Robert A. Orzell

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.

The ongoing challenge: an accurate assessment of supply linked to demand to create an enterprise-wide end to end detailed central supply chain plan

Organizations can be viewed as an ongoing sequence of loosely coupled decisions where current and future assets are matched with current and future demand across the demand-supply network at different levels of granularity ranging from a placing a lot on a tool to an aggregate capacity plan across a five year horizon. Since the early 1990s detailed enterprise wide central planning has become a key member of this ¿decision suite.¿ Despite its importance, most organizations execute central planning with ¿limited levels of accuracy or intelligence.¿ Early in the evolution of ¿central planning engines¿ IBM determined that ¿extended accuracy¿ was an important component of supply chain efficiency and customer satisfaction and made a substantial investment to develop a central planning engine which could handle the scope (complexity) and scale (size) of large organizations. This paper covers the value from this investment and the technical details of combining heuristics and optimization.