Process Optimization and NVA Reduction by Network Analysis and Resequencing

Process Optimization and NVA Reduction by Network Analysis and Resequencing

Anand Sunder (Texas Tech University, Lubbock, USA)
Copyright: © 2019 |Pages: 17
DOI: 10.4018/IJAIE.2019010102
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The article discusses a methodology to reduce cycle times through an algorithmic, analytical framework for sequential process flows. Studying process flow flexibility for reducing bottlenecks has always continued to open new research avenues. This methodology has been formulated keeping in view of sequential manually executed assembly processes, where a single operator is involved, the process steps are entirely manual or semi-automated. The concept can also be extended to other scenarios by computing a process flexibility measure in terms of time, resources and methods. Essentially this article talks about the use of an algorithm for effective scheduling on assembly lines, computing the most optimal path that that the process flow could have taken given how the process has proceeded. Current activity scheduling methods tally the progress against a plan, which is ideal and does not account for unforeseen wait times. The output of the algorithm which is the most optimal approach as computed for a given scenario will help achieve rhythm and reduce wasted time in places where it's possible to avoid them. A standard tool to measure the exact amount of compressible wait time or Muda Type of waste is chosen, the overall equipment efficiency was adopted for gauging this approach. This discusses the generalization of the principle used and its formulation as an algorithm and a flow chart.
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4. Objectives

Objective of our paper is to demonstrate the algorithm with primary focus on delay reduction for effective activity scheduling. The emphasis is on demonstrating the least possible waste d time that could have been achieved given a process flow and its constraint. Emphasis is primarily to reduce delays from the learning from unnecessary wait or wasted time, as to how it could have been replaced by a productive activity. Learnings from live process flows and integrating it with the algorithm will serve as a learning tool, with which future process flows will be benchmarked to. Figure 1 shows the objective flow chart.

Figure 1.

Objective flow chart


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