Supply Chain Projects

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I recently worked with a cement manufacturer and a leading technology company to improve their supply chain network. Granularity of the two projects are very different. In case of cement manufacturer objective was to optimize the distribution and production planning. This was at a tactical level where as the objective of the work with technology company was at strategic level. Senior management was looking for some strategic level recommendations. But there are few things which were common such as issues with the data provided and structure of the results to be produced. Though structure of the results evolved gradually as we made progress on the project. I studied operation research during my post-graduation but was not using at my work place. These two project gave me opportunity to utilize my optimization skills to solve the business problems.

While working in these projects, I learnt some new terminology as well as tactics to drive the optimization projects successfully.

1. E&O (Excess and Obsolete) Inventory: Especially in case of electronic products, inventory which cannot be used or sold as it is outdated. This is a major problem for many electronic products manufactures.

2. MVA Cost: Cost incurred in value addition at any location

3. BOM uplift transfer price: Cost incurred in moving parts manufactured at one location (country) to another location (country). This type of cost occurs because of government policies on taxes, export and import.

4. Price Masking: Electronic product assembling companies have procurement contract with leading parts supplier to get better price than any outsourced manufacturer (ODM). The price information received from leading parts supplier are not shared with ODM. So ODM comes with a price/cost called Bill of material (BOM) which is higher. The difference between the price from supplier and BOM is indicated by price masking. If there is in house manufacturing facility, price masking should always be zero.


Additionally, I learnt that unless we have good data, (which are rare) we cannot produce good results, consequently recommendations. We know that modeling any problem is an art as well as a science but presenting the results is definitely an art. While writing the optimization code one should remember that optimized solution must be presented in such a fashion that a business person can easily follow it. The code must be written in agile manner, as we make in progress in the project few additional requirements may come up. Incorporation of those new requirements in the model should not take much time.

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