Putting big data to use in the supply chain

While companies continue to get their feet wet with practical applications of big data, one industry has made tremendous headway in navigating through its big data and managing it to improve competitiveness and business excellence.

I'm talking about the retail industry — and the improvements that smarter data use and analytics have delivered not only to retail customers and customer order fulfillment, but to the supply chains behind all of these business processes that take the orders and then drive down requirements to suppliers in all corners of the world that in turn deliver a chain of finished goods to the customer.

Here are some real "difference makers":

• Using incoming Internet of Things (IoT) sensor data from its network of service stations and convenience stores, a fuel retailer knows in near real time what its fuel product mix and consumption rates are in each area of its geographical territory. Incoming point of sale (POS) data also tells it which mixes of products (besides fuel) work best in different geographical areas so it can tailor offers accordingly. The end business result? Faster time to market with offers, and better revenue positioning and agility because offers can be instantaneously changed to respond to customer demand shifts.
• To minimize exposure to risk in its global supply chain, a large manufacturer overlays geographical supplier locations with weather statistics for tornadoes, hurricanes, earthquakes, etc. — and then calculates the probabilities of natural disasters occurring through a predictive analytics program. The end business result? The company now has a way to orchestrate its suppliers so that it has backup plans and failover to suppliers in other areas of the world if a key supplier gets hit by a disaster and the incident takes down production. By proactively managing its perceived risks, the company now has a way to avoid disruption to its supply chain that can endanger revenue capture and even impair customer perceptions of the company.
• An online retailer implements real-time predictive analytics to give it visibility of which types of clothing items are selling "hottest" during peak holiday seasons. It sees that green is the most popular sweater color that is selling and quickly relays the information to its sweater supplier. Because of the strength of its analytics program, which captures and analyzes data from its website, the retailer has altered its manufacturing process with suppliers. Instead of the suppliers producing set quantities of sweaters in various colors, the suppliers now manufacture the raw sweaters without color — and then add color based upon the near real-time customer demand instructions they receive from the retailer. The end business results? A sharp drop in the post-holiday items that need to be sold out at cheap clearance levels, and the ability of the retailer to sell more units at the highest possible price.
• A supply chain logistics operation's largest operational expense is fuel for its vehicles. The company trains drivers in safe and sustainable driving practices, and then installs IoT sensors in all of its trucks that measure speeds that the drivers travel at, how long drivers keep vehicles in idle modes, and even braking habits. The end business result? A sizable reduction in operating expense (due to fuel savings from better driving habits) and a positive impact on the company's green sustainability initiative, since it has reduced its carbon footprint.

It is situations like these that make the retail industry a great example to others in the quest for value in big data — and also an industry that others can borrow big data analytics practices from.

What are the critical success factors?

• Executives in both the end business and IT were dialed in to the urgency of their situation (i.e., the need to compete fiercely and effectively in a global market, and to do it quickly).
• Big data initiatives were targeted toward very specific business aims with measurable results that were capable of contributing to the bottom line.
• Business processes were re-engineered because the companies realized they couldn't do "business as usual" if they were going to effectively use all of the new information that their big data was delivering.
• These companies weren't afraid to fail in pursuit of big data initiatives. In most of these use cases, the ultimate big data goal didn't change. but companies found that they had to revise processes and systems from what they had originally thought in order to reach the goals that they set out for themselves.