Dynamic Efficiency and Productivity Measurement

The book takes on a systematic treatment of dynamic decision making and performance measurement. The analytical foundations of the dynamic production technology are introduced and developed in detail for several primal representations of the technology with an emphasis on dynamic directional distance functions. Dynamic cost minimization and dynamic profit maximization are developed for primal and dual representations of the dynamic technology. A dynamic production environment can be characterized as one where current production decisions impact future production possibilities. Consequently, the dynamic perspective of production relationships necessarily involves the close interplay between stock and flow elements in the transformation process and how current decisions impact the changes in future stocks. Stock elements in the production transformation process can involve physical elements that can be effectively employed in the transformation process, which can include the stock of technical knowledge and expertise available to the decision maker during the decision period. The dynamic generalization of concepts measuring the production structure (e.g., economies of scale, economies of scope, capacity utilization) and performance (e.g., allocative, scale and technical inefficiency, productivity) are developed from primal and dual perspectives. As an important source of productivity growth, production efficiency analysis is the subject of countless studies. Yet, theoretical and empirical studies focusing on production efficiency have ignored typically the time interdependence of production decisions and the adjustment paths of the firm over time. The empirical implementation of these production and performance measures is developed at length for both nonparametric and econometric approaches.

Primal Analytical Foundations of Dynamic Production Analysis

This chapter develops dynamic production analysis within the context of the adjustment cost model of the firm, where adjustment costs are associated with changes in the level of the quasi-fixed factors, also known as internal adjustment costs. The chapter characterizes axiomatically several primal representations of the adjustment cost production technology. The axiomatic approach is a cornerstone to model production technology both in theoretical and empirical work. The existence of several representations of the adjustment cost production technology is essential in the analysis of the firm’s decisions and its adjustment path that are conditioned by the technology. Three set representations of the adjustment cost production technology are discussed and characterized axiomatically. Two functional representations of the technology are also addressed using an axiomatic approach.

Angebotsplanung in dynamischen Produktionsnetzwerken/Quotation planning in dynamic production networks

Das BMBF-Forschungsprojekt „Broker für dynamische Produktionsnetzwerke (DPNB)“ widmet sich dem Anwendungsszenario „auftragsgesteuerte Produktion“ im Kontext von Industrie 4.0. Für die Angebotsplanung ist ein zentraler Aspekt die Bildung eines individuellen Netzwerks aus unternehmensübergreifenden Produktionsfähigkeiten und -kapazitäten. Dieser Fachaufsatz liefert einen Beitrag dazu, wie der Prozess der Bearbeitung einer Produktionsanfrage umgesetzt werden kann.   The BMBF-research project “Dynamic Production Network Broker (DPNB)” is dedicated to the application scenario “order-driven manufacturing” in the context of Industry 4.0. A central question for quotation planning is how to connect manufacturing capabilities and capacities across companies to build an order-specific supply chain network. This paper addresses the core issues of processing a manufacturing request.