Clemens Wolff

Determining Cost-Optimal Availability for Production Equipment Using Service Level Engineering

Equipment availability is a top priority for companies with industrial production. To ensure high equipment availability, many production companies outsource equipment maintenance to external service providers. While this business has long been organized in a transaction on-call fashion, customers are more and more often demanding a shift towards longterm maintenance contracts that guarantee a certain level of equipment availability for a fixed price. However, since higher availability guarantees will usually result in higher contract prices, customers face the challenge to determine optimal levels of availability for their fleet of equipment.Applying service level engineering, this article develops a method to determine cost-optimal equipment availability for heterogeneous production equipment that is operated within the same production line. First, it is suggested to derive optimal service levels by trading-off the cost of downtime against the cost for increasing uptime. Second, using methods from graph theory, we develop an approach to quantify the downtime cost of a production line in dependence of the downtime of its individual units of production equipment.Thus, this article makes a first contribution towards the quantification of downtime cost in industrial production. It contributes to the adoption of long-term maintenance outsourcing contracts in the industrial sector.

Digitalization of Field Service Planning: The Role of Organizational Knowledge and Decision Support Systems.

Increasing competition and decreasing margins force manufacturers of industrial machinery to augment their products with complementary services—several dependent on field service planning. Hence, the efficient delivery of field services has become a main competitive differentiator and is driving companies to digitalize their processes and utilize decision support systems. Based on a qualitative interview study of maintenance providers, the paper provides empirical insight on the role of organizational knowledge and decision support systems in this digital transformation. The study shows that while maintenance providers have digitized their field service processes, many are only beginning the process of digitalization. Today, employees heavily rely on tacit and embedded knowledge in their decision-making. Therefore, knowledge-driven decision support systems—which have not yet been adopted in the industry—have been identified as an important cornerstone of the coming digitalization of field service planning.