Knowledge that Transforms

We present a tool to diagnose the behavior of planners in complex production processes and to establish improvement potential for the delivery performance by changing the planning behavior. Scientific literature on production control offers valuable knowledge, but the complexity of real‐life processes makes it impossible to directly apply this knowledge in real‐life. The presented tool identifies possible deficiencies in the current way of managing the business processes, by matching the scientific knowledge on order planning with data reflecting the real‐life processes via logistic regression. A case study at a maintenance organization illustrates the diagnosis tool.

In this paper we review the literature on appointment policies, specifically in terms of the objective function commonly used and the assumptions made about the behavior of demand. First, we provide an economic framework to analyze the problem. Based on this framework we make a critical analysis of the objective functions used in the literature. We also question the validity of the assumption made throughout the literature that demand is exogenous and independent of customers' waiting times. We conclude that the objective functions used in the literature are appropriate only in the case of a central planner facing a demand that is unresponsive to waiting time. For other scenarios, such as a private server facing a demand that does react to waiting time, these objective functions are only shortcuts for the real objective functions that must be used. A more general model is then proposed that fits these scenarios well. Finally, we determine the impact of using the literature's objective functions on optimal appointment policies.

Evidence on the impact of amplification effects on supply chain performance primarily has been derived from studies in manufacturing industries. In this article we reported on a case study from the telecommunication industry and aimed to analyze relevant root causes and associated countermeasures of the amplification phenomenon in service supply chains. Our case findings confirm the occurrence of upstream amplification of workload in the service supply chain, workload being a more appropriate measure for amplification effects in service supply chains than inventory levels. Not all of the root causes for amplification effects known from research in manufacturing environments were found to apply in this particular service context, especially those related to the use of inventory. In addition, our telecom case study highlighted a new root cause for amplification: interactions of high workloads and reduced process quality that start reinforcing each other once workloads pass a certain threshold. In this particular case, many of the known countermeasures to eliminate amplification did not apply, because of the specific characteristics of the service process, or yielded only limited results. A potentially very powerful countermeasure identified was to implement quality improvements throughout the service chain. This quality dimension links our research to the literature on service management in general, where service quality is on top of the research agenda.

We develop a dynamic prioritization policy to optimally allocate a scarce resource among K projects, only one of which can be worked on at a time. When the projects' delay costs differ, the problem (a “restless bandit”) has not been solved in general. We consider the policy of working on the project with the highest expected delay loss as if the other project was completely finished first (although recourse is allowed). This policy is optimal if: (1) the delay cost increases with the delay regardless of the performance state, (2) costs are not discounted (or, discounting is dominated by delay costs), (3) projects are not abandoned based on their performance state during processing at the scarce resource, and (4) there are no stochastic delays. These assumptions are often fulfilled for processing at specialized resources, such as tests or one‐off analyses.