Terry P. Harrison

The Challenge of Closed-Loop Supply Chains

Closed-loop supply chains differ significantly from forward supply chains in many aspects. These differences are not well understood in many contexts, and the situation is complicated by many types of product returns. Progress is slow since closed-loop supply chains are rarely considered as value-creating systems, and much of the focus is on the operational aspects, rather than the larger strategic issues. Interest is growing in the US because of the potential profitability and in the European Union because of legislation. New business models need to be developed by joint cooperation between industry and academia that take a life-cycle approach to products.

Better, Faster, Cheaper: An Experimental Analysis of a Multiattribute Reverse Auction Mechanism with Restricted Information Feedback

The majority of reverse auctions for procurement use a single-attribute (price) format while providing constraints on nonprice attributes such as quality and lead time. Alternatively, a buyer could choose to conduct a multiattribute auction where bidders can specify both a price and levels of nonprice attributes. While such an auction may provide higher theoretical utility to the buyer, it is not clear that this theoretical improvement will be realized given the increased complexity of the auction. In this research, we present an ascending auction mechanism for a buyer whose utility function is known and dependent on three attributes. Motivated by a supply chain procurement problem setting, we consider quality and lead time for the two attributes in addition to price. The auction mechanism provides the bidders with restricted feedback regarding the buyers utility function. We explore, experimentally, the performance of this multiattribute auction mechanism as compared to a price-only auction mechanism. Compared with the price-only auction, we find that our mechanism design is effective in increasing both buyer utility and bidder (supplier) profits.

Analyzing the Resilience of Complex Supply Network Topologies Against Random and Targeted Disruptions

In this paper, we study the resilience of supply networks against disruptions and provide insights to supply chain managers on how to construct a resilient supply network from the perspective of complex network topologies. Our goal is to study how different network topologies, which are created from different growth models, affect the networks resilience against both random and targeted disruptions. Of particular interest are situations where the type of disruption is unknown. Using a military logistic network as a case study, we propose new network resilience metrics that reflect the heterogeneous roles (e.g., supply, relay, and demand) of nodes in supply networks. We also present a hybrid and tunable network growth model called Degree and Locality-based Attachment (DLA), in which new nodes make connections based on both degree and locality. Using computer simulations, we compare the resilience of several supply network topologies that are generated with different growth models. The results show that the new resilience metrics can capture important resilience requirements for supply networks very well. We also found that the supply network topology generated by the DLA model provides balanced resilience against both random and targeted disruptions.

A stochastic programming model for scheduling call centers with global Service Level Agreements

We consider the issue of call center scheduling in an environment where arrivals rates are highly variable, aggregate volumes are uncertain, and the call center is subject to a global service level constraint. This paper is motivated by work with a provider of outsourced technical support services where call volumes exhibit significant variability and uncertainty. The outsourcing contract specifies a Service Level Agreement that must be satisfied over an extended period of a week or month. We formulate the problem as a mixed-integer stochastic program. Our model has two distinctive features. Firstly, we combine the server sizing and staff scheduling steps into a single optimization program. Secondly, we explicitly recognize the uncertainty in period-by-period arrival rates. We show that the stochastic formulation, in general, calculates a higher cost optimal schedule than a model which ignores variability, but that the expected cost of this schedule is lower. We conduct extensive experimentation to compare the solutions of the stochastic program with the deterministic programs, based on mean valued arrivals. We find that, in general, the stochastic model provides a significant reduction in the expected cost of operation. The stochastic model also allows the manager to make informed risk management decisions by evaluating the probability that the Service Level Agreement will be achieved.

SISCO: an object-oriented supply chain simulation system

We present SISCO, the Simulator for Integrated Supply Chain Operations, an object-oriented supply chain simulation tool. SISCO advances the concept of a supply chain simulator in a number of ways. With SISCO, we introduce a fundamentally new approach to supply chain specification, storage, and model generation. The user specifies the structure and policies of a supply chain with a GUI-based application and then saves the supply chain description in the open, XML-based Supply Chain Modeling Language (SCML) format. SISCO automatically generates the simulation model when needed by mapping the contents of the SCML file to a library of supply-chain-oriented simulation classes. SISCOs object-oriented, agent-style system architecture and detailed output improve upon current supply chain simulation tools.

Global Supply Chain Design

I review principles and methods of global supply chain design, especially from two viewpoints, in this paper. The first perspective is focused on the practitioner who is interested in an overview of the key concepts and applications of supply chain design within a global context. The second theme of this paper is to assess opportunities for research to extend supply chain design in useful directions, particularly into closely related areas such as supply chain operations and information systems.

A multi-formalism architecture for agent-based, order-centric supply chain simulation

Abstract Agent-based simulation models can effectively represent decentralized systems. However, many supply-chains are order-driven, and agent modeling cannot effectively represent the order life-cycle. We present a conceptual architecture that combines simulation formalisms, allowing an agent representation of the supply-chain infrastructure while enabling a process-oriented approach to representing orders. This architecture allows for a natural, realistic representation of different supply-chain constructs and subsystems while following a consistent overall viewpoint. Our approach provides for excellent representation of supply-chain operations, allows for very detailed operational data to be gathered, and provides efficient representation of concurrent supply-chain activities in a manner that avoids preemption.

Principles for the Strategic Design of Supply Chains

I review principles and methods of global supply chain design from a variety of viewpoints, with special emphasis on processes that have been used successfully in practice. The perspective is focused on the practitioner who is interested in an overview of the key concepts and application of supply chain design within a global context.

The impact of stochastic lead time reduction on inventory cost under order crossover

We use exponential lead times to demonstrate that reducing mean lead time has a secondary reduction of the variance due to order crossover. The net effect is that of reducing the inventory cost, and if the reduction in inventory cost overrides the investment in lead time reduction, then the lead time reduction strategy would be tenable. We define lead time reduction as the process of decreasing lead time at an increased cost. To date, decreasing lead times has been confined to deterministic instances. We examine the case where lead times are exponential, for when lead times are stochastic, deliveries are subject to order crossover, so that we must consider effective lead times rather than the actual lead times. The result is that the variance of these lead times is less than the variance of the original replenishment lead times. Here we present a two-stage procedure for reducing the mean and variance for exponentially distributed lead times. We assume that the lead time is made of one or several components and is the time between when the need of a replenishment order is determined to the time of receipt.

Sales and operations planning in systems with order configuration uncertainty

This paper addresses the problem of aligning demand and supply in configure-to-order systems. Using stochastic programming methods, this study demonstrates the value of accounting for the uncertainty associated with how orders are configured. We also demonstrate the value of component supply flexibility in the presence of order configuration uncertainty. We present two stochastic models: an explosion problem model and an implosion problem model. These models are positioned sequentially within a popular business process called sales and operations planning. Both models are formulated as two-stage stochastic programs with recourse and are solved using the sample average approximation method. Computational analyses were performed using data obtained from IBM System and Technology Group. The problem sets used in our analysis are created from actual industry data and our results show that significant improvements in revenue and serviceability can be achieved by appropriately accounting for the uncertainty associated with order configurations.