As we all know, redesign and optimization of supply chain networks is a very hot topic these days and every other day, we read stories of companies trying to overhaul their transportation network for best possible profits and improving their ability to meet the customer demand. My first introduction to Supply Chain Networks came at the Decision Making under Uncertainty Class when we were taught to come up with Solver models to design a low cost network Strategy to solve transportation problems.
IBM ILOG Supply Chain Optimization, Oracle® Strategic Network Optimization are some of the famous examples of Supply Chain Network Optimization tools used by companies to design an efficient network. These software products, allow you to decide the most optimal network, taking into consideration the complex cost assumptions and other constraints that drive the business. These software run with such high Business Intelligence that it allows you to decide on answers for tough trade off questions such as inventory pre-build versus overtime, or single source vs. multi-source using what-if Scenarios and judge based on evaluation of currency fluctuations and impacts of mergers and acquisitions to your distribution and supply network. This was fascinating for me, however I was interested in knowing how accurate the results of these software can be, when the demand is not predictable. This led me to research on such cases and one interesting reading was on Northpole.com, a leader in the Toy Industry.
Northpole is considered to have one of the most efficient supply chains in the world. At Northpole, the supply chain effectiveness starts at the top, where the CEO, clearly understands the need for an effective supply chain for the company’s success. Northpole considers Customer Intimacy as one of its key value drivers and hence the supply chain strategy is essentially a five year roll out plan with annual updates to budgets. Northpole acquired a network planning Tool Company and has deployed a small group of staff to evaluate network improvements, on a constant basis and provide expert thoughts on the results that the software generates. This tool is used for determining sourcing strategies, identifying constant flow path optimization and to determine which products need to be made in-house.
Northpole has a highly seasonal but also at times has an unpredictable demand. The company is using techniques such as Predictive Demand planning and Sensing to aid its network team as the toy demand is highly unpredictable. They constantly monitor social networking sites for clues on demand and tweak their networks accordingly. This company essentially follows a pull-based strategy to draw down inventories of slow movers and tags with companies like overstock.com and other retail channels to get rid of the excess stock. The most interesting part of managing their network was that they use the Just-in-time technique for certain products like Board Games that use standard boards and boxes. These are printed at the last moment based on need of the product.
However, Northpole is not completely free of Supply Chain woes. Highly fuel costs and aging workforce are adding concerns to the network optimization team. It will be critical for Northpole to consider these factors in taking networking decisions. I would love to hear your thoughts on the idea of using such networking software with dedicated teams and what kind of unquantifiable/qualitative aspects, that need to be considered in complementing the results of the software, given a situation of variable/unpredictable demand and how it ultimately affects the final decisions of deciding an optimal network.