Autor: Carsten Feldmann

Motion-Mining® is a technology that uses motion sensors and pattern recognition to enable automated process mapping and analysis of manual work. This article evaluates the advantages and limitations of its use in manufacturing and logistics processes. To this end, Motion-Mining® is compared with traditional lean management tools used to analyze manual activities. Experiences derived from four use cases provide decision support for selecting the appropriate method for a specific use case.

RPA refers to bots that automate repetitive, rulebased tasks in a business process. This paper describes general areas of application for RPA in logistics as well as two practical logistics examples. In addition, a procedure model for the implementation of RPA in logistics is presented. The paper answers the following questions: What are suitable use cases for RPA in logistics? What criteria support the selection of suitable processes? And how should an implementation guide be designed to systematically support an implementation project taking into account critical success factors?

Volatility in market demand leads to temporary over- and under-utilization of production assets and stocks. Levelling (heijunka) as a lean method aims at de-coupling production from market volatility. The production program is spread as even as possible over time. This achieves high asset utilization, short lead times, and low inventories. There are validated heijunka methods for the manufacturing industry, but for the process industry this remains a research gap. This article describes the Product Wheel and its validation at a building material manufacturer in order to close that gap.

There is a variety of empirically validated methods for implementing pull-systems in the manufacturing industry, but pull-based replenishment for the process industry remains a research gap. This article describes the development of a model for implementing a pull-system for an intracompany production network in the process industry and its validation in a case company.

Volatility in market demand leads to temporary over- and under utilization of productive assets. Heijunka aims at de-coupling the production system from market volatility. The production program is spread as even as possible over time. This achieves high asset utilization, short lead times, and low inventories. There are validated Heijunka methods for the manufacturing industry, but for the process industry this remains a research gap. This article describes the development of a Heijunka model for the process industry in order to close that gap.