Customer Case Study
BRIDGEi2i helps a Fortune 100 networking giant to plan their inventory flexibility requirement
through...
Customer Case Study
Non Linear Programming, Optimization & Scenario Generation
Based on the simulated demand scenarios, a ...
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BRIDGEi2i Case Study - Inventory Flexibility for Fortune 100 Networking giant

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BRIDGEi2i helps a Fortune 100 networking giant to plan their inventory flexibility requirement through discrete choice simulation models and non-linear optimization techniques.

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BRIDGEi2i Case Study - Inventory Flexibility for Fortune 100 Networking giant

  1. 1. Customer Case Study BRIDGEi2i helps a Fortune 100 networking giant to plan their inventory flexibility requirement through discrete choice simulation models and non-linear optimization techniques. Business Challenge The client is Fortune 100 technology giant primarily operating in the networking space. The company invests in R&D and design, but the manufacturing is outsourced to partners in China and other geographies. Manufacturing and shipping lead times are large and careful inventory planning is required to optimize supply chain costs. The company has a rigorous forecasting & consensus planning process, yet forecast accuracy is sometimes low, leading to missing orders and losing sales. The company has also a flexibility arrangement with its partners, which is communicated as much as 6 months in advance over a “Flexibility” requirement for product shipments which is over and top of forecasts. The main Business challenge was to optimize and rationalize this “Flexibility” arrangement with partners for which the company bears significant inventory holding & order costs, at the same time minimizing the lost sales on account of non-availability of products. BRIDGEi2i Solution BRIDGEi2i engaged in a data driven consulting engagement to build a flexibility calculation tool that learns from historical forecasts and actual bookings (the forecast errors). It takes into account the historical error distributions, the lost sales cost, the inventory holding and ordering costs, the lead time errors etc. and builds a simulation model based on this information. By simulating various scenarios of expected demand and available forecasts, its then uses a complex non-linear optimization technique to find that level of “Flexibility” at which the total cost would be minimized based on over 5000 simulation patterns. This complete and complex system is applied to around 1500 products that contribute to more than 50% of revenue for the company. While the solution developed in SAS results are deployed via an Excel based tool that allows demand planners to run “What-If” scenarios and provide a recommended flexibility requirement to their partners. Product Grouping In order to minimize volatility of individual’s products, products are segmented based on their historical error patterns and shipment quantity using a two stage density clustering technique. For products with sparse data, suitable proxies were identified and their data was used for the analysis Distribution Fitting and Demand Simulation Forecast errors for 24 month histories were used for each group and a suitable statistical distribution was fitted to identify their pattern. Choices of multiple continuous distributions were used and the best distribution was chosen. The chosen distribution was then sampled for over 5000 cases, each one representing a possible future demand scenario. For more details contact us: enquiries@bridgei2i.com © BRIDGEi2i Analytics Solutions Information Insight Impact
  2. 2. Customer Case Study Non Linear Programming, Optimization & Scenario Generation Based on the simulated demand scenarios, a system was created to calculate service level, inventory cost, lost sales cost and ordering cost for every scenario. Inventory levels have a non- linear relationship with costs and service level and were optimized to obtain the right level at which the total cost would be minimized, given the constraints that a minimum service level of demand would be met with. Excel based Visualization Tool for solution deployment An Excel based tool was developed using VBA for deployment of the solution and distribution to demand planners. The Visualization included product grouping analysis, scenario analysis and optimization visualization. About BRIDGEi2i BRIDGEi2i is an analytics solutions company partnering with businesses globally, helping them achieve accelerated outcome harnessing the power of data. BRIDGEi2i helps companies to BRIDGE the gap between INFORMATION, INSIGHT and IMPACT in their journey to institutionalize data driven decisions across the enterprise. To know more visit http://www.bridgei2i.com For more details contact us: enquiries@bridgei2i.com © BRIDGEi2i Analytics Solutions Information Insight Impact

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