources. 4. Results In this section, we discuss how the TDD and IDD measures can help managers in the PQ Company implement DBR, even though they have no prior knowledge of TOC, by going through three different scenarios: (1) identify the constraint with the aid of TDD/IDD measures; (2) exploit the constraint (e.g. choose the right product mix); and (3) subordinate other resources usage to the constraint (e.g. change the material-release policy). 4.1 Scenario one: Identify the constraint with the aid of TDD/IDD measures At this initial stage, we assume that the PQ Company releases materials according to the full demand from its customers. On a daily basis, this amounts to materials for making product mixes of 20P and 10Q. It is assumed that the managers of the PQ Company have no prior knowledge of DBR and that they only have the measures of TDD/ IDD to guide their actions. Figure 4 is a dashboard of four graphs tracking the progression of TDD and IDD at all four departments simultaneously over the course of three weeks. To avoid the bias of starting with an empty system and to better report the results that would be expected from people who know the PQ Company, we use a small initial inventory as shown in Figure 3. The initial inventory has a value of $1000. As with Figure 2, the TDD lines represent processes, but this time carried out by different resources. Where a resource does more than one process on a product, a number is appended to the resource name (such as C1 and C2). IDD lines represent inventory, and here the lines are denoted by the name of the resource with inventory on hand and the name of the process providing this inventory. As such, resource C s stock of RM1 processed by A for product P is denoted by C-A.

7 5368 M. Gupta and S. Andersen Looking at the TDD values in Figures 4(a) and 4(c), we see that an increasing number of late orders are held up at resource B. Occasionally, resources C and D receive a batch of materials from B that cannot be finished during the same day; thus, they incur TDD penalties for these. However, as C and D finish these batches, their TDD values drop to zero again. Resource A is the only resource which never receives TDD penalties, because it has sufficient capacity to fulfil the daily demand and its direct access to raw materials allows A to push processed materials to B and C. The IDD measure in Figures 4(b) and 4(d) shows a similar pattern. Materials pile up in front of resource B; meanwhile, C and D have materials waiting because they are dependent on B to finish its work. Resource A has an IDD of zero as a result of its sufficient capacity to meet market demand. It is expected that a constraint will be the place to look for the highest TDD value since the only resource burdened with TDD is the one that is late but has all the materials needed available to carry out its task. We would also expect the constraint to have a high IDD value since the non-constraints push materials to the constraint at a higher rate than they can be processed. Resources downstream from the constraint can have additional materials accumulate that are not processed by the constraint. From this description, it is logical to conclude that resource B is a constraint in our PQ Company example. Compared to a demand of 400P 200Q in a four-week period, the company manages to finish 240P 120Q. The throughput amounts to $18,000, which is equivalent to the operating expense, resulting in zero net profit. Inventory amounts to $4900, an increase of $ Scenario two: Exploit the constraint (e.g., choose the right product-mix) Now that the constraint has been identified, the task is to make the most out of it. In an implementation of DBR, a buffer is placed in front of the constrained resource to keep it from idling. Because of the nature of the PQ Company, resource B is already protected from idling because, in one of its tasks, it has direct access to raw materials. However, in the other task, the feeding resource A completes its work faster than B can consume it. Most other actions used in real companies to exploit their constraints are not relevant for the PQ Company example because there is no variability to remove or setups to reduce. What remains is the question of which product, P or Q, contributes most to the bottom line? The PQ Company example was originally designed to illustrate a point about product mix. Therefore product Q, intuitively to most managers, seems to be the obvious choice because it has the highest selling price, the shortest overall production time and the highest contribution margin. However, by using the concept of Throughput per constraint unit (T/CU), it can be seen in Figure 1 that, in order to generate a throughput (selling price direct material costs) of $45, resource B only has to work for 15 minutes on product P. However, to generate a throughput of $60 ($100 $40), resource B has to work for 30 minutes on product Q. The equivalent T/CU is $3/minute for P and $2/minute for Q. Armed with this information, imagine that the managers of the PQ Company tell its four resources that they should work on product P whenever it is available. This action is similar to what was done in the fictitious company described in The Goal (Goldratt and Cox 1984), where colour-coded notes were attached to materials that had priority over everything else. Further, the managers decide to stop releasing product Q, but they keep releasing product P. The simulation model is now extended another three weeks, and these three weeks use the new policy where P has a higher priority than Q and no further material for Q is released. The resulting TDD/IDD measures are shown in Figure 5(a) (d). Day 16 is the first day with the new policy in place. Since resources are now instructed to prioritise P over Q, the TDD and IDD values of product P start to decrease relatively fast. Product Q, on the other hand, shows a sharp increase in both TDD and IDD because orders for Q are now lying still until the backlog of product P is cleared. At the end of this second period of three weeks, TDD has reached zero for both P and Q, while IDD has found a stable level. The decision to prioritise product P had a positive impact on the PQ Company s financial performance. Because production of P now increased by 360 units and Q increased by 60 units, throughput was thus increased by $19,800 while operating expenses were at the same level, securing a net profit of $1800. Inventory also shows a decline from $ 4900 to the original starting point of $ Scenario three: Subordinate other resource usage to constraint After having run for three weeks with the release of Q constrained, managers of the PQ Company can now begin to take in new orders for Q. The next step for the company is to tie the rope, to ensure a release of materials that

8 International Journal of Production Research 5369 Figure 5. Scenario two: Progression of TDD/IDD values for product Q. (a) TDD values for product P. (b) IDD values for product P. (c) TDD values for product Q. (d) IDD values for product Q. prevents flooding of materials in the system and communication with the marketing function to discuss what can be reliably delivered. The release of materials is limited to accommodate 480 minutes of daily work for resource B with priority to product P, i.e. to produce product mix 20P 6Q. The resources are again asked to work on a FIFO (firstin-first-out) basis. Figures demonstrating the effects of this new policy are not necessary, since the TDD value now stays at zero because all orders are delivered in a timely fashion. The IDD remains constant. This policy subordinates resource A to resource B and thus prevents flooding the floor with materials and draining the company of cash. With the rope tied, the PQ Company can produce 100P and 30Q weekly, securing a profit of $ Discussion During the summer of 2009, one of the authors was working with the management of a small manufacturing company who wanted to implement drum buffer rope. Initially, the company struggled to identify their constraint and was suffering from poor due-date performance. One solution proposed by the author was to use graphs with the TDD/IDD measure for every department in the company in order to make clear to all employees what to work on and what not to work on. Seemingly, the top management of the company never really understood the significance of the measures, as they did not give priority to implementing the solution, which required a significant change in the way the ERP system was used. As an alternative, the author instead proposed an e-learning course provided by TOC consultants. The course provided a manual system based on buffer management similar to that described by Schragenheim and Ronen (1991). In the context of this paper, even though the company managed to make their due-date results consistently in the high nineties, the process left several unanswered questions. How can the company provide feedback to the individual departments about their performance? In what way can the company build an incentive system to support ongoing improvement? We argue that these questions are more easily addressed through quantifiable measures such as TDD/IDD that support the overall goal of the organisation. In this paper, we show that TDD/IDD measures complement DBR implementation and have not necessarily become irrelevant.

9 5370 M. Gupta and S. Andersen 6. Conclusion and future research directions TDD and IDD are local performance measures, which can be used to induce behaviour that is consistent with an overall plan. While the economic desirability of the plan should be governed by sound judgement using global performance measures, these local measures should point to specific areas of the company that are in need of management attention if the overall plan is to be executed with effectiveness and efficiency. The TDD measure can be used effectively to identify the constraint in an internal supply chain. Armed with this information, decisions can be made that exploit the constraint. The rest of the resources in the internal supply chain must then be subordinate to this decision. The IDD measure provides a way to monitor whether resources are, in fact, subordinating to the constraint. As shown through the PQ Company example, the result is higher profits with less cash tied up in inventory. When resources in the PQ Company are subordinated to the constraint, the TDD measure becomes redundant and the IDD measure remains fixed because there is no variability in the system. We show that these graphs provide a comprehensive overview of the entire operation to anyone who has been introduced to the measures. Whereas TDD ensures on-time delivery and quality (i.e. effectiveness), IDD continuously promotes behaviour that reduces inventory (i.e. efficiency). In this way, the local measures are not only in place to ensure on-time delivery but also to support a process of ongoing improvement that involves all workers. Although effective buffer management can result in many of the same benefits shown in this paper, there are situations in which effective buffer management is hard to implement. Supply chains are particularly filled with uncertainty, since every company may become a constraint at any point in time based on decisions made related to other parts of their supply network. Further research should look into the merits of using TDD/IDD measures to facilitate ongoing drum buffer rope-like behaviour in a supply chain. Another possible employment of TDD and IDD could be to build a reward system based on these measures. Several papers have been published by TOC proponents on the dangers of using incentive systems that drive local efficiencies, but to our knowledge there is complete silence on the topic of incentive systems used in TOC implementations. Acknowledgement The authors wish to express their gratitude to two anonymous reviewers for their insightful comments and many significant improvements in the text. References Goldratt, E.M., 1988a. Computerized shop floor scheduling. International Journal of Production Research, 26 (3), Goldratt, E.M., 1988b. The fundamental measurements. Theory of Constraints Journal, 1 (3), Goldratt, E.M., The haystack syndrome: sifting information out of the data ocean. Croton-on-Hudson, NY: North River Press. Goldratt, E.M. and Cox, J., The goal. Croton-on-Hudson, NY: North River Press. Goldratt, E.M., Schragenheim, E., and Ptak, C., Necessary but not sufficient. Croton-on-Hudson, NY: North River Press. Ho, T.F. and Li, R.K., Heuristic dispatching rule to maximize TDD and Idd performance. International Journal of Production Research, 42 (24), Lockamy III, A. and Spencer, M.S., Performance measurement in a theory of constraints environment. International Journal of Production Research, 36 (8), Mehra, S., Inman, R.A., and Tuite, G., A simulation-based comparison of TOC and traditional accounting performance measures in a process industry. Journal of Manufacturing Technology Management, 16 (3), Neely, A., Gregory, M., and Platts, K., Performance measurement system design: a literature review and research agenda. International Journal of Operations and Production Management, 15 (4), Noreen, E., Smith, D., and Mackey, J., The theory of constraints and its implications for management accounting. Great Barrington, MA: North River Press. Schragenheim, E. and Ronen, B., Buffer management: a diagnostic tool for production control. Production and Inventory Management Journal, 32 (2),

10 International Journal of Production Research 5371 Schragenheim, E., Dettmer, H., and Patterson, J., Supply chain management at warp speed: Integrating the system from end to end. Boca Raton, FL: Auerbach Publications. Simatupang, T.M., Wright, A.C., and Sridharan, R., Applying the theory of constraints to supply chain collaboration. Supply Chain Management: An International Journal, 9 (1), Wahlers, J.L. and Cox, J.F., Competitive factors and performance measurement: applying the theory of constraints to meet customer needs. International Journal of Production Economics, 37 (2 3),