Managers and Students as Newsvendors - How Out-of-Task Experience Matters

We compare how freshmen business students, graduate business students and experienced procurement managers perform on a simple inventory ordering task. We find that, qualitatively, managers exhibit ordering behavior similar to students, including biased ordering towards average demand. Experience, however, affects subjects’ utilization of information. The managers’ work experience seems most valuable when there is only historical demand data to guide decision making, while students better utilize analytical information and task training. As a result, when information necessary to solve the problem to optimality is added to historical information, students catch up to the managers, and students with classroom experience in operations management outperform managers.

An aggregation-based approximate dynamic programming approach for the periodic review model with random yield

A manufacturer places orders periodically for products that are shipped from a supplier. During transit, orders get damaged with some probability, that is, the order is subject to random yield. The manufacturer has the option to track orders to receive information on damages and to potentially place additional orders. Without tracking, the manufacturer identifies potential damages after the order has arrived. With tracking, the manufacturer is informed about the damage when it occurs and can respond to this information. We model the problem as a dynamic program with stochastic demand, tracking cost, and random yield. For small problem sizes, we provide an adjusted value iteration algorithm that finds the optimal solution. For moderate problem sizes, we propose a novel aggregation-based approximate dynamic programming (ADP) algorithm and provide solutions for instances for which it is not possible to obtain optimal solutions. For large problem sizes, we develop a heuristic that takes tracking costs into account. In a computational study, we analyze the performance of our approaches. We observe that our ADP algorithm achieves savings of up to 16% compared to existing heuristics. Our heuristic outperforms existing ones by up to 8.1%. We show that dynamic tracking reduces costs compared to tracking always or never and identify savings of up to 3.2%

European Research Center for Information Systems (ERCIS):Gründungsveranstaltung Münster, 12. Oktober 2004

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Railway crew scheduling with semi-flexible timetables

We investigate the impact of coordinating the timetable and the crew schedule in an operational freight railway system. Usually, those problems are solved sequentially-resulting in suboptimal schedules for train drivers due to large idle times between two train rides. We seek to coordinate the timetable and the crew schedule on the operational level by adding flexibility to the timetable. We introduce small time windows that allow to shift entire trains forwards and backwards by discrete time periods. We present a mathematical model and solve it with a column generation heuristic. We test our model on three real datasets of a major European Freight Railway Operator and show that significant reduction in idle time and cost can be achieved

Modeling and analyzing information delays in supply chains using transfer functions

Advanced inventory policies require timely system-wide information on inventories and customer demand to accurately control the entire supply chain. However, the presence of unsynchronized processes, processing lags or inadequate communication structures hinder the widespread availability of real-time information. Therefore, inventory systems often have to deal with obsolete data which can seriously harm the overall supply chain performance. In this paper, we apply transfer function methods to analyze the effect of information delays on the performance of supply chains. We expose the common echelon-stock policy to information delays and determine to what extent a delay in inventory information and point-of-sale data deteriorates the inventory policies' performance. We compare the performance of this policy with the performance of an installation-stock policy that is independent of information delays since it only requires local information. We find that this simple policy should be preferred in certain settings compared to relying on a complex policy with outdated system-wide information. We derive an echelon-stock policy that compensates for information delays and show that its performance improves significantly in their presence. We note potential applications of the approach in service parts supply chains, the hardwood supply chain, and in fast moving consumer goods settings. (C) 2014 Elsevier B.V. All rights reserved

Equivalent Inventory Metrics: A Behavioral Perspective

We analyze how performance metrics that contain equivalent information affect actual decisions. We consider two such performance metrics from supply chain management, days of supply and inventory turn rate, where one is the inverse of the other. We argue that individuals' assessment of performance is also affected by the metric as opposed to solely based on the inventory value that actually matters. We perform three laboratory studies and analyze how decisions are affected by the metric used to indicate inventory performance. The first study considers alternative inventory optimizations, out of which one must be selected. The second study analyzes a decision maker who must decide on the effort to invest in optimizing inventory of a specific product. The third study corresponds to the economic order quantity model. Our behavioral models suggest that decisions are affected by the metric that is used to indicate performance, and we find support for the predictions in laboratory experiments with human subjects: Under the inventory turn rate metric, individuals overvalue inventory reductions. Compared to decisions under the days of supply metric, they choose worse inventory optimization options, invest more effort optimizing inventory of specific products, and choose higher ordering cost

The value of real time yield information in multi-stage inventory systems - Exact and heuristic approaches

We consider a random yield inventory system, where a company has access to real time information about the actual yield realizations. To contribute to a better understanding of the value of this information, we develop a mathematical model of the inventory system and derive structural properties. We build on these properties to develop an optimal solution approach that can be used to solve small to medium sized problems. To solve large problems, we develop two heuristics. We conduct numerical experiments to test the performances of our approaches and to identify conditions under which real time yield information is particularly beneficial. Our research provides the approaches that are necessary to implement inventory control policies that utilize real time yield information. The results can also be used to estimate the cost savings that can be achieved by using real time yield information. The cost savings can then be compared against the required investments to decide if such an investment is profitable. (C) 2014 Elsevier B.V. All rights reserved

A graph partitioning strategy for solving large-scale crew scheduling problems

Railway crew scheduling deals with generating driver duties for a given train timetable such that all work regulations are met and the resulting schedule has minimal cost. Typical problem instances in the freight railway industry require the generation of duties for thousands of drivers operating tens of thousands of trains per week. Due to short runtime requirements, common solution approaches decompose the optimization problem into smaller subproblems that are solved separately. Several studies have shown that the way of decomposing the problem significantly affects the solution quality. An overall best decomposition strategy for a freight railway crew scheduling problem, though, is not known. In this paper, we present general considerations on when to assign two scheduled train movements to separate subproblems (and when to rather assign them to the same subproblem) and deduct a graph partitioning based decomposition algorithm with several variations. Using a set of real-world problem instances from a major European railway freight carrier, we evaluate our strategy and benchmark the performance of the decomposition algorithm both against a common non-decomposition algorithm and a lower bound on the optimal solution schedule. The test runs show that our decomposition algorithm is capable of producing high-quality solution schedules while significantly cutting runtimes compared to the non-decomposition solution algorithm. We are following a greenfield approach, where no information on previous schedules is needed. Hence, our approach is applicable to any railway crew scheduling setting, including network enlargement, integration of new customers, etc

The Effect of Social Preferences on Sales and Operations Planning

Sales and operations planning processes are used to align production quantities and customer demand. Two key activities of these processes are demand planning and production planning, which are often assigned to individuals in different departments. Production planning requires accurate demand forecasts from demand planning to be able to choose proper production quantities, but demand planners have to invest effort to create accurate demand forecasts. We study the role of social preferences (altruism, inequality aversion, and competitive pressure) in incentivizing demand planners to invest effort, and we analyze how social preferences interact with monetary incentives. We use a game theoretic model and laboratory experiments. Our results indicate that social preferences can be used to incentivize demand planners to invest effort and that this effect is anticipated by production planners. The resulting more accurate demand forecasts and adapted production quantities result in higher company profit. We also provide an optimization model for optimally allocating investments to financial incentives and social preference building