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The Ongoing Challenge for a Responsive Demand Supply Network: The Final Frontier—Controlling the Factory

  • Kenneth Fordyce
  • R. John Milne
Chapter

Abstract

Over the past 20 years organizations have put significant energy into making smarter decisions in their enterprise wide central planning and “available to promise” processes to improve responsiveness (more effective use of assets and more intelligent responses to customer needs and emerging opportunities). However, firms have put only limited energies into factory floor decisions and capacity planning and almost none into generating a tighter coupling between the factory and central planning. The bulk of the work to make “smarter factory decisions” has focused on two simple metrics: increasing output and reducing cycle time—often without accommodating the need to run lots at different velocities and without recognizing how the operating curve (trade-off between lead time and tool utilization—Appendix 3) links them. In fact, many of the recent Lean initiatives have focused on eliminating variability to induce simplicity to achieve improved output or cycle time without concern for the impact on responsiveness or capacity. The purpose of this paper is to (a) make clear the critical, and often overlooked, role of factory responsiveness with respect to central planning; (b) explain how traditional factory planning and the current application of Lean can severely impact the firm’s responsiveness; (c) elaborate on touch points between central and factory planning demonstrating simple tactical methods that can improve responsiveness and protect the factory from churn; (d) explain why smarter dispatch scheduling is critical to successful responsiveness; and (e) outline the basics of smarter dispatch scheduling. Although the focus of this paper is the factory, many of the core concepts apply to a wide range of industries from restaurants to health care delivery.

Keywords

Cycle Time Wait Time Operating Curve Central Planning Average Wait Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  1. 1.IBM Systems and Technology GroupPoughkeepsieUSA

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