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Use Supply Chain to Predict Consumer Demand

Perhaps no industry deals with volatility in consumer demand quite as much as consumer electronics. The combination of shrinking product lifecycles and broad, rapid adoption of new products can wreak havoc on a supply chain. Electronics manufacturers have worked to fine-tune their supply chains to better manage this volatility, using data collection, aggregation and visualization technologies to gain visibility into supply chain activities. This has allowed them to compress their reaction times and minimize the impact of unexpected market shifts.

Yet, even with these tools in place they are still reacting to changes that have occurred and so are at least a step behind the markets they serve.  The next phase in supply chain management involves shifting from this reactive mode to proactive management by taking a “big data” approach to consumer demand in which the supply chain plays a pivotal role.

When data from the supply chain and other internal sources is integrated and analyzed in context with external sources of information, both structured and unstructured, the organization can begin to predict future shifts in demand with greater accuracy. The supply chain then becomes just one of the business assets—along with production, marketing and sales—the organization can engage to capitalize on positive shifts and minimize the impact of negative shifts.

The first step in this evolution is ensuring the foundation is in place in the form of accurate, complete and timely supply chain data. Some organizations may believe they have this foundation solidly in place—and many do—but an audit of supply chain data collection practices may still be warranted to ensure the right data is consistently being collected at the right time.

Once the foundation is confirmed, technologies can be implemented to integrate supply chain data with data from other internal as well as external sources, such as news feeds, weather predictions and social media. When this complex web of data is subjected to statistical analysis and human interpretation, it creates a clearer picture of future demand.

Rather than bearing the primary burden of reacting to shifts, the supply chain becomes part of a predictive business model in which all aspects of the business are working in concert to meet demand. For example, imagine if your business could accurately correlate social media activity with the success of new product introductions. You have more accurate sales predictions in advance of a launch and less need to adjust distribution immediately following the launch. 

The supply chain has already validated the value of data collection and analysis as organizations have used supply chain data to enhance inventory management, speed product movement and improve efficiency. Now, the next phase in this evolution is to build on that foundation and use supply chain data as the cornerstone of big data initiatives that move the organization from a reactive to a proactive business model. 

Let us know how your organization is using these critical resources to enhance the business in the comments section below.

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