2. Improve Supply Chain Efficiency
Cost efficiency, cost reduction, and spend analytics will continue as top business priorities in supply chain management. Embedding big data analytics in operations leads to a 2.6x improvement in supply chain efficiency of 10 percent or greater, according to Accenture.
3. Better Assess Supply Chain Risk
Sixty-one percent of companies regarded as leaders in supply chain management consider supply chain risk management very important. Those same leaders also recognize the need for capabilities that provide greater visibility and predictability across their supply chains (Source: Accenture). Big data can help assess the likelihood of a problem and its potential impact, and support techniques to identify supply chain risk. Combining the analysis of historical data, risk mapping, and scenario planning can enable a risk management approach for early warning.
4. Improve Supply Chain Traceability
Traceability is often directly linked to supply chain risk. For 30 percent of companies, traceability and environmental concerns continue as the biggest issues to watch for (Source: Ethical Corporation). Traceability and recalls are by nature data-intensive. Big data has the potential to provide improved traceability performance; it can also reduce the thousands of hours involved with accessing, integrating, and managing product databases that capture products that should be recalled or retrofitted.
5. Agility - Improve Reaction Time and Order-to-Cycle Delivery Times
Ninety percent of companies say that agility and speed are important or very important to their business (Source: SCM World). The ability to quickly and flexibly meet customer fulfillment objectives is rated the second most important driver of competitive advantage across all industries. Embedding big data analytics in operations can have an impact on organizations’ reaction time to supply chain issues (41 percent) and can lead to a 4.25x improvement in order-to-cycle delivery times, according to Accenture.
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