Book
Supply Chain Optimization through Segmentation and Analytics
In a meeting with a COO (Chief Operating Officer) the author was told, “We have had dozens of consulting companies through here, each telling us that they know the best way to solve our productivity and logistics problems. But every one of them only solves a small part of the problem. Are you going to come in here and give me another solution that fixes a part of our problem but leaves us missing the mark in other areas of our business? In your opinion, which solution is best?” The author was quick to jump at the opportunity and stated, “All of them are best.” “What do you mean?” asked the general. “You’ve already identified the problem,” the author continued. “Each solution fixes part of your planning and scheduling problems. But none of them fixes all the problems. What I would recommend is a segmented approach. Your facility does not need a ‘one size fits all’ solution. It needs a segmented solution. Within each segment, a different planning and scheduling tool is optimal. We need to define the segments, select the appropriate
tool for that segment, and execute using that tool. Then
you will finally achieve optimality.”
With that, the author and the COO proceeded to discuss
the details of a segmented planning and scheduling approach
for his extremely complex supply chain. That is what this book
will teach the reader how to do.
First let us take a look at how good you, the reader, are at
segmentation. I have included a planning and scheduling test
created by Einstein. It is called the Albert Einstein Riddle.
Albert Einstein wrote this riddle and claimed that if you could
solve this “pure logic” problem you must be in the top 2% of the
intelligent people in the world. He starts with these rules
1. On a street there are five houses painted five different
colors.
2. In each house lives a person of a different nationality.
Judul | Edisi | Bahasa |
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Strategic supply chain management | en | |
Does big data mean big knowledge?: knowledge management perspectives on big data and analytics | Vol. 21 Iss 1 | en |