A recent article on CFO points to a topic gaining strong momentum in the business press: the supply chain is the next big thing for big data to address. The authors, Regenia Sanders and Jason Meil, detail why this focus has become so compelling: “Big data can have a measurable impact on driving greater accuracy in planning, ensuring that companies make the right amount of the right product. Advanced algorithms and machine learning can facilitate increased forecast accuracy across a company’s SKUs, which drives greater turns, less waste, less inventory, and fewer stock-outs, which leads to higher EBITDA, lower working capital, and greater competitiveness.” Companies clearly see the benefits of leveraging big data for supply chain management, yet studies show a surprising hesitance to move forward with initiatives. In Inbound Logistics, a Capgemini Consulting study is cited showing nearly all shippers and third-party logistics providers (3PLs) believe big data is vital to their efforts to improve tactical and strategic operation of their supply chains. Yet only eight percent of shippers and five percent of 3PLs have implemented big data initiatives in their supply chain. The title of a Fortune article—“Big Data Could Improve Supply Chain Efficiency—If Companies Would Let It”—further underscores the conundrum. A number of challenges must be addressed for big data/supply chain initiatives to move forward, including cost, cultural resistance to change, and in many cases a disconnect between internal IT and supply chain organizations; but one overarching challenge is the nature of supply chains in the current global commercial environment. Today’s global manufacturer may have hundreds of factories and distribution centers, and hundreds of thousands of items it sells. An item may be sold in a single market (e.g., Spain or Portugal) but thanks to the supply chain it travels through multiple countries (originating, say, in Asia) along the way. A product going through a network like that may also go through multiple ERP systems, number changes, and various unit of measure changes. In such a network, how is demand translated into supply in a single flow? Consolidating the data is one thing, but addressing data harmonization is a big component of making sense and use of big data. Visibility is about bringing up all the information crossing a network into a single, real-time support system so when a company sees a change in demand they can immediately translate it across the entire network. That lets them see where and how demand can be satisfied. When companies realize that the technology is at hand to do this—to harmonize and make useful what seems on the surface incredibly complex and dissonant—the hesitation to move forward with big data initiatives to improve supply chain operations is likely to dissipate. After all, what supply chain decision-maker wouldn’t want the following at his or her fingertips?
- Multi-tier demand and supply chain visibility
- Long-term and short-term demand and supply chain planning
- Supply chain risk identification and mitigation
- What-if analysis and execution
- Financial and operations performance management
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