How can predictive ordering impact fresh departments?
Dive Brief:
- A new platform called Shelf Engine uses predictive analytics to create bulk orders for fresh food distributors and retailers, according to GeekWire.
- Using data provided by the supplier or retailer, Shelf Engine creates an optimized order list made to save money and reduce food waste. It is, according to its creators, one of the first predictive ordering systems made specifically for the perishable food industry.
- Shelf Engine recently received $800,000 in seed money to scale up its platform.4 profit-driving costing methods food manufacturers need to know
Dive Insight:
For many years, retailers have relied on a mixture of sales trends, in-house data, and intuition to drive their product ordering.
With the industry more crowded and competitive than ever, supermarkets — particularly large chains with deeper pockets — are betting superior technology like predictive ordering can give them an edge. According to Transparency Market Research, the global market for predictive analytics, worth just over $2 billion in 2012, is expected to reach $6.5 billion by 2019.
Industries of all kinds use predictive technology, from banks to insurance companies to restaurants. For retailers, these programs hold the promise of reducing waste and profit loss, as well as boosting customer loyalty.
Whole Foods’ partnership with data firm dunhumby, announced during the retailer’s recent earnings call, signals a move towards optimizing product ordering and merchandising through customer data and predictive technology. Kroger’s new high-tech store model, which offers real-time recommendations to customers, suggests a similar company strategy.
The fresh industry that Shelf Engine deals in can be particularly tricky, since goods tend to be both pricey and perishable. If the company’s platform can truly optimize bulk ordering, that creates a nice one-two punch of cost savings along with food waste reduction that could be marketed to consumers.
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