Cycle Time Reduction Through W.O.R.C.


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Introduction

Lean, theory of constraints (ToC), quick response manufacturing (QRM), cross-training, and staticstical process control (SPC) are powerful, tried and true methodologies for process improvement. However, these tools are rooted in high-volume manufacturing environments and don’t always play nice in a high-mix, low volume (HMLV) operation. The new W.O.R.C. manufacturing strategy was specifically developed to overcome these shortcomings while capitalizing on their strengths.

Limitations of the Current Toolset

Lean is a collection of tools and methods designed to eliminate waste, reduce delays, improve performance and reduce costs. Lean focuses on eliminating non-valued added activities, as opposed to more traditional improvement efforts, which focus on reducing the time in value-added steps. The problem with lean is that many of the tools work best in a high-volume process that has very little variation in product mix.

ToC is a methodology that focuses on removing bottlenecks from a process through a series of five steps: Identify the constraint, Exploit (improve) the constraint, Subordinate (align all activities), Elevate (additional actions) and Repeat. The problem with ToC is that, by definition, eliminating one bottleneck creates another, and in a high-mix process the bottlenecks can change with the mix.

Quick Response Manufacturing (QRM) is a cellbased strategy closely related to focus factories that was developed specifically for HMLV that has been gaining popularity over the past few years. The problem with QRM is that it works best when equipment sets from several sequential departments can be physically organized into small cells. This becomes problematic in operations that have processes requiring capital-intensive environments like plating, clean room imaging, etc., where setting up a single machine in a cell is prohibitive.

Cross-training is critical to manufacturing continuity to overcome employee absences, specific department surges, and other unforeseen events that would compromise ongoing processes. The problem with cross training is that it is typically employed randomly, meaning that employees are cross trained based on their past experience or interest with no strategy to cross train across closely related tasks.

SPC uses statistical analysis to monitor and control processes. Once again, the problem with SPC is that it works best in a mature, high-volume process with a stable product mix. Companies tend to focus SPC on product specific attributes that change with each product, which creates challenges with processes that change part numbers multiple times daily, like in printed circuit manufacturing.

To read the full version of this article which appeared in the August 2017 issue of The PCB Magazine, click here.

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