Nowadays, for customers the logistical performance of industrial companies is as important as the price and quality when buying decisions have to be made. It can be observed that considering the KPIs of logistical performance as quality figures, similar to the product quality, becomes quite popular within national and international markets. Two logistical performance key figures that can be pointed out in that context are short lead times and high schedule reliability. The delivery times demanded by markets often are shorter than the realizable lead times of products or the replenishment time of raw materials or purchased parts. In order to deliver the products in time, companies have the opportunity to implement so called order penetration point (OPP) within their productions. The OPP specifies the point within a production which connects upstream processes linked with work orders and downstream processes link with costumer orders. The OPP is often built up as a stock of unfinished goods. Currently companies position their OPP only with the goal to satisfy the demand of short lead times set by the market. Other logistical targets such as a low work-in-process (WIP), high schedule reliability or a high utilization are usually not taken into account. Hence, due to the complexity of positioning the OPP companies underestimate the logistical potentials that can be achieved by positioning the OPP optimally. In this publication the fundamental determining factors which both influence the position of the OPP and are influenced by the selected position of the OPP are presented. In particular the dependencies between the four logistical targets, lead time, WIP, schedule reliability and the grade of utilization, and the position of the OPP are discussed. Exemplarily the correlation between the position of OPP and the schedule adherence at the end of the supply chain are presented. It can be assumed that the schedule adherence increases by moving the OPP towards the end of the supply chain. Possible reasons that explain this particular effect, like the reduction of lead time variation, will be discussed in detail.

Kurzfassung Unternehmen befinden sich derzeit in einem Umfeld, in dem die effiziente Nutzung von Ressourcen von großer Bedeutung ist. Um einen langfristigen Erfolg sicherstellen zu können, wird daher die Verfolgung von logistischen Zielgrößen kundenseitig immer wichtiger. Eine Herausforderung dabei ist es, Kapazitätsangebot und -nachfrage durch gezielte Maßnahmen aufeinander abzustimmen. In diesem Beitrag wird ein Ansatz vorgestellt, mit dem diese Maßnahmenauswahl im Rahmen einer quantitativen Analyse unterstützt wird.

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Kurzfassung Der Plan-Abgang eines Arbeitssystems – definiert durch Plan-Termine und -Mengen – wird maßgeblich durch die Aufgaben der Losgrößenbildung sowie der Durchlaufterminierung festgelegt und besitzt als planungsrelevante Stellgröße einen erheblichen Einfluss auf das logistische Systemverhalten. Auf Basis der Verfahren der Kapazitätsabstimmung können die Schwankung des Plan-Abgangs reduziert und die Termintreue des Arbeitssystems erhöht werden. In diesem Beitrag werden ein analytisches Modell zur Beschreibung der Auswirkung einer Belastungsanpassung auf den Plan-Abgang vorgestellt und zukünftige Forschungsziele in diesem Bereich skizziert.

Enterprises are increasingly being challenged by a high diversity of variants, shortening product life cycles, and growing cost pressure. One possibility of facing these challenges is to improve the efficiency of logistics. As the borderline between a company's production with and without reference to a specific customer order, the customer order decoupling point (CODP) is a good position to significantly increase the efficiency of logistics. Apart from shortening lead times, it may also diminish the lateness at the end of the supply chain. There is currently no comprehensive analytical description of actual schedule variances within a supply chain. This paper presents a Lateness Histogram to serve as an analytical model designed to describe the mean and maximum delivery delay in a store (e.g. CODP). It is fed with the distribution of schedule and quantity variances at the store input as well as the distribution of demand at the store output. Thus, the model is another step towards analytically describing the potentials of implementing a CODP or moving it along the production process in order to obtain a possibility of quantifying the amount of lateness reduction as the CODP moves downstream. This is followed by empirical simulation studies aimed at validating the model.