Manufacturing companies often complain about the difficulties they face in meeting their ... planning and control (PPC) software for their performance deficits.
Production Planning & Control, Vol. 16, No. 7, October 2005, 634–651
Stumbling blocks of PPC: Towards the holistic configuration of PPC systems H.-H. WIENDAHL*y, G. VON CIEMINSKIz and H.-P. WIENDAHLz yInstitute of Manufacturing and Management (IFF), University of Stuttgart, Nobelstrasse 12, 70569 Stuttgart, Germany zInstitute of Production Systems and Logistics (IFA), University of Hannover, Scho¨nebecker Allee 2, 30823 Garbsen, Germany
Manufacturing companies often complain about the difficulties they face in meeting their customers’ logistic requirements. Many blame the perceived inadequacies of their production planning and control (PPC) software for their performance deficits. The paper illustrates why this is only a partial view of the causes of the shortcomings. PPC software is just one of six configuration aspects of the entire PPC system. The authors argue that the configuration of the PPC aspects objectives, processes, objects, functions, responsibilities and tools has to be carried out methodically and consistently in order for the PPC system to function properly. The analysis of examples of so-called ‘stumbling blocks’ of PPC, inadequate configurations of one or several of the aspects, supports this claim. The paper closes with the proposal of a checklist that the authors suggest as a first approach to ensure the consistent configuration of PPC systems. Keywords: Production planning and control systems; Configuration aspects of PPC systems; Stumbling blocks; Configuration and operation of PPC systems; Actors in PPC
1. Introduction It is almost 30 years since Orlicky (1975) first described the material requirements planning (MRP I) algorithm. To this day the algorithm remains the kernel of many production planning and control (PPC) systems. Despite 30 years of progress in PPC theory and practice, and the definition of additional key functions, a large number of manufacturing companies remain unsatisfied with the degree of fulfilment of their logistic objectives. Recent surveys prove that companies still miss their logistic targets by a wide margin (Fraunhofer IPT Institute 2003, Wiendahl 2003a). This applies to the logistic performance measures of production—work-inprogress levels, throughput times and schedule reliability—in the same way as to those of stores: inventory levels, service levels and delivery delays. *Corresponding author. Email: Hans-Hermann.Wiendahl@ iff.uni-stuttgart.de
A historical review reveals various causes of the unsatisfactory logistic performance and, considering these, the solutions that a holistic configuration of PPC systems requires. In the past, critical evaluations of PPC methods identified the limited capabilities of computer hardware as the principal cause for the insufficient fulfilment of logistic objectives. These hardware limitations only allowed a step-by-step development of PPC algorithms. Due to this, the manufacturing resource planning (MRP II) algorithm that followed MRP I is characterised by the successive execution of its functions. As real situations in manufacturing companies seldom conform to the rigid assumptions that are underlying this algorithm, there were calls for a more realistic consideration of practical conditions. PPC research therefore concentrated on the development of new functions and algorithms (Plossl 1985, Vollmann et al. 1997) and neglected the analysis of the required preconditions such as an organisational framework for PPC (Kraemmerand et al. 2003).
Production Planning & Control ISSN 0953–7287 print/ISSN 1366–5871 online # 2005 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/09537280500249280
Stumbling blocks of PPC Over time, the remarkable progress of computer technology facilitated the application of more powerful planning software such as enterprise resource planning (ERP), supply chain management (SCM), advanced planning and scheduling (APS) or manufacturing execution systems (MES) (Stadtler 2002). All of these systems carry out a considerably larger number of functions than their predecessors. They apply sophisticated mathematical algorithms to solve multi-variable optimisation problems and can thus consider numerous planning restrictions simultaneously. Due to the immense complexity of the implementation of these large systems they often fail to produce the substantial logistic performance improvements the companies are hoping for (Davenport 1998). In contrast, other businesses preferred ‘simple’ PPC approaches. The increased popularity of just-in-time principles and Japanese management methods made companies avoid the application of software and focus on organisational aspects instead. They achieved remarkable performance gains, e.g. by the introduction of Kanban control cards (Soder 2004). The contrast between highly sophisticated, computerised PPC systems whose logistic performance is insufficient and simple, rules-based control mechanisms that achieve astonishing results made researchers and industrialists realise that the problems of PPC cannot be solved by more powerful software alone. There seem to be other causes of the described performance deficits, which had been neglected so far. The standard textbooks on PPC offer detailed descriptions of the theoretical foundations of the PPC functions, mainly mathematical models and algorithms (Plossl 1985, Fogarty et al. 1991, Hopp and Spearman 2000, Vollmann et al. 1997). However, instructions on the design and implementation of PPC systems are uncommon or not very detailed. Fogarty et al. (1991) emphasise that the choice of a logistic strategy should reflect the nature of the customer demands. The logistic strategy in turn determines appropriate manufacturing strategies and the corresponding feasible planning and control methods. Vollmann et al. (1997) stress the importance of mapping the planning and control processes specifically for the purpose of implementing PPC software to support the planning functions. The same authors provide a selection of the prerequisites of the system implementation. Otherwise, there are only case studies on MRP or ERP system implementations available that provide some indication on the critical success factors of PPC systems (see, for example, Akkermanns and van Helden 2002, Wiers 2002). In general management literature, important approaches are being discussed that aim to ensure the fulfilment of business objectives. Publications on PPC almost completely ignore these discussions, especially
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as far as the role of operational employees is concerned. Kaplan and Norton (1996) propose the balanced scorecards as a method to link business strategies to specific aspects of performance. Miles and Snow (1978) determine what types of business organisations lead to above-average levels of performance. Maslow (1987) and Huczynski and Buchanan (1991) explain the important influence that human motivation and employee involvement have on the performance of a business. Storey and Sisson (1993) discuss the effects of performance-related pay on the performance of a company and provide instructions on the effective design of remuneration systems. In order to ensure that PPC systems contribute to high levels of logistic performance, these general methods and approaches have to be adapted for the specific field of production management. Publications that transfer these approaches to the field of PPC have only recently been published (Wa¨fler 2003, Wiendahl and Westka¨mper 2004, Nyhuis 2004). According to the authors’ experience it is not only the neglect of above-mentioned important factors but also the lack of awareness of the correlations between separate factors that affect the configuration of PPC systems and lead to undesirable logistic performance deficits. These so-called ‘stumbling blocks of PPC’ are errors in the configuration of a PPC system as a whole. The symptoms of these stumbling blocks, insufficient fulfilment of logistic objectives, a lack of transparency and excessive efforts required, are easily identifiable. Often though those responsible for PPC on the operational level are not able to simply remove the stumbling blocks. On the one hand the interdependencies between their causes make a final analysis more difficult; on the other hand, the changes required by the situation can exceed the competencies of the operational staff involved. In most cases, only the managing directors can remove the causes of the stumbling blocks. Therefore, the objective of this article is to create a framework for the identification, analysis and removal of classic stumbling blocks of PPC: Section 2 defines the key terms of PPC. The PPC system, configuration aspects of PPC and stumbling blocks of PPC. . Section 3 describes typical stumbling blocks of PPC. The descriptions first identify their respective symptoms, analyse their causes and present possible solutions to remove the stumbling blocks. The practical examples included in the discussion of each stumbling block are based on the experiences the authors gained in industrial projects. The projects focus on the configuration of PPC concepts, the selection of suitable software tools and the implementation of both in practice. .
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Section 4 outlines a framework for the holistic configuration of a PPC system. It lays the foundations for a coherent and customised composition of the planning and control functions of manufacturing companies. . The conclusions in section 5 draw the insights together in the form of a questionnaire. The holistic configuration of a PPC system should consider the issues that the questionnaire raises in order to avoid the formation of stumbling blocks. .
This merges the aspects of the functions and data of PPC as well as its processes and responsibilities in an integrated model that provides a basis for the holistic configuration of PPC systems.
PPC system includes the three value added processes, Source, Make and Deliver, in accordance to the terminology of the supply chain operations reference (SCOR) model (Supply Chain Council 2004) (cf. figure 1a). The input and output stores of a company are thus subject matter of a PPC system in the same way as production. The PPC system crosses company boundaries: It allows for the requirements of customers and suppliers since, following supply chain management principles, the management of the storage processes takes the delivery performance of the suppliers as well as the demand behaviour of the customers into account. According to this definition, the term ‘PPC system’ comprises more than just the PPC software. The software is only the tool to plan and control the logistic process chain as well as the storage of production master data and feedback data.
2. Key terms of PPC The PPC system is the central logistic control mechanism that matches a company’s output and logistic performance to the customer demands. The task of the PPC system is to plan, initiate and control the product delivery of a manufacturing company as well as to monitor and, in case of unforeseen deviations, i.e. disturbances or order changes, to re-adjust the order progress or the production plans.
2.1 PPC system In the context of this paper, the term ‘PPC system’ denotes the entirety of functions and tools used for the planning and control of the logistic processes in a manufacturing company. The scope of application of a (a)
2.2 Configuration aspects of a PPC system On the basis of this definition, six configuration aspects of a PPC system can be distinguished (cf. figure 1b): The ‘logistic objectives’ of a company are situated at the heart of the PPC system. If necessary, these have to be differentiated for different departments of the company. . The ‘PPC processes’ determine the logical and chronological order of PPC planning and control activities. Thus they define the workflow of order processing in terms of the information flow along the logistic process chain. The activities related to the material flow follow the same logic, but are not directly a subject matter of the PPC system. .
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Figure 1. Definition of a production planning and control system. (a) Scope of application and (b) configuration aspects.
Stumbling blocks of PPC The ‘PPC objects’ are the planning objects of PPC. The most important objects are the articles (finished products, components or raw materials), resources (machinery and personnel) and orders (customer orders, spare parts orders, sample orders, etc.). . The ‘PPC functions’ define the activities that are required to plan and control the logistic processes in the stores and in production. The fundamental activities are the definition of local objectives and targets, forecasting and decision-making, providing feedback on order progress as well as continuous improvement. . ‘PPC responsibilities’ determine the positions—and therefore the members of staff—that are in charge of certain PPC activities. Conventional PPC systems ignore this organisational view as they operate on the assumption that responsibilities are organised by a central entity (see for example Hackstein 1989, Vollmann et al. 1997). . The five configuration aspects described above constitute the logical core of a PPC system. The purpose of the ‘tools for planning and control’ is to support the operational order processing by (semi-)automated PPC activities. This creates standards for the operational activities and relieves staff of time-consuming routine tasks. More time therefore becomes available for the required planning and control decisions. .
These configuration aspects serve as a theoretical basis to analyse and remove the stumbling blocks of PPC.
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Ideally, the symptoms can be traced back to a single cause. In this case, only one configuration aspect is affected and the mistake in the configuration is easily detected and removed. An example is the entry of incorrect planned capacity values into PPC software. If, for instance, the capacity of a bottleneck work system has wrongly been set at 18 hours per working day instead of the correct value of 16 hours, production overloads arise. This stumbling block can be easily removed by a simple correction of the planned capacity value. In cases where several cause-and-effect relationships influence or even amplify each other, the removal of stumbling blocks becomes more complex. Here, several of the configuration aspects are affected. Even though their symptoms are as apparent as for the simple stumbling blocks, their removal is a lot more difficult: It is necessary to, first, identify the relationships between the different causes. Secondly, the causes in different configuration aspects have to be changed simultaneously and in a co-ordinated way. Typically, this exceeds the competence of the operational actors so that their managers have to understand and remove the stumbling block.
3. Typical stumbling blocks of PPC The following examples describe the stumbling blocks with several causes that are most commonly found in industrial practice. Each explanation is divided into the description of the symptoms and the analysis of their causes. Measures that are used for the removal of the stumbling blocks follow. The examples are based on real situations in industrial companies.
2.3 Stumbling blocks of PPC The presence of stumbling blocks of PPC becomes apparent through symptoms such as the insufficient fulfilment of logistic objectives, a lack of transparency of order processing or an unnecessarily high effort of the staff involved in carrying out PPC activities. The term stumbling block exclusively applies to internal mistakes in the configuration of the six aspects defined above. Factors related to the external environment such as unreliable suppliers or literally ‘chaotic’ customers are not considered. The PPC system itself does not have any control over these factors. Nevertheless, the external factors represent requirements that have to be considered when designing the PPC system. An analysis of the relationships between causes and effects is required in order to detect and remove the stumbling blocks.
3.1 Stumbling block ‘missing positioning in system of logistic objectives’ The first example of a stumbling block of PPC highlights the importance of defining consistent objectives and of communicating the responsibilities for fulfilling the objectives clearly to the staff that plan production operations or carry them out. In PPC, one can often find conflicts between the logistic objectives work-in-progress level (WIP level), utilisation, throughput time and schedule reliability because they are neither compatible nor locally or temporally constant (Wiendahl 1995). Accordingly, one should never maximise or minimise the value of just one objective, but consider the simultaneous effects of measures on all logistic objectives. The nature of these conflicts has been recognised for some time
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(Gutenberg 1951, Plossl 1991). Nevertheless, many companies are not aware of their consequences. Frequently, production managers are trying to ‘optimise’ the utilisation of work systems concurrently to the throughput time. Detailed investigations demonstrate that such an approach is not target-oriented because ultimately no single objective of the optimisation can be defined. Substituting the minimum-cost objective for the logistic objectives does not resolve the conflict. Instead, companies should start by setting strategic objectives derived from the market environment (Ketokivi and Heikkila¨ 2003). Typical examples of such objectives are ‘Reduce throughput times by 50%’ or ‘Maintain a delivery reliability of 95%’. These objectives serve as the priorities, which dominate the trade-off that has to be reached with the remaining logistic objectives. The production operating curves are a proven methodology for the analysis of the interdependencies between logistic objectives and their consequences for PPC. They quantitatively describe the dependence of the objectives utilisation, throughput time and schedule reliability on the WIP levels in production and can easily be computed (Nyhuis and Wiendahl 2003). Figure 2 shows that the best possible target values for the different logistic objectives do not coincide at the same WIP level of a work system. A classical example of this phenomenon is the conflict between ‘short order throughput times’ and ‘high work system utilisation’ that was already mentioned. Whereas short work system throughput times can only be achieved at low WIP levels, high WIP levels are required in order to guarantee a high utilisation. This in turn leads to excessive throughput times. The situation requires a trade-off between the logistic objectives. Companies can achieve this by
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Figure 2. Logistic operating curves as a model of the interdependencies between logistic performance measures.
positioning their logistic processes at certain operating points on the production operating curves. The conflict between objectives described above only represents a stumbling block if those responsible ignore it in their day-to-day job. In a typical example, the managing directors of a medium-sized manufacturer of construction components required short throughput times to achieve short delivery times. At the same time, they demanded a high utilisation of expensive machinery in order to obtain a fast return on investment. The production operation curves clarify the conflict that the production department faced as a result (cf. figure 2): On the one hand, the objective of short throughput times requires a low WIP level in production (WIPTTPmin). On the other hand the objective of a high utilization necessitates a high WIP level (WIPUmax). The inconsistent directives of the directors are the cause for two stumbling blocks: In day-to-day business, concrete decisions concerning orders have to be taken. Conflicting management directives fail to determine the most important logistic objective. As a result a guideline for these decisions is missing. . As the management directives described above are contradicting in themselves the target values that are derived from them have to be as well. Therefore it is impossible for operational planners to take rational decisions. .
The production operating curves are helpful tools to analyse and remove both stumbling blocks: Initially, the curves explain the interdependencies between the logistic objectives and facilitate their relative prioritisation (step 1 of the logistic positioning). . The remaining target values follow from the value set for the most important objective. For example, the desired throughput time determines both the target utilisation as well as the target WIP level (step 2 of the logistic positioning). .
Taking a strategic decision, the directors regarded short throughput times as the most important objective. However, in order to implement the new management directives, further boundary conditions had to be considered. The machine operators still tried to maintain high WIP levels at the work systems so that they always see a work load in front of their machines and can reduce setup times by changing the sequence of orders. Obviously, this strategy also supports a high work system utilisation. At the same time it adversely affects throughput time and schedule reliability.
Stumbling blocks of PPC In order to maintain the desired prioritisation of the logistic objectives, management should take the following actions: Offer ‘qualification’ in production logistics to all relevant employees (including shop floor operators) and communicate the new priorities of the logistic objectives. . Verify the conformance of the logistic objectives with the ‘interests’ of the employees. In particular, management has to ensure that compensation schemes effectively support the objective of short throughput times. .
The following section describes how those involved influence production planning and control, and how their decisions impact on the fulfilment of logistic objectives.
3.2 Stumbling block ‘divergent stakeholder interests’ The second stumbling block confirms the importance of the consistency between the logistic objectives and the PPC staff who have the responsibility for meeting the targets. It also stresses the fact that staff has to be qualified in order to carry out PPC functions. In an empirical study on the implementation of ERP systems, Amoako-Gyampah (2004) came to the conclusion that different levels of the management hierarchy have different perceptions of the system to be introduced. It is therefore essential that the managing directors not only provide all future users with adequate training in the application of the new system, but that it is equally important they make efforts to convince staff members of the benefits of the change and of the necessity to utilise the new system to achieve enhanced business objectives. The report by Wiendahl et al. (2002, 2005) on the introduction of a Kanban control is a prominent example for the potential for conflicts between such business objectives and the individual objectives and interests of production employees. In this case, production management wanted to reduce throughput time and WIP levels significantly. The central planning department was responsible for the design of the Kanban system and the setting of its parameters. On the basis of customer demands and target replenishment times the planners also calculated the number of Kanban cards required. The production department was briefed about the changes and a subsequent trial run passed without problems. The company therefore regarded the implementation of the new production control system as a success.
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It came as a surprise that the production was not able to sustain the aspired improvements for more than a short time after implementation of the new control system. Rather, both the values of WIP and throughput times soon rose to old levels again. The detailed analysis of the production department that was initiated as a consequence, revealed insufficient consultation with the production operators. The operators pursued the objectives ‘job security’ and ‘stable order processing’ by stockpiling orders for uncertain times in the future. This leads to unnecessary safety stocks, permanent changes to the order sequence and decreasing schedule reliability. Obviously the pull principle that is underlying the Kanban control does not conform to these interests of the production operators: Kanban enforces temporary idle times for most work systems. In order to counteract this, the operators added copied Kanban cards to the Kanban control loops to raise WIP to the previous levels. Thus, they apparently resolved the conflicts between the objectives of the company and their own individual objectives. Production management only realised that the unwanted modifications had been made and understood the exact causes of the modifications after the analysis of the Kanban control system. The example highlights the prerequisites for a sustained successful implementation of the new control system; thorough qualification of all staff involved and an incentive system that emphasises the objectives of due-date oriented order processing (in order to avoid order sequence modifications) and flexible working hours (in order to guarantee processing on demand) rather than promoting the conventional objective of high resource utilisation.
3.3 Stumbling block ‘missing responsibility for inventories’ The third stumbling block illustrates the consequences of a lack of coordination of the responsibilities for the PPC processes and objects. They result in an insufficient fulfilment of the logistic objectives. Often, there is no clear dividing line separating one area of responsibility from another. Typical symptoms are high inventory levels of purchased components and finished products, or recurrent discussions on the binding effect of orders and their reliable fulfilment. The company described in this section produces make-to-order machines of medium complexity. Depending on the customer requirements, this may include engineer-to-order operations. A detailed analysis was initiated by the managing directors who were
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dissatisfied with the high inventory levels of purchased components and finished products, and the uncertainties caused by sudden changes of due-dates or engineering changes. Figure 3a shows parts of the order processing chain. Figure 3b indicates the problems resulting from an unclear definition of interfaces: The production is responsible for the material flow from the start of the production order (i.e. printing of order documentation) to the final operation (i.e. input to store). This includes the responsibility for throughput times and WIP levels. Subsequently, the finished products are handed over to the sales department, either to be delivered immediately or to be stored in the finished product store. The purchasing department has the responsibility for all purchased components. However, there is no responsibility defined for the target inventory levels for finished products. The company neither deemed it necessary to define nor to regularly monitor them, because final assembly should not take place before there is a customer order. This should have prevented finished products from being stored. Recurring appeals to cut inventory levels remained without effect. Instead, purchased component and finished product inventory levels were steadily growing. In addition, staff in the shipment area complained about too small dispatch and storage spaces. The root cause analysis showed: .
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Two issues have to be solved to improve the interfaces: 1. Placing of orders (information flow): Does the person who acquires new or altered information directly benefit or have a quantifiable advantage from passing it on? Would it be to his/her disadvantage if he/she did not pass it on? 2. Delivery of orders (material flow): Does the supplier have a direct, quantifiable benefit from a timely delivery to his/her successor? Would a late delivery be to his/her disadvantage? In our example, a handover deadline was fixed for the transfer of products to the shipment area, which is within the responsibility of the sales department. The resulting deadlines are realistic, because the calculation of the order flow includes a capacity check.
Initially, customers insist on the machines being delivered as soon as possible. Near completion of the order, they tend to postpone the delivery date when they realise that the machine is not needed now, e.g. because of building delays. (a)
The actual start date of production is delayed relative to the planned start date. The reasons are product engineering changes due to changes in customers’ requests or design modifications by the engineering department.
From a production point of view, the sales department places fixed ‘orders’. The fact that products are handed over without transferring inventory responsibility to the sales department is the reason why the sales department experiences neither an advantage nor any disadvantage if it fails to pass on the postponement of customer due dates. . Likewise, from a sales point of view, the ‘promise’ made by production seems to be binding. But production has no fulfilment risk: delivering .
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Figure 3. Stumbling block ‘missing responsibility for inventories’. (a) Status as planned and (b) actual status.
Stumbling blocks of PPC goods on time means production is cleared of its responsibility, without having to worry about any penalties when orders are completed behind schedule. A possible solution may be to add the finished product store to production’s responsibility. This initiates the necessary improvements out of self-interest. An analysis of the interface between production and purchasing shows similar results: The required due date for purchased components is calculated by backward scheduling, before passing it on to the suppliers with a safety lead time. Production takes no responsibility for inventory levels from the planned start but only after the order is actually started. This is why production is merely interested in passing on information regarding production orders being pulled ahead but not about orders postponed. Frequently, the problem is solved by transferring the responsibility for material dispatch and material inventory responsibility to production.
3.4 Stumbling block ‘inconsistent responsibilities for functions’ If the responsibilities for PPC objects, functions and objectives are defined inconsistently, top management is obliged to spend a high effort on resolving unnecessary disputes as the following example exemplifies. One of the principal tasks of management is to clearly define the responsibilities and assign the functions within a company. It is generally accepted that appropriate objectives have to be defined so that responsibilities within the organisation are consistent and therefore the functions be carried out reliably (Kaplan and Norton 1996). Unfortunately, reality often rather reflects the informal organisation, i.e. the power structure among the persons concerned. In a company with 300 employees, the ‘right way’ to fulfil functions and to accomplish the given objectives was the subject of heated discussions among the three departments of dispatch, production and logistics: Dispatch is responsible for order release, production takes on capacity control and sequencing at the work systems, and logistics is responsible for promising delivery dates, thus being partially in charge of order generation. Each department has its own system of objectives, the priorities differ: The primary objective of dispatch are ‘short throughput times’, the principal goal of production is a ‘high utilisation’, while the top priority of logistics is a ‘high schedule reliability’. All these objectives were quantified by targets. The
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resulting conflicts are illustrated by the following disputes: Dispatch aims to release orders at the latest possible moment to meet the objective of short throughput times. Whenever demand is low, intense debates with production are unavoidable. Production wants orders to be released much earlier to maintain a high utilisation. . To meet the objective of high schedule reliability and ensure that customers receive their products on time, logistics strives for realistic delivery promises. It sets the planned start and finish dates as well as the sequence of orders in accordance with these promises. As soon as demand rises, however, the available capacities are not sufficient to keep the promised delivery dates. Hence, production is urged to raise capacity. If this is not possible, the dispatch department is asked to release orders at an earlier point in time. . Usually, the parties concerned are not able to reach an agreement. Therefore, often top management is asked to solve the conflict and decide upon which orders to release or which to speed up. Necessarily, the set objectives are missed. .
A helpful framework to remove this stumbling block is Lo¨dding’s (2004) model of manufacturing control. Its basic idea is to combine the functions of manufacturing control with the objectives of the PPC system. Thus, it becomes possible to assess whether the responsibilities for functions and objectives are consistently defined. He defines the following four functions (cf. figure 4a): Order generation determines the planned input and output, as well as the planned order sequence. . Order release determines when orders are released to the shop floor (actual input). . Capacity control determines the available capacity in terms of working time and the number of staff assigned to work systems, and thus affects the actual output. . Sequencing determines the actual sequence of order processing for a specific work system, and thus affects schedule reliability. .
These functions affect the three manipulated variables ‘input’, ‘output’ and ‘order sequence’. The discrepancies between two manipulated variables lead to the observed variables of manufacturing control (cf. figure 4b): The start deviation results from the difference between planned input and actual input. . The WIP level results from the difference between actual input and actual output. .
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Figure 4. Model of (a) functions and (b) logistic interdependencies in manufacturing control. Adapted from Lo¨dding (2004).
The backlog results from the difference between planned output and actual output. . The sequence deviation results from the discrepancy between actual and planned sequence. .
The observed variables affect the objectives of PPC described above, i.e. throughput time, WIP level, utilisation and schedule reliability. Figure 4b shows the interdependencies connecting functions, manipulated variables, observed variables and objectives to each other. The functions define the manipulated variables, the observed variables result from the discrepancies between two manipulated variables, and the logistic objectives are determined by the observed variables. As a basic principle, conflicts arise when one department takes the responsibility for a specific objective when the accomplishment of this objective is also affected by another department. These conflicts obviously cannot be resolved by the persons involved. Figure 5 illustrates how the responsibilities for objectives and functions are defined by the described company: .
The first conflict arises between the production and the dispatch departments: Although order release (via actual input) and capacity control (via actual output) affect the objectives of throughput time and utilisation, the responsibility for objectives and functions is not united under ‘one authority’. Accordingly, situations, in which the achievement of an objective depends on the decisions of the other department, require a higher authority to make the final decision.
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The second conflict arises between the production and the logistics departments: Production affects the objective of schedule reliability via capacity control (actual output) and sequencing, whereas logistics impacts the objective via order generation. Again the responsibility for objectives and functions is not united under ‘one authority’. This inevitably leads to a permanent conflict as described in the above paragraph and requires a higher authority to solve each case. For production to call for an earlier order release to ensure utilisation even complicates the matter, as a third party, i.e. dispatch, has to be considered.
To remove this stumbling block the responsibility for the complete order processing chain must be put ‘into the same pair of hands’. An order management centre could fulfil this role. Alternatively, it is possible to divide the order processing chain into sub chains, in which the responsibilities for objectives and functions are combined.
3.5 Stumbling block ‘insufficient quality of feedback data’ The insufficient quality of feedback data reported in the following case is a symptom of the lack of integration of all PPC functions in the tools for planning and control. Data quality has recently been identified as one of the important factors in the configuration of PPC system (Xu et al. 2002). All purposeful and successful planning
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Stumbling blocks of PPC Actual input Dispath
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Figure 5. Stumbling block ‘inconsistent responsibilities for functions’.
and control depends on a complete, consistent and current data basis for all planning, control, execution and performance measurement activities (Wiendahl et al. 2003a). Besides the production master data, the production feedback data are especially important for this purpose: Feedback data represent the inputs for the logistic ‘performance measurement’ carried out at the end of a production planning period. Deviations between the planned and actual values of logistic performance measures lead to new control decisions or the adjustment of target values (see section 3.6). . For day-to-day business the continuous logistic ‘performance monitoring’ is more important. The deviations between planned and actual order progress detected by this function have to be corrected by immediate control measures in order to make sure that promised or planned due-dates can be maintained despite order changes or inevitable disturbances. .
There is a range of possible causes for the insufficient quality of feedback data, of which the IT structure in a manufacturing company is one of the more significant reasons. In a survey carried out by the Fraunhofer Institute for Systems and Innovation Research in 2001, 60% of the companies responded that there is no hardware connection between the production data acquisition (PDA) software and the remaining IT structure (Beckert and Hudetz 2002). A timely and fast
intervention of production control in production is thus impossible. At times there is a complex structure of mutual dependencies that is underlying the symptoms. This is exemplified by the following example: In the manufacturing company considered, the feedback data were characterised by inconsistencies that resulted from a substantial delay in recording the data in the PDA software (only 75% of operations showed a positive throughput time). However, the actual processing times matched the standard processing times relatively accurately. After the introduction of new planning software the problem disappeared within a period of six weeks. A preliminary analysis showed that the feedback data were only used for the controlling of costs but not for the ongoing monitoring of the order progress. A second ‘manual feedback system’—local inspections by the foremen—provided the feedback information required to control the order progress in time. As the feedback data were not immediately incorporated in the next production plan, the operators did not recognise the benefit of the plausible and immediate provision of feedback data. The regular appeals by the production managers to increase the quality of the feedback data therefore did not have any effect. The PPC cycle shown in figure 6 provides a basis for a detailed analysis of the situation. It consists of a logical sequence of the activities of production planning and control. Based on insights from decision theory, the
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H.-H. Wiendahl et al. In the chosen example, the fundamental causes of the stumbling block were due to the open control loop between decision and execution activities:
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At the start of the project the feedback between the ‘Do’ and the ‘Re-plan’ phases malfunctioned. The capacity planning function was missing from the software in use, which thus produced unrealistic production plans. The foremen had to correct the production plans produced by the software by extensive manual interventions. The low quality of the feedback directly resulted from this: As the operators did not see a direct personal benefit from feeding events back plausibly and promptly, they regarded the activity as a pointless exercise leading to excessive effort. . The introduction of new planning software interrupted this spiral of errors. The operators realised that the quality of the production plans (in the shape of their own dispatch list) depended on their provision of accurate feedback data. .
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Figure 6. The PPC cycle as a model for management and execution activities.
cycle splits the four phases of the Deming cycle (Plan, Do, Check, Act) (Deming 1992) into eight separate steps (Balve et al. 2001, Wiendahl 2002). The setting of targets during the ‘Act’ phase marks the beginning as well as the end of the PPC cycle. The ‘Plan’ phase contains three steps that prepare the actual production process: ‘Forecast’ determines the necessary information inputs of production planning, i.e. the probable demand for, as well as the supply of, finished products, materials and capacities, . ‘Allocate’ directly relates the demand for articles and resources to their supply, . ‘Decide’ defines the production plan. .
The actual manufacture of the products is subject of the ‘Do’ phase. The ‘Check’ phase has to ‘Collect’ information about the order progress in production and to ‘Evaluate’ this in comparison to the original plans. Differences between planned and actual require corrective control measures. These are also referred to as ‘Re-Plan’. If the PPC system regularly fails to achieve the set targets, systematic errors may be the cause. For this reason, the ‘Re-Act’ phase completes the cycle. The ‘Learn’ step should help to avoid the performance shortcomings in the future. PPC methods and the various planning and control parameters contained within them have to be set consistently and in accordance to the logistic objectives. The required accuracy of planning predetermines the tolerable delay between production events and their feedback. An effective performance monitoring is therefore a prerequisite for realistic production plans (Wiendahl 2002).
This analysis explains why the problem with the insufficient quality of feedback data disappeared in this short period.
3.6 Stumbling block ‘errors in PPC parameters’ As is shown by the sixth example, the omission of a key PPC function results in a stumbling block because parameters in the tool for planning and control cannot be set correctly. One of the functions of PPC software is to effectively support dispatch activities. Dispatch parameters in the PPC software serve as the basis of (semi-)automated dispatch decisions. The effectiveness of these decisions therefore depends on the actors’ understanding of logistic processes on the one hand and on the appropriate setting of dispatch parameters on the other. The central PPC planning parameters include the planned values for the offset and replenishment times, order throughput times and operation throughput times. With the help of these time-based parameters purchase orders are placed and production orders are scheduled. Hence the parameters represent the logic foundation of the entire due-date structure in a company. Figure 7 shows that the successive scheduling runs of the MRP II approach utilise scheduling parameters at different levels of aggregation. Between the level of an entire purchasing process (parameter: replenishment time) and the disaggregated level of separate operations (operation and inter-operation times) there are normally two levels of aggregation: one for the material
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Figure 7. Classical scheduling parameters of PPC.
requirements planning and one for the capacity requirements planning and scheduling. Therefore there are two relevant types of stumbling blocks:
stumbling block in this context. The company considered did lack this function: The feedback data—order master data and due dates—have to be recorded at all work systems on the shop floor (step ‘Collect’ in the PPC cycle in figure 6). Subsequently, order throughput times and other logistic performance measures can be calculated from these. . Subsequently, logistic performance measurement has to determine the accuracy of the planning parameters. This is achieved by comparing the throughput time parameters set for the scheduling function of the PPC software with the actual values measured in production. If necessary, the parameters have to be adjusted bearing in mind the logistic objectives (step ‘Learn’ in the PPC cycle in figure 6). . Only the introduction and regular execution of the PPC cycle guarantees the accuracy of the throughput time parameters. The procedure described equally applies to all other PPC planning parameters. .
1. Inconsistent parameters: The scheduling parameters at different levels of aggregation are inconsistent (e.g. the offset of a manufactured component is equal to 3 weeks, the sum of the throughput times of all operations included in the manufacturing order is equal to 4 weeks). 2. Unrealistic parameters: The values of the scheduling parameters are normally not maintained at the planned values in reality (e.g. the mean planned throughout time of manufacturing orders is equal to 5 days whereas the actual mean throughput time is equal to 7 days). Manufacturing companies often underestimate the significance of correct parameter setting. The scheduling parameters are merely estimated or derived from historic data. In this way, a tool manufacturer used a mean planned throughput time value of 27 working days for scheduling manufacturing orders. This value was based on the experience of the production foreman. The actual mean value of the throughput time for the manufacturing orders was equal to 32.5 working days. The differences between plan and actual mean values occurred due to the production bottleneck: a long operation throughput time of a coating process. The prerequisite for realistic planned values is the knowledge of the actual values. The lack of a performance monitoring function constitutes an obvious
Adjusting parameters may lead to another stumbling block: For the purpose of replenishing the finished products store, the order dispatch function assumed a throughput time of 27 working days. Backward scheduling runs generated the required production orders based on this assumption. Thus, the difference of 5.5 days between the planned and the actual throughput time affected the schedule reliability. The
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production was running into backlog. For this reason, companies have to be aware not to enter the vicious circle of production control when modifying planning parameters. The next section explains how to act correctly when these modifications become necessary.
3.7 Stumbling block ‘lack of logistic understanding’ Due to a lack of logistic understanding, many manufacturing companies fail to make the correct connections between decisions taken in the PPC functions and their effect on the degree of fulfilment of the logistic objectives. How a production system deals with logistic issues and how this affects planning and control has been the subject of discussion for some time, especially in view of the familiar shortcomings of the MRP approach and the lack of logistic understanding on the part of the users of PPC. The vicious circle of production control is a particularly illustrative example of how little is known about the actual interdependencies between manipulated and observed variables (cf. figure 8). In the USA this circle was first described by Mather and Plossl (1978), while Kettner (1981) and Wiendahl (1995) explained its consequences to the German audience. The vicious circle sets out from the mistaken conclusion that schedule reliability is poor because the planned throughput
Throughout times and their variation increase
times are too short. This is shown in the throughput diagram in figure 9a. When increasing the values of the parameters in the backward scheduling run, the orders will be released to the shop floor much earlier. As orders cannot be started ‘in the past’, the input curve takes a leap (one-off load surge), making the WIP levels at the work systems and hence the length of the order queues grow (cf. figure 9b). This implies, on average, longer waiting times and longer throughput times of orders, along with an increased variation of throughput times (Wiendahl 2002). As a result, the schedule reliability is decreasing and completing important orders on time is only possible by means of rush orders and costly expediting exercises. The vicious circle is spiralling upward to stabilise at a level where the amount of work pieces stored as work-in-progress exceeds the storage capacity (Wiendahl 1995). The correct logistic analysis would be as follows: The backlog is the actual cause of the due-date deviation of orders (cf. figure 10a). This backlog cannot be reduced by increasing the planned throughput times, but by temporarily increasing capacities or outsourcing work. Figure 10b shows the effects of this intervention: From the ‘present day’ the backlog will gradually decrease. As a result, adherence to the planned due dates is improving, and from a certain point in time planned and actual output are matched. However, such reactions call for flexible capacities (Wiendahl 2002). Outsourcing work for some time basically has the same effect. However, compared to an increase of capacity the impact will be delayed (cf. figure 10c).
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Figure 8. Stumbling block ‘lack of logistic understanding’ causes vicious circle of PPC. Adapted from Plossl and Kettner.
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Figure 9. Inadequate logistic reaction to interrupt vicious circle of PPC. Throughput diagrams for (a) initial situation and (b) for an increase in planned throughput times.
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do not conform. This inconsistency shows analogies to fluid mechanics (Wiendahl 2003b): Throughput time planning based on mean values assumes that the order stream resembles a steadily flowing river (a so-called laminar flow of orders). Only when throughput time variation is very low, schedule reliability is sufficient. . If the order stream resembles a mountain torrent (comparable to a turbulent flow of orders), the focus has to be on the individual order. The individual planning of throughput time ensures schedule reliability despite strongly varying throughput times. .
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Such a situation allows for two alternatives: Backlog Time
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Figure 10. Adequate logistic reactions to interrupt vicious circle of PPC. (a) Initial situation, (b) temporary increase in capacity and (c) temporary outsourcing.
A similar effect may be achieved by deferring make-tostock orders (orders not related to a customer request) or rejecting customer orders, though the latter might have a negative effect on the market.
3.8 Stumbling block ‘inadequate logistic guidelines’ The stumbling block described below shows the consequences of a lack of consistency between the PPC functions and the process, which the functions are meant to control. An important instance of wrong PPC parameter setting is the formulation of inadequate logistic guidelines. In this case, the planned throughput times entered in the PPC software are realistic and match the mean value of the production throughput times. However, the variation of the actual throughput times is higher than the planned tolerance. Hence, the available planning functionality (throughput time planning based on mean values) and the actual throughput time performance (high variation of throughput times)
On the one hand, logistic turbulences might be inevitable. Individual throughput times are necessary and the software must be adapted accordingly. . On the other hand, the steady-river scenario is feasible. Orders are processed according to the FIFO rule (or maximum slack). A low variation of throughput times ensures the planned schedule reliability. .
The relationship between logistic requirements and logistic capabilities determines the choice of a logistic guideline. The requirements depend on the allowed due-date deviation (tolerance requirements), the demand fluctuation (flexibility requirements) and the delivery time (speed requirements), cf. figure 11 (Wiendahl et al. 2002, 2003b): Tolerance requirements: Is the planning tolerance set for a value such as throughput time, smaller than the actual variation? . Flexibility requirements: Do the fluctuations in demand exceed capacity flexibility? . Speed requirements: Do heterogeneous delivery times require heterogeneous throughput times? .
If the requirements exceed the capabilities, it is necessary to apply individual throughput times for each order. Practical experience shows that missing one of the three requirements is sufficient to increase the variation of throughput times. In most cases, this is due to varying order priorities or sequence changes meant to avoid setup times. Accordingly, sufficient planning tolerances, little demand fluctuations and homogeneous delivery times allow for order throughput times to be based on mean values. The same applies vice versa: Heterogeneous delivery times, considerable fluctuations in demand and tight planning tolerances call for the individual planning and control of orders.
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Following a flow-oriented guideline makes it easier to forecast the throughput time and thus to determine the delivery date. Traditional PPC methods support this approach, too. However, strong fluctuations in demand and unforeseen events make it difficult to provide capacity according to need. This is why it places high demands on flexible capacities and predictive performance monitoring to achieve the ideal of a steady order stream.
4. Configuration of the production planning and control system Many industrial companies are dissatisfied with the degree to which they fulfil their logistic objectives: throughput times and inventory levels seem to vary uncontrollably; promised due-dates can only be adhered to by use of costly expediting exercises. For this reason, there are controversial views about the potential of PPC software in academia and practice. The examples of stumbling blocks of PPC presented above show that the ever-present demand for improved software with more powerful algorithms is not always justifiable. Rather, it is the inconsistent configuration of the aspects of PPC that affects the fulfilment of
logistic objectives. A holistic (re-)configuration of the PPC system has to consist of the following three stages: Initially management has to determine the logistic strategy, i.e. the logistic performance that it wants to offer to the customers. This includes the prioritisation of external logistic objectives and the trade-off between internal logistic target values. Manufacturing companies have to ensure that the logistic strategy matches their manufacturing vision which predetermines the design of its production systems (Riise and Johansen 2003). In fact, companies should ideally formulate manufacturing and logistic strategies simultaneously and also design production systems and the related PPC system in parallel. . The technical concept of the PPC system has to be built on the basis of the logistic strategy. The basic logistic configuration has to ensure that the configuration aspects of PPC—processes, objects, functions and responsibilities—are consistent with each other as well as the achieved prioritisation of logistic objectives. The selection of suitable production planning and control methods and algorithms facilitates a partially or fully automated materials and capacity dispatch. The analyses of the stumbling blocks of PPC offer instructions on how to .
Stumbling blocks of PPC avoid inconsistencies of the configuration aspects of the PPC system. . The third stage is the implementation concept of the PPC system. This includes the selection of PPC software that is capable of supporting the technical concept, the setting of all relevant parameters in the PPC software and the development of a suitable implementation strategy that includes the qualification of all staff. Case studies confirm the necessity of a role-specific training-on-the-job implementation (Wiendahl and Westka¨mper 2004). It is not sufficient to configure a PPC system once on implementation. As a rule, changes to the internal and external situation of the company require a periodic verification in accordance with the PPC cycle shown in figure 6. This ensures that the current configuration, the methods used and the parameters set are still suitable. Two types of changes can be distinguished: Abrupt changes, such as the introduction or withdrawal of competitive products, the development of new technologies or other changes to the market environment are relatively easy to detect. In such cases, the need for action is obvious. From a logistic perspective there is no need for new methods or tools for detecting such changes. Timely indicators of market or technological changes are desirable. These, however, are research issues for general management disciplines. . Creeping changes are much more difficult to detect. Step-by-step adjustments of market volumes, delivery or replenishment times hardly attract the attention of those responsible. However, for the configuration of PPC systems this type of change is much more critical because it necessitates the continuous verification and adjustment of the chosen configuration in parallel to day-to-day business. It can be compared to the sharpening of tools that a good craftsman regularly carries out. .
5. Conclusions The discussion of the stumbling blocks presented above highlights the importance of a holistic configuration of PPC systems. Although section 4 outlines the main phases of a methodical PPC configuration process, a fail-safe procedure has not been developed in detail yet. However, as focussed questionnaires are a way of assessing the appropriateness of management and production system designs (Barnes and Rowbotham 2003), the following questions can be recommended as part of a ‘quick-check’ to assess the
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suitability of a chosen configuration. The questions are separated into five sections: Objectives and stakeholder interests: Have the logistic objectives been defined and are the objectives consistent? Is their degree of fulfilment being monitored? . Is someone responsible for the fulfilment of the objectives? . Have the logistic objectives been matched to customer demands and are they consistent with the performance targets for the employees on all hierarchical levels (the stakeholders)? .
Logistic guideline and PPC methods: Does a logistic guideline exist? Do the planning and control methods used match the logistic guideline? . Is there a mechanism that ensures the consistency of logistic guideline, logistic positioning and the planning and control methods used? Is someone responsible for this mechanism? . .
Order processing chain and responsibilities: Have the separate process steps of the order processing chain been defined? . Has the responsibility for each step been assigned? . Have the interfaces between the responsibilities been defined unambiguously? . Do those who have to fulfil the logistic objectives have an adequate level of authority for making decisions? .
Data quality and parameter setting: Is there a mechanism that ensures the accuracy of master data and feedback data? Is someone responsible for this mechanism? . Are the values of the planned throughput times consistent across all three scheduling levels of the PPC system (long-range and intermediate-range planning, and short-term control)? . Is there a mechanism for continuously checking, and adapting if necessary, the accuracy of PPC parameters? Is someone responsible for this mechanism? .
Qualification of employees and logistics audit: Do all staff involved in the logistics function understand the fundamental interdependencies between the logistic objectives, the manipulated variables and the observed variables? Is there a regular refresh activity? . Does a logistics audit form part of the quality management system? .
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From a practical point of view, the answers a company provides to the questions above directly indicate areas that the company has to improve in order to achieve a holistic PPC configuration and to avoid the stumbling blocks described. From a scientific point of view, further research has to be carried out in order to adapt the organisational and human aspects of existing performance management theories to the field of production management and integrate them into the framework for configuring PPC systems.
Acknowledgements This article reports on research activities of the project ‘Modellbasierte Auftragsmanagement-Gestaltung’ (Model-based Configuration of the Order Management Process) that is funded by the German Research Foundation (DFG) under registration WI 2670/1.
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Hans-Hermann Wiendahl studied Industrial Engineering at the Technical University in Berlin. He has worked at the Fraunhofer Institute for Manufacturing Engineering and Automation (IPA) and at the Institute for Industrial Manufacturing and Management (IFF), University of Stuttgart, since 1996 where he held positions as researcher, department manager and now technical manager ‘Order Management’. He completed his PhD under the supervision of Professor Westka¨mpfer and is now working on his habilitation thesis. His main research interests are in production management, especially PPC, as well as in the selection and implementation of ERP and MES systems. He was responsible for numerous research and industrial projects and has published on these subjects extensively. Gregor von Cieminski holds a degree in Manufacturing Sciences and Engineering from the University of Strathclyde in Glasgow. He is a research assistant at the Institute of Production Systems and Logistics (IFA) at the University of Hannover. As a member of the production management research group his interests are in the fields of logistic modelling of production processes and supply chain management. He has published several articles on these subjects in scientific journals and conference proceedings.
Hans-Peter Wiendahl studied Mechanical Engineering at the Engineering School in Dortmund, at the RWTH in Aachen and MIT (USA). Under the supervision of Professor Opitz, he completed his PhD in 1970 and his habilitation thesis in 1972. Until 1979 he was manager of planning and quality at Sulzer Escher Wyss GmbH in Ravensburg before becoming the head of paper machinery design for the same company. He became professor and head of the Institute of Production Systems and Logistics (IFA) at the University of Hannover in 1979 and held this position until 2003. His main research interests are in production management, factory planning and production systems. He is the author and publisher of numerous books and articles on these subjects.
