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The control of production processes is the subject of several disciplines, such as statistical process control (SPC), total productive maintenance (TPM), and automated process control (APC). Although these disciplines are traditionally separated (both in science and in business practice), their goals have a great deal of overlap. Their common goal is to achieve optimal product quality, little downtime, and cost reduction, by controlling variations in the process. However, single or separated parallel applications may be not fully effective. This implies the need for an integrated approach to define, describe and improve the control of production processes. This paper discusses how controls from disciplines such as SPC, TPM and APC can be seen as a coherent set of efforts directed to the technical control of production processes. To achieve this, an integrated process control (IPC) model is introduced. The model provides a structure to get an overview of the functions of controls and their interrelations. It shows that there is no one best way to control a process: the optimal set of controls depends on the situation. The main contingencies are briefly addressed. The possibilities to use the model for prescribing, describing and improving control are illustrated. Finally, implications for business practice are discussed.

The work described in this paper is part of a research that is focussed on structuring the tools of statistical process control and studying the possibilities to apply them in different situations [1]. Statistical process control traditionally uses output measurements to control the stability of a process and to detect causes of non-stability (out of control situations) [2] and [3]. However, as a result of the trend to strive for prevention instead of detection, SPC is shifting from controlling product characteristics to controlling process input and process factors. The goal of this shift is to detect and resolve problems in the process before they can lead to (in-stable) variation in the product. It shows that in some cases statistical tools such as control charts can be used to monitor process factors (such as furnace temperatures), but in many cases, other tools, such as maintenance and automated controls that are part of other disciplines than SPC, are used to achieve process control. Thus, the control of production processes is the subject of statistical process control (SPC) but also of several other disciplines, such as total productive maintenance (TPM) [4], and automated process control (APC) [5]. In this paper we will focus on SPC, TPM and APC, because they are three well known and frequently used examples of disciplines directed to process control. However, one could think of other related “disciplines” that are directed to the control of production processes, such as source inspection and Poka Yoke [6]. Although each discipline has a specific approach to process control, there is a great deal of overlap between these disciplines because of their common goal: to achieve optimal process performance in terms of product quality, downtime, and costs, by controlling variations in the process. Despite this overlap, these disciplines are traditionally separated, both in science and in business practice. In practice each discipline is often initiated by separate departments: SPC by the quality department and production; TPM by the maintenance department; APC by the engineering department. In these cases efforts to improve control tend to be limited to tools from one of these this disciplines, or in case controls from different disciplines are used, they are often not related to each other. This may result in single, or separated parallel mono-disciplinar applications. Since the tools from various disciplines are partly additional, but also partly overlapping alternatives, this situation may be not fully effective. Also in literature the overlap of process controls did not result in an integrated approach to process control. Although literature from the separate disciplines partly claim the same area, most of the referred publications from these disciplines hardly mention each other. Instead they tend to refine and expand their particular field to the level of control programs, thus implicitly claiming a larger part of the working areas of other disciplines. If tools from other disciplines are mentioned they are often depicted as part of or supporting to the one described. Some papers discuss the integration of SPC and APC, but mainly focus on the mathematical aspects of integration [7], [8] and [9]. Other papers discuss the integration of SPC-related techniques and TPM [10] and [11], but limit the discussion to management aspects. This paper however, presents a generic approach that also integrates process control tools outside the fields of SPC, TPM and APC, and addresses technical operation aspects of integration. The first goal of this paper is to show the relations and overlap of controls from different disciplines, and thus the need for an integrated approach to process control. After a discussion of the working areas and overlap of SPC, TPM and APC, the need for an integrated approach is addressed. The second goal of this paper is to present a model that supports an integrated approach to process control, by providing a structure to describe and systematize the controls of a process. This model, the IPC model, is introduced and the possibilities to use the model for business practice and scientific purposes are discussed. Finally, conclusions and directions of further research are addressed.

This paper shows the need for an integrated approach to process control in production. The goals of controls from various disciplines are interrelated and partly overlapping. In this way they can be each other's alternatives or can function as useful supplements. The risk of approaching process control from one discipline is that process controls are limited to process control tools and aspects of this discipline which may be not optimal or even counterproductive. To support an integrated approach to process control, the IPC model is introduced. The model can be used to understand, describe, analyze and prescribe the control of production processes. In using the model, it is important to consider the in#uence of situational factors. Preliminary research already gives insight in these `contingencya in#uences, but additional research is necessary in this respect. Although this paper focuses on controls on the operational level, organizational aspects also play an essential role in an e!ective control system. In future research, a model similar to the IPC model will also be developed for control activities in product and process development, and also for activities for performance measurement and improvement. All these elements will be combined into a decision support system for the application of process control techniques. The decision support system should provide: f models for control activities in design, production process control, and performance measurement and improvement,f methods and guidelines for "lling in these models, including control scenarios and organizational guidelines, f a database with control tools, including information on functions and situational factors of process control tools. The goal of all this is to structure the wide variety of control tools, and to help organizations to use them in an e!ective and e$cient way.