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This technology has increasingly been adopted and deployed by leading-edge companies. Decision processes such as overall sales, inventory and operations planning, "profitable proximity" sourcing, and new product innovation can all be aided by this technology. Inventory optimization is a critical component of our modern supply chain architecture.
Typical business problems that an IO application can support include:
• How much inventory should I hold of each product, and where is the most cost-efficient point to store that inventory?
• My products are often seasonal or cyclical in terms of demand; how do I most efficiently plan and deploy overall inventory?
• How will a change in a supplier or production location impact my overall inventory cost or customer service levels?
The majority of IO offerings in the current market have been built around unique optimization algorithms, and each will yield a different result. It is therefore important for prospective buyers to do their homework in understanding the inventory or business planning problem needing to be addressed.
Deployment of this type of application usually includes two types of approaches. In an interactive attended deployment, information inputs and outputs are depicted within the actual IO tool, and planners interact with the application to obtain inventory targets or perform analysis. In an automated deployment, the IO application itself sits in the background, with automated feeds of inventory or safety stock targets sent directly to the designated advanced planning system. In this mode, planners continue to interact with the planning system, but receive exception messages and certain analysis from some form of planning workbench fed by both the IO and the planning applications.
Existing deployments among manufacturers indicate that the ROI for this type of technology can be significant and meaningful. Usability across vendor offerings is improving, helping in more rapid adoption.
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