Knowledge that Transforms

We consider a product sold in multiple variants, each with uncertain demand, produced in a multi‐stage process from a standard (i.e., generic) sub‐assembly. The fan‐out point is defined as the last process stage at which outputs are generic (outputs at every subsequent stage are variant‐specific). Insights gained from an analytical study of the system are used to develop heuristics that determine the stage(s) at which safety inventory should be held. We offer a relatively‐simple heuristic that approaches globally‐optimal results even though it uses only two relatively‐local parameters. We call this the VAPT, or value‐added/processing time heuristic, because it determines whether a (local) stage should hold inventory based only on the value added at that local stage relative to its downstream stage, along with the processing time at that local stage relative to its downstream stage. Another key insight is that, contrary to possible intuition, safety inventory should not always be held at the fan‐out point, although a fan‐out point does hold inventory under a wider range of conditions. We also explore when postponement is most valuable and illustrate that postponement may often be less beneficial than suggested by Lee and Tang (1997).

Notwithstanding the popularity of outsourcing as a business strategy, the performance benefits realized through outsourcing efforts are observed to be mixed in practice. This leads to important unresolved questions regarding why some firms are able to derive substantial value from their outsourcing initiatives while other firms are left disappointed. This study joins an emerging literature integrating transaction cost economics and capabilities‐based perspectives to develop a deeper understanding of the drivers of outsourcing performance. I develop a theoretical model that examines the independent and joint influence of governance misalignment (i.e., deviation from transaction cost's predicted mode of governance) and a firm's outsourcing capability on the performance of outsourced processes. I test the theoretical model using a dataset of 172 outsourced and 156 in‐house processes. The finding that governance misalignment corresponds to inferior process performance supports transaction cost's discriminating alignment hypothesis. Interestingly, I also find that a retained technical expertise (TE) and outsourcing knowledge management routines (OKMR; both contributors to a firm's outsourcing capability) positively influence outsourcing performance both directly and via their relationship with governance misalignment. While a retained technical expertise and outsourcing knowledge management routines each positively influence outsourcing performance, they do so in distinctive ways. These findings have important managerial implications and make a significant theoretical contribution. Specifically, this study demonstrates that the notion of a governance misalignment is firm‐specific, conditional on the governance capabilities of the organization. This insight underscores the value of integrating transaction cost logic with capabilities‐based perspectives.

We study the scheduling of multiple tasks under varying processing costs and derive a priority rule for optimal scheduling policies. Each task has a due date, and a non‐completion penalty cost is incurred if the task is not completely processed before its due date. We assume that the task arrival process is stochastic and the processing rate is capacitated. Our work is motivated by both traditional and emerging application domains, such as construction industry and freelance consulting industry. We establish the optimality of Shorter Slack time and Longer remaining Processing time (SSLP) principle that determines the priority among active tasks. Based on the derived structural properties, we also propose an effective cost‐balancing heuristic policy and demonstrate the efficacy of the proposed policy through extensive numerical experiments. We believe our results provide operators/managers valuable insights on how to devise effective service scheduling policies under varying costs.

The flow of jobs within a system is an important operating characteristic that influences system performance. While the majority of previous studies on manufacturing performance consider product flows only as an implicit parameter of the design, we introduce an explicit measure of flow dominance based on entropy and test its efficacy in predicting the performance of manufacturing systems. In computing entropy flow dominance ( EFD ), we aggregate information embedded in the routings of all products within a system into a single measure. EFD is designed to indicate on a 0–1 scale the level of flow dominance, where 1 represents a pure flow shop and 0 represents a pure job shop. The result is a simple measure that provides managers a way to explain and predict complex phenomena. Our experimental results indicate that EFD is a statistically significant determinant of manufacturing system performance. Furthermore, the model including EFD as an independent variable accurately predicts manufacturing system performance as measured by job flow time, flow time standard deviation, and work in process. We note that the same results can also apply to service systems, such as the “back‐room” low‐contact type systems, that have similar characteristics as manufacturing systems.

We analyze trade‐offs in the organization of public safety networks when network assets are distributed across districts and a district values network assets in its own and other districts. Comparing centralized, decentralized, and mixed organization forms, we capture two critical properties: interoperability among distributed technology‐based network assets and the ability of districts to opt‐in or opt‐out of the centralized form. We model the provision of public safety networks, where network assets are chosen by each district or by a federal government, where these assets have a positive cross‐district spillover that depends on interoperability, where investments in effort can be made to improve interoperability, and where districts can opt‐in or opt‐out of centralized provision. With the adoption of centralized, decentralized, or mixed provision as a result of districts' opt‐in or opt‐out choices, we identify conditions that determine when the districts deviate from the social optimum and thus regulatory intervention is beneficial to incent the socially optimal organization form. We show how the socially optimal organization form can be achieved through policy instruments such as a sharing rule for the cost of interoperability effort and direct government grants.

We consider a revenue management problem involving a two compartment aircraft flying a single leg, with no cancellations or over‐booking. We apply the practice of transforming a choice revenue management model into an independent demand model. Within this assumed independent model, there are two sets of demands, business and economy, each with multiple fare class products. A business passenger can only be accepted into business. An economy passenger can be accepted into economy or upgraded into business. We define a two‐dimensional dynamic program (DP) and show that the value function is sub‐modular and concave in seat availability in the two compartments. Thus the bid prices are non‐decreasing with respect to these state variables. We use this result to propose an exact algorithm to solve the DP. Our numerical investigation suggests that in contrast to standard backward induction, our method could be included in production revenue management systems. Further, when the economy compartment is capacity constrained, we observe a substantial monetary benefit from optimal dynamic upgrading compared to the static upgrading procedures currently used in practice.

Delivery time differentiation is a supply chain concept that has been implemented in various industries, but not yet in the automotive industry. One reason is that the effects of delivery time differentiation on the supply chain are not well understood. The BMW Group, for instance, has considered offering an express order option, where express orders bypass standard orders in the supply chain processes to achieve short delivery times. Express orders distort planning processes, increase operations cost, and increase the delivery times of standard orders, however the effects have not been quantified yet. This study analyzes the impact of express orders on the supply chain, when express orders are built‐to‐order. To understand the supply chain consequences of express orders better, we analyzed the relevant supply chain processes at BMW Group. We determine the effect that built‐to‐order express orders have on delivery times and on component demand. To analyze the effect of introducing express orders on expected delivery times and expected cost, we use queuing theory and derive expressions for the transient behavior of a discrete time batch queue. Our analyses indicate that many supply chain processes are only marginally affected. However, the orders to the suppliers become considerably more uncertain, which must be compensated by additional safety stock. Our results indicate that express orders can be an attractive option for BMW and other automotive companies. If the fraction of express orders stays at a reasonable level, express orders can be delivered within about two weeks.