Trading partners in our industry are leveraging more data to improve retail performance and product lifecycle management. Increasingly, different data sets are being brought together to provide more complete insights into issues and opportunities. And increasingly, those data are real-time as well.
Shopper behavior is changing rapidly. Shoppers expect more and are beginning to apply online expectations to physical retailers. To be successful in this increasingly competitive environment, retailers must tailor interface to shoppers, make shopping easier, and meet shoppers’ expectations on every visit. Issues like on-shelf availability are taking on far more importance. Data are being combined and analyzed by retailers and their vendor partners to improve marketing and promotion, provide better customer service and optimize shelf quality and selection.
Real-time data enable manufacturers and retailers to collaborate to identify root causes of failures in the supply chain. Consultants can look at product throughout the supply chain to discover packaging, storage, handling and transportation issues that lead to unsaleable product or excess supply chain costs. Combined with sales and predictive shopper behavior data, retailers can improve replenishment forecasting and manufacturers can improve production, distribution and promotion. Performing these activities using real-time can be even more valuable, particularly in situations like new product launches, where historical data are unavailable.
Returns data about unsaleables offer another asset for understanding product performance. Harnessing these data and bringing them together with sales, promotion or other data can give a true picture a product’s lifecycle or retail performance. A collaboration opportunity exists for retailers and manufacturers to better understand and influence performance, and to drive real time improvements.
Recently Inmar worked with a top-tier retailer to bring disparate data sets together, analyze those data, and identify improvement opportunities. Real-time and historical data from a number of sources to develop a more complete picture of supply chain, store and product performance. Opportunities were identified to improve product promotion and on-shelf availability, reduce unsaleables, and improve other processes, both within the retailer and with its manufacturer partners. By looking at all of the pieces of the puzzle, the retailer was able to see the “big picture” of potential opportunities.
Do you think “big data” and real-time analytics can ultimately bring retailers and manufacturers together to collaborate more effectively to their mutual benefit? I would enjoy hearing your thoughts on “big data” real-time information and how it impacts your company. Please join the discussion and leave your comments below.