FUNCTIONAL REQUIREMENTS AND DESIGN STEPS FOR A KEY METRIC SYSTEM TO PLAN RAMP-UP COSTS Herwig Winkler*, Michael Slamanig* and Bernd Kaluza* * Alpen-Adria-Universitaet Klagenfurt, Department of Production/Operations Management, Business Logistics and Environmental Management, Universitaetsstr. 65 - 67, 9020 Klagenfurt / Austria E-Mail:
[email protected]
Abstract: The successful execution of ramp-ups is imperative for the financial performance of companies. In 2004, almost 60 per cent of all ramp-ups missed their economic targets. Currently, ramp-up costs are often insufficiently planned and controlled. This leads back to the fact that their drivers are opaque, and that methods and/or instruments supporting the planning and controlling of ramp-up costs are entirely missing. This contribution presents the requirements for a key metric system to improve the planning and control of ramp-up costs. Furthermore, we systematically demonstrate how this metric system can be built up by following a four step approach. Finally, also the benefits of using the key metric system are investigated. Copyright © 2007 IFAC Keywords: cost reduction, low cost ramp-up, key metric system, planning, controlling
1. INTRODUCTION Shorter product life cycles, an increasing diversification of demand and the increasing intensity of global competition have forced companies to strive for a more rapid and efficient introduction of new products (Abele, et al., 2003; Teckemeier and Bauer, 2005). Therefore, the execution of production ramp-up, the period between the end of product development and full capacity production, strongly impacts the economic success of a new product (Pelousek and Bauer, 2005). In recent years, a great deal of research has been conducted on the analysis and solution of different problems within ramp-up management (Terwiesch, et al., 2001). Despite these efforts, many important problems have not yet been solved. According to an international study conducted by the University of St.Gallen, in 2004 almost 60 percent of all ramp-ups carried out in the European automotive industry missed their technical and/or economic targets (Fitzek and Kampker, 2005). One of the unsolved problems within ramp-up management that drives this alarming figure concerns the planning and control of ramp-up costs. A short time prior to the launch of the new product, there is no room for error left, which often leads to a disproportionate increase of ramp-up costs in cases of trouble shooting (Di
Benedetto, 1999). Additional ramp-up costs seriously influence the financial performance of a company and can cause large yield-losses in the last resort. We define ramp-up costs as the sum of all costs incurred during the ramp-up period, with the exception of investments which are directly allocated to the product via depreciation (Möller, 2002). In order to allocate ramp-up specific costs, it is useful to distinguish between several types of ramp-up costs. The main types of ramp-up costs are planning costs, costs for building and testing the production and logistics systems, costs for qualifying employees, production- and logistics-related ramp-up costs (quality costs, coordination costs and operating costs) and costs for adaptation and reorganization. Currently, ramp-up costs are often insufficiently planned and controlled. This is due to the fact that the conventional methods of cost planning used in a well-known volume production environment are not appropriate to meet the specific requirements of a production ramp-up (Möller, 2002). In order to avoid cost explosion during the ramp-up, costs have to be carefully planned. This necessitates adequate methods and/or instruments. Specific methods and/or instruments supporting the process of planning and controlling ramp-up costs are entirely missing (Schuh, et al., 2002). We argue that key metric systems are good instruments for planning and
controlling ramp-up costs because of their simplicity to use, their adaptability to changes within the rampup and its underlying conditions, and their ability to illustrate cause-and-effect relations (CER). 2. REQUIREMENTS FOR A PLANNING AND CONTROLLING KEY METRIC SYSTEM Due to the specific problems associated with the planning of ramp-up costs, an effective key metric system has to fulfil a set of functional requirements. The main goal of the key metric system is to assist and coordinate the cost planning process of a rampup by providing all the cost information required. In order to achieve this objective, the complexity of the planning task has to be reduced by increasing costtransparency within the ramp-up. Cost-transparency is reached by identifying and defining the main processes within the ramp-up phase. Main processes are, for example, pilot production, manufacturing start up and the qualification of employees. Knowing the ramp-up processes helps to reveal the causes of ramp-up costs, namely the cost drivers and the correlations between them (Slamanig, 2006). Thus, a better understanding of the ramp-up and the costs incurred within the single process is created. According to this, the first functional requirement which an adequate key metric system has to fulfil is to provide relevant cost information to establish a detailed and comprehensible basis for decision making and planning. In addition, changes in the design parameters of the ramp-up can be analyzed in consideration of their effects on ramp-up costs. Changes during the planning process may occur because of top-down budget-constraints which result in restrictions concerning, e.g., the ramp-up capacity, the product design and/or functionality as well as the ramp-up production volume. The second functional requirement is to provide support in coordination. Reducing planning complexity by dividing the whole ramp-up into its single processes necessitates coordination in order to bring the functional plans like the production cost plan and the logistics cost plan together into one coherent overall ramp-up cost plan (Slamanig, 2006). Thus, coordination contributes to system thinking as several tasks and problems within the ramp-up cost planning process are not treated independently anymore, but rather are considered in their overall context. The third functional requirement is the capability of supporting communication and learning processes within the ramp-up cost planning process. Besides the qualification and motivation of employees, rampup specific know-how is one of the main factors for improving the quality in planning ramp-up costs as well as planning reliability (Möller, 2002). Problems concerning ramp-up specific know-how could be traced back to two basic deficits (Housein, et al., 2002). The first type is deficits resulting from insufficient communication and coordination within the planning process. On the other hand, deficits arise from a failure to transform implicit ramp-up
know-how into explicit structured knowledge (Terwiesch and Xu, 2004). Therefore, an adequate key metric system has to ensure that the quality of planning ramp-up costs is being improved by enhancing the level of communication and knowledge exchange. In order to reach this target, cost-planning knowledge has to be collected, structured and provided. If this specific knowledge base is continuously extended with the experience and expertise gained in new ramp-up projects, planning complexity decreases while planning reliability increases (Slamanig, 2002). Thus, repeating similar basic failures in planning and decision making could be avoided. The fourth functional requirement for a key metric system is the possibility to simulate alternative scenarios. Nevertheless, it is impossible to anticipate all effects of decisions on ramp-up costs (Wiendahl, et al., 2002). This is due to the fact that ramp-up is characterized by a high degree of uncertainty (Haller, et al., 2003). There is uncertainty in the final product and process design, the volumes produced in the different stages of the ramp-up, the capacity required during the ramp-up, and the reliability of the equipment (Haller, et al., 2003). Even though this uncertainty cannot be eliminated, some of the potential consequences of decisions can be forecasted using simulation techniques (Schmidt, 1992). A simulation model cannot instruct a decision maker what decisions should be made, but instead it clarifies what would happen in making a decision in a given situation (Sterman, 1991). Thus, the planning risk is minimized and wrong decisions can be avoided at the outset. Figure 1 summarizes the requirements for an adequate planning and controlling key metric system. 1.1. Providingrelevant relevant Providing costinformation information cost
4.4. Simulatingalternative alternative Simulating scenarios scenarios
Adequate instrument for planning and controlling ramp-up costs
2.2. Providingsupport supportinin Providing coordination coordination
3.3. Supporting Supporting communication and communication and learningprocesses processes learning
Fig. 1. Requirements for an adequate instrument for planning and controlling ramp-up costs 3. DESIGNING AN ADEQUATE KEY METRIC SYSTEM FOR PLANNING RAMP-UP COSTS According to the considerations outlined before, it is imperative to design a model which represents both the causes of ramp-up costs and their effects on ramp-up costs. Therefore, we will follow a four step approach: First, relevant processes and activities within the ramp-up project must be determined as well as the types of costs caused by these activities.
Second, the most relevant cost drivers have to be identified for each of the ramp-up processes. In a further step, these cost drivers must be transferred into key metrics. Fourth, these key metrics have to be set in a hierarchical order so that they can be concatenated into an integrated key metric system. 3.1 Defining the ramp-up phase and ramp-up costs In a first step, the main ramp-up processes of the specific ramp-up project have to be identified and/or designed. Before starting with process identification, the ramp-up phase and the ramp-up costs have to be defined. The ramp-up phase represents a specific period within the product life cycle which is located at the end of product development and the beginning of full-scale production (Blanchard, 1978). Delimiting the ramp-up phase from its upstream and downstream phases may cause difficulties because of the smooth transitions between the consecutive life cycle periods (Slamanig, 2006). Furthermore, the length and design of the ramp-up largely depend on the industrial sector and the product type as well as the complexity of the underlying production and logistics processes (Carillo and Franza, 2006). As previously mentioned, it is assumed that the entire ramp-up phase can be divided into different sub processes. In reference to process identification, two different situations can be distinguished. In cases where there is detailed documentation of former ramp-up projects, processes can be defined by adapting former processes using available processdata and/or process-maps. In cases where a ramp-up is being planned without the possibility of reverting to former material, processes have to be defined from scratch. Furthermore, the ramp-up processes need to be categorized into two different process types: successive and simultaneous processes. Successive processes take place when the achievement of one project objective, e.g., a milestone or a quality gate, activates another process. Simultaneous processes occur when the operation of several processes occurs at the same moment in time. In the ramp-up phase, for instance, employees are trained during pilot production (Terwiesch and Bohn, 2001). At this point, it is noteworthy that planning a ramp-up calls for process engineering in the majority of cases. Subsequent to process identification, activities within the single processes have to be detected and differentiated. This step is of particular importance for the planning and controlling of ramp-up costs, since ramp-up costs are determined by the length and intensity of the activities taking place in the individual ramp-up processes. With the identification of the single ramp-up activities, the possibility is created to allocate resources to the activities and therefore to plan the costs which arise from providing and using these specific resources. In this context, it is assumed that certain activities take place in each of the ramp-up processes and therefore can be bundled. Some sample categories for these bundles could be: planning activities, production activities, material provision, quality
management and reorganizational activities (Möller, 2002). After identifying and assigning activities to the particular ramp-up processes, the corresponding types of costs and activity cost unit rates have to be determined. The allocation of costs to the different ramp-up processes may cause additional problems. In particular, problems arise if the ramp-up is carried out within an existing production facility where other products are simultaneously produced. In this particular case, the same resources are used for both the ramp-up of a new product as well as the regular production of other products. In order to cope with this task, the concept of activity based costing (ABC) may be helpful. ABC is a method of assigning costs to products and services based on the number of activities or transactions involved in the underlying processes. Furthermore, ABC seeks to identify cause-and-effect relationships to objectively assign costs. The result of this first conceptual step is a structured set of activities including the corresponding ramp-up costs, their types of costs and the associated activity cost unit rates. This set can be regarded as a conceptual framework for the following steps. 3.2 Identification of cost drivers within ramp-up In a further step, the activities have to be analyzed in order to identify cost drivers which significantly influence the corresponding ramp-up costs. These main cost drivers represent relevant parameters for the planning process. Such cost drivers could be the innovation or complexity level of a new product, the number of additional/new parts, components and/or sub-assemblies (Slamanig, 2006). When identifying cost drivers, it is essential to consider the low information level as well as the low number of sources of information which characterize the early stages of the planning process. Similar to the proposed procedure in step 1, cost drivers can be identified in two different ways. On the one hand, cost drivers can be deduced by applying empirical cost analysis when detailed cost-data from earlier ramp-ups is available. On the other hand, cost drivers have to be designed theoretically because cost data is often not sufficiently available. The number of cost drivers identified should not exceed a specific number to prevent the key metric system from becoming over-designed and therefore unmanageable (Slamanig, 2006). In addition, the time and effort put into the acquisition of data has to be taken into consideration. As we have observed, only a small number of cost drivers cause most of the total rampup costs. “Twenty is plenty” can be used as a rough rule of thumb. As a result, an activity-based set of the most relevant cost drivers can be created. 3.3 Transferring cost drivers into key metrics After identifying an accurate amount of cost drivers, they have to be transferred into key metrics in the next conceptual step. The quantification of the parameters is a necessary precondition to provide
In the last conceptual step, the key metrics have to be set in a hierarchical order so that they can be concatenated into an integrated key metric system. At the top of the key metric system, the aggregated planned resource use of each process is situated, which in turn is determined by the resource use in its underlying bundled activities. Multiplying the planned resource use per process (number of activities) with the corresponding activity cost unit rate results in the ramp-up costs for each process. The result of summing up all process- and activitybased planned ramp-up costs is the total ramp-up cost at a certain planning stage. The resource use within the bundled activities is thereby influenced by a specific number of key metrics which are situated at the lowest hierarchical level of the key metric system. These key metrics represent the most relevant cost drivers. At this level of the key metric system, relations between the single key metrics exist as the variation of one key metric leads to changes in other key metrics, and therefore in the planned ramp-up costs. For example, reducing the number of pre-products manufactured within the pilot-production leads to a decrease of the training level of employees (Almgren, 2000). Even though lower training costs should be planned because of less training activities, a decrease in the training level of employees usually leads to increased quality costs and time delays, since the employees commit more failures. By illustrating such basic causalities between changes in the planning parameters and their effects on ramp-up costs, additional costs can be better planned, analysed and controlled. In doing so, cost overshooting can be avoided from the outset. In order to provide such CERs from the lowest to the top-level of the key metric system, the interactions between the key metrics as well as their effects on
Target dimensions within ramp-up
Cost Planning level
Bundles of activities
Costs planned
Output planned
Time planned
Σ AB1 AB2 …
e.g. ramp-up preparing process number of necessary new ramp-up preparing activities x specific activity cost unit rate
ABn
+ Key metric level
3.4 Creating a key metric system
resource use have to be identified (Slamanig, 2006). The relations can therefore be either mathematical or qualitative and logical, and the strength of the relations can be indicated by their sensitivities (+/-). By detecting the CERs, analysis chains are created which provide detailed information about ramp-up processes, their underlying activities, ramp-up costs and their drivers as well as their underlying interdependencies. Figure 2 presents an overview of the conceptual framework of the key metric system.
Hierarchical order
significant cost planning information. These key metrics represent the most relevant planning parameters for the planned ramp-up project. For example, the cost driver ‘complexity level of the new product’ can be quantified using the key metric ‘number of components within the new product compared to the forerunner product’. In doing so, it is possible to assess the monetary effects of changes on the planning parameters within the planning process. However, it is impossible to quantify all of the cost drivers. This is due to the fact that some of the cost drivers, such as the availability of purchased items or production facilities at a certain ramp-up stage, can hardly be expressed using a key metric. Nevertheless, it is essential to incorporate the most important non-quantifiable cost drivers, so-called soft factors, into the key metric system, as they seriously influence ramp-up costs. Thus, besides quantitative planning parameters, the key metric system should also include relevant qualitative factors, e.g., the qualification and flexibility level of employees.
+
e.g. ramp-up time
e.g. qualification level of employees
-
e.g. process stability, complexity of the product e.g. pilot production volume
Cause and effect relationships (exemplified)
Fig. 2. Conceptual framework of the key metric system for planning ramp-up costs 4. BENEFITS OF USING THE KEY METRIC SYSTEM By following the previously outlined four-stepapproach, the complexity of the ramp-up planning process can be strongly reduced by identifying the causes of the emergence of ramp-up costs as well as the effects of planning changes on the ramp-up costs. In doing so, transparency increases as the problem of ramp-up cost planning is dealt with by considering the whole ramp-up phase as a set of successive and simultaneous processes and subordinate activities. These activities are in turn influenced by a set of several related cost drivers. As a result, the information required to make decisions during the planning of ramp-up costs is concretised. Furthermore, the number of unknown causes and impacts on the ramp-up costs is limited, even though a certain level of fuzziness still persists. Due to an intensive and permanent examination of the ramp-up, its processes, costs, cost drivers and their interrelations during and after the conception stage, processes can be learned (Slamanig, 2006). In order to maintain the practicability of the key metric system, new insights in the form of modifications and/or extensions have to be permanently incorporated, e.g., additional cost drivers or relationships. Thus, the communication and coordination level between the people involved in the ramp-up cost planning process is kept high. Simulating and therefore anticipating the effects of
changes in the planning process on the ramp-up costs is essential, but may be exhausting and extensive without adequate IT application support. However, the visual illustration of the involved key metrics and their relations can significantly accelerate the process of transferring the required information. 5.
CONCLUSION
Due to the specific problems associated with the planning of ramp-up costs, this paper has provided the conceptual design of a key metric system in order to increase the quality of planning ramp-up costs as well as the planning reliability. After defining the functional requirements to an effective key metric system that supports the process of planning and controlling ramp-up costs, we have demonstrated how to develop such a key metric system by following a four step approach. The basic idea behind this approach is that ramp-up costs represent future resource use within different ramp-up processes, caused by a set of activities taking place in the single processes. Since most of these activities are performed repeatedly, they can be combined into activity bundles. The resource use within the bundled activities is driven and influenced by a specific number of key metrics, and interdependencies exist between these key metrics. The conception of a key metric system which helps the understanding of the emergence of ramp-up costs, and which therefore provides the required cost planning information, calls for a systematic approach. This paper has demonstrated how to identify and analyse the rampup phase, its processes, activities, costs, cost drivers and their underlying interdependencies. Results from an implementation within the automotive sector have shown that the key metric system is able to meet the ascertained requirements. Even though a lot of problems within ramp-up management are still unsolved, this approach makes a practical contribution to more effective and efficient planning of ramp-up costs. REFERENCES Abele, E., J. Elzenheimer and A. Rüstig (2003). Anlaufmanagement in der Serienproduktion. ZWF – Zeitschrift für wirtschaftlichen Fabrikbetrieb, Vol. 98, No. 4, pp. 172-176. Almgren, H. (2000). Pilot production and manufacturing start-up: the case of Volvo S80. International Journal of Production Research, Vol. 38, No. 17, pp. 4577-4588. Blanchard, B.S. (1978). Design and Manage to Life Cycle Cost, Portland. Carillo, J.E. and R.M. Franza (2006). Investing in product development and production capabilities: The crucial linkage between timeto-market and ramp-up-time. European Journal of Operational Research, Vol. 171, No. 2, pp. 536-556. Di Benedetto, C.A. (1999). Identifying the Key Success Factors in New Product Launch. The Journal of Product Innovation Management, Vol.16, No. 6, pp. 530-544.
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