Within production planning and control (PPC) complex interdependencies are present. Companies have to deal with fields of tension created by frequently opposing logistic objectives. Conducting an active and expedient positioning within the conflicts between the logistic objectives is of utmost strategic importance. This paper presents an approach for modelling the impact of tasks of production planning and control on a supply chain’s logistic objectives. A production planning and control framework is thus established. First of all the PPC tasks are described universally valid. Moreover the framework contains systems of logistic objectives for the core processes of a company’s internal supply chain showing the interdependencies and highlighting existing fields of tension. Quantitative logistic models are located within the framework, so that they can be used for calculating values for PPC parameters and moreover for positioning the production within the indicated fields of tension. In addition a case study is introduced to illustrate the use of the approach in industry.

Introduction

The mission of production planning and control is to plan the production in matters of volumes and dates on a regular basis and to realize the plan despite unavoidable disruptions like the lack of staff or delayed deliveries as economically as possible [1]. In contrast the mission of supply chain management is to plan and control the flow of material and information in supply chains or production networks with the aim to satisfy the customer and to minimize costs [1]. Mathematically modelling logistic processes makes it possible to generate simplified depictions of real-life situations. The resulting logistic models are characterized by reduced function (due to dispensing with unimportant factors present in the real world) and idealized function (due to simplifying indispensable factors found in the real world) [2]. Logistic models are thus excellent tools for procuring information and supporting decision-making. There are different types of models including task and process models as well as quantitative descriptive, impact and decision-making models. Task and process models are generally oriented on describing processes and interactions between processes. They deliver references for designing tasks and processes in various areas (e.g. planning and control). The Supply Chain Operations Reference Model (SCOR-Model) is an example here [3]. It is kept consistently structured, branch-neutral, generic and universally valid. Focussing PPC the advanced Aachen PPC Model provides a coherent description of a reference process landscape for production planning and control [4]. Quantitative descriptive, impact and decision-making models are oriented on quantitative interactions of concrete variables (e.g. logistic objectives)—however, in turn, do not take into account the whole of relationships occurring in or between processes. An example of a quantitative impact model is the Logistic Operating Curve Theory. Using approximation equations, the Logistic Operating Curves make it possible to position a production within the field of tension created by the opposing logistic objectives Work-in-Process (WIP), throughput time and utilization [5, 6]. A positioning is necessary because a minimization of WIP, a minimization of throughput time and a maximization of utilization cannot be reached at the same time. There are numerous, rich in detail, quantitative logistic models. Nevertheless, since the individual models are not linked, holistically analysing and designing planning and control processes remains a challenge.