Supply Analytics: An Overlooked Opportunity
July 02, 2012
As companies work to elevate the value and sophistication of their products and services, they must also do the same for their “information supply chains.” This requires generating deeper insight and intelligence to uncover hidden risks and rewards from increasingly voluminous and fragmented data littered across internal and, increasingly, cloud-based databases. Supply chain analytics can deliver the needed intelligence and insight. But to do so, the analytics needs to go beyond the silos (such as focusing only on forensic spending analysis in procurement) to layer multidimensional data modeling, visualization, optimization, and other technologies on top of a harmonized view of this data.
Supply chain analytics is a broad topic; it is typically focused on the customer/demand side to tune the demand-driven supply chain. However, the supplydriven side is just as important to assure the discovery and positioning of multitier supply, which requires reliable and cost-effective inventory, capacity, and supplier capabilities. We define supply analytics simply as analyses that support improved design and management of the inbound value chain. But, a simple definition doesn’t mean that the analytics are simple. True supply analytics goes well beyond supply planning against an S&OP (Sales & Operations Plan) and sharing it with suppliers. As the old quote goes: “Ginger Rogers did everything Fred Astaire did, but backwards and in high heels.” So when a heel breaks on the supply side (for example, the Japanese tsunami, Thailand flooding, and so forth), bad things happen.
Those “bad things” represent supply chain risk potential, which has become a subject of great interest to supply chain professionals.
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As companies work to elevate the value and sophistication of their products and services, they must also do the same for their “information supply chains.” This requires generating deeper insight and intelligence to uncover hidden risks and rewards from increasingly voluminous and fragmented data littered across internal and, increasingly, cloud-based databases.
Supply chain analytics can deliver the needed intelligence and insight. But to do so, the analytics needs to go beyond the silos (such as focusing only on forensic spending analysis in procurement) to layer multidimensional data modeling, visualization, optimization, and other technologies on top of a harmonized view of this data.
Supply chain analytics is a broad topic; it is typically focused on the customer/demand side to tune the demand-driven supply chain. However, the supplydriven side is just as important to assure the discovery and positioning of multitier supply, which requires reliable and cost-effective inventory, capacity, and supplier capabilities. We define supply analytics simply as analyses that support improved design and management of the inbound value chain. But, a simple definition doesn’t mean that the analytics are simple. True supply analytics goes well beyond supply planning against an S&OP (Sales & Operations Plan) and sharing it with suppliers. As the old quote goes: “Ginger Rogers did everything Fred Astaire did, but backwards and in high heels.” So when a heel breaks on the supply side (for example, the Japanese tsunami, Thailand flooding, and so forth), bad things happen.
Those “bad things” represent supply chain risk potential, which has become a subject of great interest to supply chain professionals.
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