Performance Measures From goal to control : A modeldriven approach

Student thesis: Master thesis (including HD thesis)

  • Jon Andersen
  • Tommy Dahlbæk Bell
  • Claus Boye Asmussen
The topic of this report is how performance measures, company functions and production control principles can be combined to form a model aimed at supporting decision makers with respect to guiding the company or organization as whole towards their chosen goal or goals. This is accomplished by designing a model which indicates which company functions, performance measures and processes will be affected when adjusting a control principle or showing which control principle to adjust with the objective to improve a performance measure.
This was accomplished by modeling, both visually and mathematically, the company functions and production processes and combining the models with performance measures applicable for an organization employing an MTS planning and control concept. The performance measures are based on a framework chosen by a citation study and choosing one of the most cited authors in the performance measurement field, which has a perspective which is similar to that explored in the report. The company functions are based on the planning and control concept, which composes a number of company functions w most, if not all, MTS companies possess. The production control principles are based on the chosen planning and control concept and is identified based on domain knowledge and accepted literature in the field.
An information and material goods model is designed and is the basis for assigning control principles and performance measures to each function. Combining the performance measures, company functions and control principles via the developed models form a decision support model. Which upon review revealed a lack of focus on how to measure the tactical level/company functions of an MTS company? This was already suspected based on domain knowledge and published literature. There is a shortage of performance measurements aimed towards benchmarking how company functions perform. This is particularly aimed at production planning and production scheduling, for which no performance measures where identified. Developing two new performance measures and expanding the concept of RunningCost into covering the allocation of overhead expenses based on activities, which comprises how a function performs its primary function. For instance, the overhead cost for sales is allocated based on the number of saleslines which Sales process or how many products the packing and shipping department process. The performance measures developed represents a stability index and an economical index of how well both production and scheduling performs their primary function.
The performance measures are compared to historical numbers to make meaning of them and track how the functions develop compared to the overhead cost incurred. The stability index indicate how stable a plan is based on how many changes are introduced from the beginning of a period compared to the end of that period, the economical index indicates how the expenses of a plan develops over the same period.
The developed decision support model and the new performance measures and expanded runningcost is used in an example to indicate how it is possible to affect the performance measure ‘Ability to keep promise’ which indicate the company or organizations ability to deliver the right amount at the promised date. Using the decision support model it is identified that the performance measure is based on data from the finished goods inventory and data which represents the development of the inventory and sales information containing promised sales and amount, must be subjected to further data analysis to answer how much each of the control principles affect the performance measure. A number of multivariate methods are available to discover latent connections between data, in the example Principal Component Analysis is chosen to base the analysis on. However the dataset chose to apply the analysis on is incomplete and must be manipulated to be eligible for applying the analysis on. This is not a problem as this is only an example, however in an actual situation, more data would have to be collected and organized for this method to be viable. Based on data manipulation and insertion of estimated values, the PCA method indicates that each control principle does not affect how the finished goods inventory is able to affect the performance measure ‘Ability to keep promise’.
The conclusion of the report and the developed model is that is there is insufficient work done on performance measures for the tactical and that many performance measures are developed with an economical perspective which is not always sufficient to measures performance in some company functions. Further, there is ample opportunity to further develop the decision support model into a more comprehensive system, by applying further research into identification and application of performance measures towards the tactical level. Identifying and modeling further performance measures and connections towards the operational level through the tactical level would increase the decisions support models applicability.
Publication date1 Jun 2012
Number of pages94
ID: 63500577