Human planners, planning structure and the vertical bullwhip

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1 Human planners, planning structure and the vertical bullwhip Dieter Fischer a, Jan Fransoo b, and Philip Moscoso c a University of Applied Sciences of Northwestern Switzerland, Windisch, Switzerland, dieter.fischer@fh-aargau.ch b Technische Universiteit Eindhoven, Eindhoven, Netherlands, j.c.fransoo@tm.tue.nl c IESE Business School, Madrid, Spain, pmoscoso@iese.edu Abstract In hierarchical production planning structures, decisions by planning entities at the various levels may lead to a planning bullwhip, for example, because of an inappropriate updating frequency of plans. In this planning bullwhip, poor estimates of the lead time and poor actual performance reinforce each other. We study the planning bullwhip in relation to the mitigating role that human planners may play, as the effect very often is originated and amplified by the use of decision support systems. We use the case study methodology. Our findings suggest that especially the planning structure plays a crucial role in enabling humans to mitigate the planning bullwhip. Keywords: Production planning and control, human planners, decision making, case study 1. Introduction Most planning structures in industry are hierarchical. The planning process consists of several consecutive decisions, increasing in level of detail and decreasing in planning horizon, whereby the higher planning level imposes constraints on the lower planning level. There are good reasons for organizing planning processes in this way, such as decreasing the complexity of the planning problem by decomposition, leaving control opportunities at a place as low as possible in the decision hierarchy to encourage fast response, and allowing the planning systems to make as much use as possible of local information (see, e.g., [1],[12],[3]). Within production planning, the lead time is a key control parameter in the planning hierarchy. It can be argued that the lead time is an exogenous parameter to the planning [3], while others argue that the lead time should be endogenous to the planning system and reflect the current state of the shop floor (e.g., [7]). In most of the planning systems deployed in industry, which are based on MRP-logic, lead time is an exogenous parameter to the planning system. That is, the user inputs a value for the lead time into the ERPsystem, for each of the levels in the bill-of-materials. It is however not obvious, from a theoretical point of view, what should be the proper value of the lead time and if and how it should be updated [6][14]. The main difficulty with a more or less regular update of the lead time is that the planning system can become instable, since a modification in a key planning parameter such as the lead time leads immediately to substantial changes in the planning and release policy, likely to have substantial effects on the actual lead time on the shop floor. This effect has been coined the "lead time syndrome" [10] and later studied in a more formal and quantitative manner [13]. Selcuk et al. [13] demonstrate the existence of the lead time syndrome subject to certain assumptions regarding the updating behaviour of the planning parameters by the planner operating the ERP system. The effect has strong similarities to the supply chain bullwhip effect studied by Forrester [5] and Lee et al. [9], and hence the lead

2 time syndrome can also be named the planning or vertical bullwhip. Following a number of more conceptual papers on the lead time syndrome, Selcuk et al. [13][14] completed a number of formal studies investigating the effect of the lead time syndrome. Empirical studies of the vertical bullwhip have not yet been published in the academic literature. There are several reasons why an empirical study is useful. First, the papers on the lead time syndrome assume a specific planning structure and also a specific way in which the main planning parameter (lead time) is updated. Due to the large-scale implementation of ERP systems, it may be the case that the updating procedures in real life are different. It is not unreasonable to assume that due to the complexity of parametrising large scale ERP systems, planning parameters are updated far less frequently than is commonly assumed in the research literature. Second, in industrial practice human planners and operators play a key role in planning systems, and thus the vertical bullwhip may be influenced by the actions of humans in the system. Third, the information infrastructure may be crucial for the size and effect of the vertical bullwhip. Modern information technology, such as Manufacturing Execution Systems [11] enable higher level echelons to have fairly detailed status information while the absence of such or similar systems may decrease the visibility of higher level planners on the actual process on the shop floor and thus enhance the vertical bullwhip effect. In this paper, we present a number of insights into the reality of the vertical bullwhip. They are based on a single case study at a Swiss discrete manufacturing company, supplying very sophisticated parts for the air defence industry. The company had been having substantial problems with similar symptoms as can be observed when the vertical bullwhip occurs. After introducing a substantial change in the planning structure and organization, those problems disappeared in a short period of time. The case study provides us with a clear context to investigate some empirical aspects of the planning bullwhip which to our knowledge have not been reported in the literature. The paper is organized as follows. In Section 2, we will discuss our research questions and methodology. Section 3 includes a description of the industrial case, and in Section 4 we analyze the case and present our results. We conclude by presenting our main findings and insights in Section Research questions and methodology The vertical bullwhip is an interesting phenomenon that has not received attention in the empirical Operations Management literature. Consequently, insights are lacking into the empirical validity of the effect. Furthermore, there are no insights into the moderating and possibly mitigating effects of human planners on the vertical bullwhip. In this paper, we are interested in developing some of these insights. Our two main research questions therefore are: 1. Can we observe the vertical bullwhip in reality and do the theoretical explanations for the existence of the vertical bullwhip hold in reality? 2. What are additional moderating effects on the vertical bullwhip that have not yet been modelled in theoretical studies on this phenomenon? The first question focuses on finding empirical evidence that establishes a clear link between the lead times, the planning frequencies, the resulting backlog and performance deterioration. Earlier studies on the lead time syndrome [10][13][14] provide us with the following hypotheses that form the basis for analysis: Hypothesis 1a Under a hierarchical planning structure, lead times tend to be updated by higher level planners as a consequence of incorrect loading based on queuing behaviour on the shop floor. This is the basic premise of the lead time syndrome. This presupposes an active updating procedure by higher-level planners based on the operational lead time performance on the shop floor. It assumes that if the workload increases on the shop floor, the overall lead time increases according to Little's law (see, e.g., [7]). As a consequence, the planner "observes" these larger lead times and adjusts the planning parameters. Hypothesis 1b Decreasing the planning frequency decreases the vertical bullwhip effect, made visible by a decrease in the order backlog, a decrease in lead times, and an increase in on-time delivery performance. If the planning frequency is decreased, any overreaction that may occur as a consequence of shortterm queuing effects is smoothened. Consequently, decreasing the planning frequency will decrease the variability in lead times [13].

3 With regard to additional effects that have an impact on the vertical bullwhip, we are particularly interested in the effects of having complete and detailed information at the higher level (provided by modern IT systems for manufacturing execution), the effect of humans interfering with system decisions to mitigate the vertical bullwhip, and the effect of the planning structure, in particular the number of planning levels. Hypothesis 2a Having more complete, updated, and accurate status information available at the time of making the plan, increases the quality of planning and hence decreases the vertical bullwhip effect. If the planner has more updated information, he will have a better overview of and insight into the actual performance of and progress at the shop floor. As a consequence, he will be able to make better informed decisions. Also, the planner will be tempted to plan in more detail, effectively reducing the operational flexibility on the shop floor. However, it should be noticed that this does not mean that planners increase the planning frequency. Hypothesis 2b Humans may have more information than a computer system, and may have more local control options than modelled in such a system, and hence will attempt to correct any adverse behaviour caused by system decisions. As a consequence, this will mitigate the major effects of the vertical bullwhip. In industrial practice, it is reasonable to assume that there is more flexibility than any computer system can model. Operators can make modifications in the machine allocation, or planners and foremen can prioritize in a smart manner. Also, the planners and operators physically observe the backlog and will attempt to reduce this by taking additional measures. On the one hand, this may lead to an increase in the vertical bullwhip effect due to further overreaction; alternatively, it can be argued that because of additional local flexibility, this will reduce the vertical bullwhip effect. Hypothesis 2c Decreasing the number of planning levels decreases the vertical bullwhip effect since the system will have less feedback loops to cope with, and delays in the system will decrease. One of the major factors affecting the planning bullwhip is delay in information and delay in response. Decreasing the number of planning levels will generally decrease these delays. There will however be an adverse effect in terms of planning complexity. We investigate these hypotheses using a single indepth case study [4][15][16]. The case company selected was Oerlikon Contraves AG, a discrete parts manufacturer in Switzerland that supplies complex parts to the air defence industry. A description of the case site and observations is included in the next section. We have collected data on the case by several methods. First, we have conducted extensive interviews with the production manager, the manager of the production control centre, the IT manager responsible for the ERP system, and with the two project managers responsible for the APS/MES system. Furthermore, the work of three planners has been observed extensively and our observations have been discussed with them. Also, we have observed extensively the work of the foreman and discussed the observations with them. Second, we collected documents describing the functionality and performance of the system. The main work was conducted during three full days and follow-up of the initial findings has been done by phone, follow-up meetings and . The field work was conducted by one of the authors. The combination of observations, interviews, discussions, and documentary study enabled us to triangulate many of the findings from the case, and have several sources for each of the findings. 3. Case description: Oerlikon Contraves AG We now describe the production planning characteristics of the case under consideration that are relevant with regard to the research questions and hypotheses formulated in section 2. First, general production and planning characteristics of the case are described, and then the key problems of the initial planning system are reviewed. Finally, a new planning approach that eventually improved planning performance is summarized Production and planning characteristics of OCAG Oerlikon Contraves AG (OCAG) is a Swiss manufacturer of discrete parts for the air defence business, and belongs to a European multi-billion industrial group. The core capability of OCAG is the development and manufacture of advanced air defence

4 systems as well as simulators and training systems. 60% of the products offered are standardized. However, they are quite complex in terms of the number of components they are built of. In fact, OCAG manages around 600 client orders a year that on the shop-floor revert to around 15,000 production orders. Manufacturing of an order typically requires going through 5-7 different work units for completion, with an average lead time of around 2.7 months. Stocks are primary kept at the level of subcomponents and raw materials, and orders are only released when a client demand exists. Fulfilment of due dates is very critical as typically the OCAG products are subassemblies of even larger defence systems. 3.2 Initial planning system and perceived problems Until the year 2002, production planning at OCAG was done end-to-end with an integrated ERP (SAP), but organizationally following a decentralized approach. The planning tasks were distributed across four organizational levels: a. At the beginning dispatchers did a rough production plan, determining key dates for start and end of a client order. b. These plans were then detailed at a next level by the work preparation unit, adjusted for the different shop floor working units. c. Those operational production units were allowed to do some final adjustments in the production plans if required. They were able, for example, to choose a particular machine among a set of equals. d. In order to certainly meet the agreed due dates of key client orders, the role of order chasers was introduced. 24 of these operators physically pushed high priority orders through the shop-floor. For this they negotiated which each of the different working unit the best possible schedule for their orders. Thanks to a significant effort of the order chasers OCAG was able to fulfil 87% of its due dates, but generating a significant backlog of orders. The order chasers had in fact created two types of order flows at OCAG. High priority orders were pushed physically through the shop-floor but at the expense of regular order been delayed repeatedly. Additionally, higher level plans were flawed as the ERP system in place was permanently not up-to-date with respect to the shop-floor. That happened because the tremendous effort by the planners to systematically and regularly update the comprehensive ERP was not considered to be worth the effort. Consequently, for example, despite the tremendous backlog, the lead times remained essentially unchanged in the ERP, and consequently in the production plans released. Moreover, once an order chaser took charge of an order, planners did lose control of it until the chaser reported its completion Solving planning problems with a new approach OCAG decided to revise the described production planning approach, and completed implementation of a new one in In this new model, the planning decisions were completely centralized in a production control centre with 10 qualified planners that had gained experience in previous OCAG assignments. Order chasers were discontinued, and all remaining operation units had to stick strictly to the plans provided by the control centre. Additionally, OCAG installed an Advanced Planning System (APS). In this new planning system, once a customer order enters, material requirements and base data are elaborated in the ERP. Then the orders are transferred to the APS for the shop-floor planning (backward termination), including a daily 2- step capacity harmonization. Permanently, work progress is fed directly into the APS by the shop-floor operators. Further, every fourteen days, the control centre informs the local operating units about the medium-term capacity requirements. On this base, these units generate their resource action plans (including shift work planning). With this new planning system planning results at OCAG were significantly improved. First of all, it was able to drastically reduce the backlog (completely after one year into the new model). But it also could improve accomplishment of due dates to 97%, and planners had at every moment a very accurate view of work in progress. 4. Case analysis and results We will analyze the case following the structure provided by the hypothesis described in Section 2. Hypothesis 1a Under a hierarchical planning structure, lead times tend to be updated by higher level planners as a consequence of incorrect loading based on queuing behaviour on the shop floor. First of all, it is interesting to note that, formally, lead times were not updated. This was not an explicit policy decision, but changing the lead times of

5 thousands of items in a comprehensive ERP system like SAP was too cumbersome for the planner to conduct in a systematic and frequent way. The system lead times thus remained unchanged. Effectively, however, lead times were updated by the actions of the order chasers. The order chasers changed the sequence of the production orders on the shop floor, effectively reducing the lead time for a small number of highpriority customers and increasing the lead time for a large number of other customers. Effectively, this lead to an average lead time increase of about 15%. In addition, a considerable backlog was built up in the system, as has been discussed in the previous section. The detrimental effect of updating that is found in the theoretical studies thus was also found at our case site. However, the mechanics of the effect look to have been different. While in the updating procedures studied in the literature, the main driving force for the updating is the overall queuing behaviour on the shop floor, at the case site the updating was done informally and differentiated: some customers received shorter and controlled lead times, while the majority of customers received longer lead times. Simulation studies in the literature have demonstrated that it is possible, even under high utilization and backlog, to give preference to a small number of orders with high priority [2]. Apparently, human decision makers tend to act more differentiated than the updating rules suggested in the literature. This differentiation and creativity of human planner has also been established in a study by Fransoo and Wiers [7], who conducted a quantitative field study and conclude that planners deploy a larger variety in their decision making once the complexity of the planning problem increases. We thus find partial support for hypothesis 1a: we do observe the vertical bullwhip, but the mechanics are more subtle and differentiated than suggested in the modelling literature. Hypothesis 1b Decreasing the planning frequency decreases the vertical bullwhip effect, made visible by a decrease in the order backlog, a decrease in lead times, and an increase in on-time delivery performance. Similarly as in hypothesis 1a, it is interesting to note that both under the old and the new planning structure, the formal planning frequency was identical: plans are made daily. However, the work of the order chasers and the authority they had to continually update the planning by giving preference to certain orders and changing the announced sequence of production meant that under the old system the effective planning frequency was much higher. Plans were being updated on an almost continuous basis. Under the revised planning structure, this effective planning frequency was decreased. Hypothesis 2a Having more complete, updated, and accurate status information available at the time of making the plan increases the quality of planning and hence decreases the vertical bullwhip effect. It is very clear from the case description that the main role of the new software was to have complete, updated, and accurate status information. Furthermore, the planners now have far more accurate status information. Note that this information is not used for more frequent planning decisions, as decision are made only daily and not revised in between planning decision. Hypothesis 2b Humans have more information than any system, and have more local control options than modelled in a system, and hence will attempt to correct any adverse behaviour caused by system decisions. As a consequence, this will mitigate the major effects of the vertical bullwhip. In the old situation, an extensive number of local control options existed for the planners. These local control options were executed by the order chasers, who continually updated the sequence of orders. Instructions of the planners at the higher level were typically running behind the shop floor reality. This caused a substantial planning bullwhip. It can therefore be concluded that in this particular case, the planning bullwhip was reinforced rather than mitigated by the action of the human planners. Hypothesis 2c Decreasing the number of planning levels decreases the vertical bullwhip effect since the system will have less feedback loops to cope with, and delays in the system will decrease. This hypothesis can be confirmed in our case study. In fact, the reducing the number of planning levels has probably been the main cause, alongside with the reduction in the planning frequency, in reducing the vertical bullwhip. This is in line with the insights from theoretical studies. 6. Conclusions and insights In this study, our objective was to investigate the role of human planners and the planning structure on

6 mitigation of the planning bullwhip. We have developed a number of hypotheses based on theoretical studies of the planning bullwhip and investigated whether these hypotheses hold in an empirical setting. Our findings indicate support for the main mechanisms underlying the vertical bullwhip, namely the influence of planning frequency and number of planning levels. We did not find support for the mitigating abilities of human planners. Apparently, the opportunities for human planners to mitigate any effect are substantially constrained by the planning structure, and the availability of up-to-date information. Although this is the first empirical study to explicitly investigate the impact of human decision making and decision structure on the vertical bullwhip (or lead time syndrome), our study is not conclusive. Especially the role that humans could play as selfcontrolling actors in decentralized systems could not be ascertained very well. Our results suggest that this role may be very limited. However, it should be understood that in the new situation, the complexity is still relatively low [7]. Difficulties may arise once the utilization rate of the system increases and the central planning function may be unable to cope with the increased complexity. At the same time, the decentralized decision making capabilities have effectively been taken out. Further study may be needed to investigate this effect. In general, however, we find empirical support that is in line with the findings from modelling analysis, as well as the insights developed about the traditional horizontal bullwhip in supply chains. Acknowledgements This research has been supported financially by the European Science Foundation, COST Action A-29 "Human and Organizational Factors in Industrial Planning and Scheduling", chain management: design, coordination and operation. Handbooks in operations research and management science, North-Holland, Amsterdam, 2003, pp [4] Eisenhardt KM. Building theories from case study research. Academy of Management Journal 14 (1989) [5] Forrester JW. Industrial Dynamics. Productivity Press, Cambridge, [6] Fransoo JC. Lead Time Technology. Inaugural Lecture, Technische Universiteit Eindhoven, Eindhoven, [7] Fransoo JC and Wiers VCS. Action variety of planners: Cognitive load and requisite variety. Journal of Operations Management, in press (2005). [8] Hopp W and Spearman M. Factory Physics (2nd edn.). Irwin McGraw-Hill, Boston, [9] Lee H, Padmanabhan V and Whang S. Information distortion in a supply chain: the bullwhip effect. Management Science 43 (1997) [10] Mather H and Plossl GW. Priority fixation versus throughput planning. Production and Inventory Management Journal, 1978 :3, [11] McClellan M. Applying Manufacturing Execution Systems. The St. Lucie Press, Boca Raton, [12] Meal HC. Putting production decisions where they belong. Harvard Business Review 62:2 (1984), [13] Selcuk B, Adan IJBF, De Kok AG, and Fransoo JC. An explicit analysis of the lead time syndrome: Stability condition and performance evaluation. Working paper, Technische Universiteit Eindhoven, Eindhoven, 2006 [14] Selcuk B, Fransoo JC, and De Kok AG. The effect of updating lead times on the performance of hierarchical planning systems. International Journal of Production Economics, in press (2005). [15] Voss CA, Tsikriktsis N, and Frohlich M. Case Research in Operations Management. International Journal of Operations & Production Management 22 (2002), [16] Yin, RK. Case Study Research: Design and Methods (2nd edn.). Sage, Thousand Oaks, References [1] Anthony RN. Planning and control systems; A framework for analysis. Graduate School of Business Administration, Harvard University, Boston, [2] Bertrand, JWM and Van de Wakker AM. An investigation of order release and flow time allowance policies for assembly job shops. Production Planning & Control, 13 (2002) [3] De Kok TG and Fransoo JC. Planning supply chain operations: Definition and comparison of planning concepts. In: De Kok AG and Graves SC (eds.). Supply