R. John Milne

Matching Assets with Demand in Supply-Chain Management at IBM Microelectronics

In the early 1990s, the IBM Corporation decided that its microelectronics division should expand from producing parts exclusively for other IBM locations to producing a range of products for diverse customers. To overhaul its supply-chain-management applications to handle the new business, it developed intelligent models to match assets with demand to determine which demands it could meet when and to provide manufacturing guidelines. In 1994, the PROFIT team began applying OR techniques to build these tools, interweaving linear programming with a traditional material resource planning algorithm and a heuristic matching process based on clues established in the explosion algorithm. The team has deployed three core applications: a weekly division run that determines customer commitments and manufacturing requirements, daily manufacturing runs that identify the best use of manufacturing resources to meet division requirements, and a division available-to-promise application that facilitates fast response to customers placing orders (not described). This work has improved manufacturing utilization and customer-order response time.

IBM Blends Heuristics and Optimization to Plan Its Semiconductor Supply Chain

IBM uses operations research techniques to plan its enterprise semiconductor supply chain. The scale and complexity of this planning problem make developing robust supply chain optimization tools a challenge. Pure optimization methods are computationally infeasible, and fast heuristic methods alone generate poor results. Consequently, we developed a method that decomposes the problem by dividing the bills of materials product structure horizontally and vertically into complex and simple portions that are based on the major stages in semiconductor manufacturing and the choices of supply chain paths for building parts. The method then solves the complex portions with a mixed-integer program and the simple portions with fast heuristics that contain small embedded linear programs. A unique pegging algorithm, an explosion heuristic, and an implosion linear program enable coordination among these portions. The result is a unified production, shipping, and distribution plan with no evidence of the original decomposition. This method has helped IBM to improve its asset utilization, customer service, and inventory levels.

The Ongoing Challenge: Creating an Enterprise-Wide Detailed Supply Chain Plan for Semiconductor and Package Operations

In the mid-1980s, Karl Kempf of Intel and Gary Sullivan of IBM independently proposed that planning, scheduling, and dispatch decisions across an enterprise’s demand-supply network were best viewed as a series of information flows and decision points organized in a hierarchy or set of decision tiers (Sullivan 1990). This remains the most powerful method to view supply chains in enterprises with complex activities. Recently, Kempf (2004) eloquently rephrased this approach in today’s supply chain terminology, and Sullivan (2005) added a second dimension based on supply chain activities to create a grid (Fig. 14.1) to classify decision support in demand-supply networks. The row dimension is decision tier and the column dimension is responsible unit. The area called global or enterprise-wide central planning falls within this grid.

The IBM advanced planning system for managing next generation demand-supply networks

IBM formed a team in the mid 1990s to develop a next generation demand-supply matching system. Using advanced heuristic algorithms and Linear Programming (LP), this team built a comprehensive system comprising solutions covering the complete spectrum of Supply Chain Planning (SCP). This systems cutting edge innovations and tremendous business impact have generated dozens of intellectual properties and earned major awards in operations research achievement for IBM. Since 2005, this system has also become a daily solver at Analog Devices, Inc. IBMs system fully supports a long term, incremental deployment of advanced SCP functions whenever needed with minimum effort required.

Incorporating contractual arrangements in production planning

The semiconductor supply chain is full of complexities outside of the traditional order, make/buy, and deliver process. One critical challenge occurs when part of a semiconductor fabricators capacity is allocated to produce wafers designed by and provided to fabless companies. In this situation, linked customer requirements are expressed simultaneously at both the semiconductor level of the supply chain and the finished goods level. As a result of the complex contractual relationships between the foundry and the fabless company, a new solution model and method is needed to determine a production plan. In our approach, two linear programming (LP) models are solved sequentially where the results of a first LP are post-processed into input for a second LP. We describe the application of this approach for two different types of contracts where the goal is maintaining as much common modeling as possible while ensuring the unique features of each contract are covered. For one type of contract, the first LP model determines the minimum quantities of wafers required to be released into the fab to meet the contractual obligation; these required starts are added as a constraint for the second LP model. For the other type of contract, the first LP determines production at one level of the bills of materials and feeds these outputs into a second LP that determines production for later stages of manufacture.

Enhancing mathematical programming models to account for demand priorities increasing as a function of delivery date

Linear and mixed integer programs are frequently used to allocate resources to support a prioritized statement of demands or needs. This paper describes how to enhance these mathematical programming formulations to reflect the dynamic relative importance of demands, namely the priorities of demands – relative to other objectives – increasing as a function of the time of demand fulfillment. We illustrate the modeling using data from a light-emitting diode manufacturer from Taiwan.

A production scheduling problem with sequence-dependent changeover costs

This paper presents a novel three-step heuristic algorithm which assigns products to parallel heterogeneous machines and determines the sequence in which the products are run on each machine. The objective is to minimise the total set-up cost, which is sequence-dependent. In the algorithm’s first step, products are assigned to machines while evaluating each potential assignment’s impact on the tentative sequence. In the second step, an optimal sequence is determined given the assignment from the first step; this is done using a dynamic constraint-generation method. The third step improves the solution through a local search heuristic. The developed algorithm is very efficient in solving large problems and has been implemented at the corresponding author’s organisation.

Special Issue Editors' Note: Wagner Prize Second-Place Winner Lee et al./Grady Health System

The Grady Health System application earned special recognition and inspired special publication treatment. As a 2013 Wagner Prize finalist, it is the first paper to be named a second-place winner; previously only the first-place finalist had been designated as winner. In addition, it is the first to become an Edelman Award finalist in the Edelman competition immediately following the Wagner competition. Therefore, it is the first to be honored with special publication treatment in which the project will be presented in a combined Wagner–Edelman paper. Because the judging criteria of the two competitions are complementary, a combined paper permits the authors to fully describe the Grady project, while efficiently reviewing aspects of that project that were considered by the judges in both competitions. The Wagner criteria emphasize innovative methods, although considering beneficial impact and other attributes. The Edelman criteria emphasize beneficial impact, although considering innovative methods and other attributes. The comprehensive Grady paper is scheduled to be published in the upcoming Edelman special issue (January–February 2015) of Interfaces. In the unlikely event of a delay, it will appear in a subsequent issue. Here are brief highlights of this important work: The subject is hospital emergency departments: improving effectiveness, especially quality and timeliness of care, while at the same time improving efficiency. The project is the first application of a new approach, as accomplished at Grady Memorial Hospital, Atlanta, Georgia. The approach entails customizing and implementing a decision support system that incorporates an innovative combination of machine learning, simulation, and optimization. Customization at Grady Memorial Hospital involved extensive data collection from before and after implementation. The result over several implementation stages was significant gains in effectiveness, measured by reduced average length of stay, lower number of readmissions, increased average throughput rate, and greater ability to base treatment decisions on interactions with departments outside of the emergency department. Simultaneously, the result was gains in efficiency, achieved through initiatives such as process consolidation, activity tracking, addition of a clinical decision unit, and staffing revision, all of which could be accomplished without significantly changing the physical layout and without investing additional funds or other resources. The same approach, with details adapted to particular environments, has been implemented successfully at several other private and public hospitals. A website that contains related photos, presentations, hospital-insider notes, and an Institute of Medicine–National Academy of Engineering letter concerning the significance of the work is available at http://www2.isye.gatech.edu/ medicalor/EDadvances.

Operations Research Capstone Courses for Business Majors with Analytical Backgrounds

Clarkson University provides a one-semester capstone design course required for undergraduate seniors majoring in Engineering and Management. In this paper, we discuss how the capstone experience has been facilitated when the projects are operations research OR based. Key aspects include multiple student teams working on a single clients problem, student experience customized to the project, a client focused approach, and coaching of students whose academic background includes a single course dedicated to OR as well as other courses in engineering and management. We suggest why our practices may be applied at other universities with analytically talented business students. Many of our recommended practices apply to industry projects for students from any major e.g., finding projects with appropriate scope.

Introduction: 2012 Franz Edelman Award for Achievement in Operations Research and the Management Sciences

This special issue of Interfaces is devoted to the finalists of the 41st annual competition for the Franz Edelman Award for Achievement in Operations Research and the Management Sciences, the profession’s prestigious award for the practice of operations research and business analytics. As in previous years, the finalists this year cover a wide range of industries, functions, and countries around the globe.