Supply chain analytics has been around for a decade, but a growing chorus of experts believes the technology is...
finally on the verge of taking off, propelled by the demands of globalization and enabled by the convergence of mobile and cloud computing.
Supply chain analytics is “any sort of prepackaged analysis that provides decision support and insight,” said Robert Kugel, senior vice president of research at Ventana Research Inc., a benchmarking and advisory firm based in Pleasanton, Calif.
When applied to supply chains, business intelligence (BI) can provide the basic reporting and querying tools for accessing demand data, such as retail sales, and supply-side data on incoming raw materials, for example. It is often reactive in nature, whereas more advanced BI tools add supply chain analytics into the mix, according to several experts. Predictive analytics, for example, can anticipate changes in buying patterns or recognize when a key supplier is at risk of failing. BI dashboards are best used for monitoring supply chains and detecting problems via alerts, while analytics helps drill down to find root causes and identify solutions.
Predictive analytics allows manufacturers to formulate intelligent responses to the reality shown in the BI data. It can include optimization algorithms, pattern recognition and what-if analysis, and could soon verge into artificial intelligence that can evaluate multiple if-then decisions and make recommendations.
Meanwhile, the type and volume of supply chain data is growing exponentially, from point-of-sale (POS) data on purchases, to real-time inventory tracking provided by global transportation carriers. Now, instead of applying BI and analytics tools to more limited sets of older data, Kugel said, manufacturers can put them to work on “big data,” much of it real time, that is now streaming in from service providers and the smartphones carried by truckers and salespeople. Armed with this timelier, more accurate information, companies can be better at anticipating supply and demand and respond more intelligently.
“People are looking for easier and better ways to use the data that they’re collecting, and they’re collecting a much wider set of data than was available years ago,” Kugel said.
Mobile analytics could play important role
The data will only expand as the predicted convergence of cloud computing, mobility, social networking and location-based services becomes a reality, according to Rich Sherman, North America director for the Supply Chain Council, a benchmarking and best-practices organization based in Cypress, Texas.
“I can be tracking material in motion, globally, anywhere,” Sherman said.
At the same time, cloud computing is making sophisticated, “best of breed” analytics more accessible and affordable. “It changes the whole nature of [supply chain analytics] deployment,” he said. “It gives you the capability to access more expertise in a shorter period of time.”
Sherman said the mobile-cloud convergence has led to “data as a service” providers who collect information from shoppers’ GPS-equipped smartphones. The services are effectively analytics platforms that retailers such as Best Buy are already using to separate serious buyers from tire-kickers. For example, when a customer is in the store, the retailer can transmit price-matching guarantees and other offers to avoid losing business to a nearby competitor recommended by the customer's smartphone app. Manufacturers could use the data to plan more effective promotions.
Cloud-based mobile analytics is not only affecting the demand side. “It’s everybody,”Sherman said. “You take a look at truck drivers. Today, the most sophisticated carriers have onboard computer systems, but the older owner-operators can’t afford that. But what every owner-operator has is a smartphone.”
Improved use of analytics can also boost the effectiveness of the alerting systems that many supply chain managers use, Sherman said. Without analytics to filter alerts, the number of alerts can exceed 300 per day. With analytics, manufacturers can deploy algorithms that make decisions on the alerts or offer recommendations on how to respond to them.
Manufacturers can also use supply chain analytics to compare the performance of their supply chains against industry benchmarks. Sherman said many companies use the Supply Chain Council’s Supply Chain Operations Reference (SCOR) framework to set up performance metrics and analyze them against a database of other companies’ metrics. Some use Lean Six Sigma techniques to measure supply chain efficiency. Consulting firms and other organizations also offer benchmarks for supply chain analytics.
Analytics for supply chain best practices
Supply chain analytics can improve the quality and speed of decisions, according to Kugel, who cited the supply chain disruption caused by the devastating March earthquake in Japan as a prime example of a crisis demanding a quick, effective response.
Kugel said supply chain analytics can be especially effective for manufacturers that use integrated business planning methods such as sales and operations planning (S&OP) and collaborative planning, forecasting and replenishment (CPFR) to enlist suppliers and customers in managing their supply chains. Effective analytics can go way beyond the standard inventory optimization task of minimizing stockouts by helping to determine the true cost of certain types of stockouts, he said. Planners can then use this deeper analysis to make more profitable trade-offs between inventory cost and availability.
Analytics can also be used for dynamic rebalancing of fulfillment. For example, a manufacturer might use an analysis of customers to decide which should be given 100% fulfillment guarantees and which can receive lesser service levels without negatively impacting the bottom line.
Analytics can also lead to more efficient shop floors, as production managers receive more accurate forecasts and find more cost-effective ways to meet customer requirements.
Despite the huge potential of supply chain analytics, Kugel said, few manufacturers even know what is happening in their supply chains, let alone apply complex analytics to them. But a few technology and consumer-goods manufacturers are effective users of supply chain analytics, including General Electric Co., Cisco, Intel Corp., and Procter & Gamble. “FedEx is an IT shop that flies airplanes, and they do some good analytics, too,” he said.
For his part, Sherman would add to that list DuPont, Kraft Foods Inc., Johnson & Johnson Services Inc., and the diversified health care products manufacturer, Baxter International Inc. He said the companies on the AMR Research (now Gartner) list of the top 25 supply chains tend to be big users of analytics.
Sherman said early adopters of supply chain analytics have roughly a 50% advantage over competitors when it comes to supply chain expenses as a percent of revenues. “The leaders are several years if not a decade or more advanced than the average company,” he said.