“Supply chain industry leaders have access to more information today than ever before in history,” writes Adam Robinson (@TweetsByARob). “As a result, business leaders can reap a significant return on investment by thoroughly analyzing this data. Unfortunately, some may not understand what supply chain big data truly is, how it is useful, and why they need to take advantage of it as soon as possible.”[1] Most analysts agree that to survive in tomorrow’s business environment companies must transform themselves into digital enterprises. A good place to begin that transformation is with the supply chain. As Robinson notes, the supply chain is already gathering data. Analyzing that data can help propel companies more fully into the digital age and company executives are listening to the pundits. David Weldon (@DWeldon646) reports, “Data analytics continues to be one of the top areas of IT investments and organizations expect to substantially increase their spending in this area in 2016.”[2]
The term “big data” has been around long enough that people have a pretty good understanding of what it means. In the supply chain environment, Robinson defines supply chain big data as “the ultimate compilation of data gathered in the course of business. This includes risk analysis, detailed reports of how a supply chain functions, and even lead generation. Big data is collected from a variety of sources, such as internet websites, transportation management systems, individual-employee data entry, and more.” From that definition, one can understand why the transformation to a digital enterprise can begin with the supply chain. It reminds me of what Lora Cecere (@lcecere), founder of Supply Chain Insights, stated, “The supply chain IS Business, not a department within a business.”[3] Louis Columbus (@LouisColumbus) adds, “Big data is providing supplier networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence shared across supply chains.”[4] Columbus argues that advanced supply chain analytics are revolutionizing supply chain management while many other analysts describe what’s happening as a transformation. Either way — transformation or revolution — the face of the supply chain is changing.
Availability of Data
As Robinson noted, supply chain professionals have more access to data than ever before. “The volume of big data faced by companies each day is vast,” writes Klaus Rueth, senior director analytics & supply chain services at HAVI Global Solutions. “It is estimated that 2.5 quintillion bytes of data are created every day. The challenge is to select the insightful aspects of the data and to utilize this in future supply chain planning.”[5] As Christy Pettey notes, “Availability of good data is necessary but not enough. The key to better decision making and improved supply chain performance relies on supply chain analytics.”[6]
Insight Generation
Advanced analytics provide insights far beyond anything that has been previously possible. Pettey observes, “There is an anticipated push for ‘smart automation’ in the future, which means reducing the level of human intervention by making better decisions. This can only be done by advanced analytics that can predict future scenarios, or analyze real time data and make complex, profitable decisions, sometimes on the spot.” The need goes beyond end-to-end supply chain visibility and requires a new System of Insights layer between a corporation’s Systems of Record and its external data. Fortunately, Enterprise Cognitive Systems are now available that enable these Systems of Insight to improve visibility, execute decisions, integrate data, and support corporate alignment. Regenia Sanders (@Pippiaka) and Jason Meil (@jasonmeil), executives at SSA & Co, add, “Big data can have a measurable impact on driving greater accuracy in planning, ensuring that companies make the right amount of the right product. Advanced algorithms and machine learning can facilitate increased forecast accuracy across a company’s SKUs, which drives greater turns, less waste, less inventory, and fewer stock-outs, which leads to higher EBITDA, lower working capital, and greater competitiveness.”[7] If improving decision making was the only thing that advanced analytics did for a business, they would be a good investment. Bain analysts, Michael C. Mankins and Lori Sherer (@lorisherer), insist decision making is one of the most important aspects of any business. “The best way to understand any company’s operations,” they write, “is to view them as a series of decisions.”[8]
Improved Business Processes
Columbus reports, “Big data and advanced analytics are being integrated into optimization tools, demand forecasting, integrated business planning and supplier collaboration & risk analytics at a quickening pace.” Rueth adds improved trade promotion to that list. He explains, “The success of promotional campaigns varies widely between different markets and regions. … Big data, if harnessed in the correct way, offers the opportunity to account for such disparities with extreme precision. In turn, this allows companies to manage their supply chain and stock levels more appropriately. It is therefore important to note that the long-term, strategic benefits of big data are as important as its immediate advantages.” To achieve the “extreme precision” discussed by Rueth, cognitive computing platforms are required. That’s because cognitive computing systems can analyze and make sense of a limitless number of variables. The complexity of today’s supply chains involves many more variables than older systems can handle.
Conclusions
With almost unanimous agreement that the future business environment requires companies to embrace advanced supply chain analytics, it’s surprising to learn that many companies have been slow to implement them. “Companies clearly see the benefits of leveraging big data for supply chain management,” writes Bill DuBois, a business consultant with Kinaxis, “yet studies show a surprising hesitance to move forward with initiatives.”[9] Renee Boucher Ferguson (@RBoucherFerguso) agrees that the rate of adoption has been sluggish. “While some industries like health care and retail are starting to see the transformational potential of big data and predictive analytics,” she writes, “these strategies haven’t quite panned out for supply-chain managers. Why? The biggest obstacles appear to be the cost of hiring skilled employees and the complexity of connecting nodes across an extended supply-chain network.”[10] Cognitive computing systems can help with these challenges as well by embedding much of the “human expertise” within the software and using other automated techniques to help connect nodes. The bottom line is that many of the obstacles for implementing advanced analytics are being overcome thanks to artificial intelligence. Robinson concludes, “When a supply chain leader opts to ignore supply chain big data, the company will have a huge likelihood of failure, or at least inability to grow and expand.”
Footnotes
[1] Adam Robinson, “Supply Chain Big Data: Why Supply Chain Leaders Are Using Big Data Analytics,” Cerasis, 23 September 2015.
[2] David Weldon, “Data Analytics Among Top Trends Driving IT In 2016,” Information Management, 29 January 2016.
[3] Lora Cecere, “Sage advice? Only for turkeys.” eft, 1 February 2013.
[4] Louis Columbus, “10 ways big data is revolutionising supply chain management,” Forbes, 13 July 2015.
[5] Klaus Rueth, “Supply Chain Management in a Digital Age,” Procurement Leaders, 22 October 2015.
[6] Christy Pettey, “Why Supply Chain Analytics is a Must Have,” Smarter with Gartner, 14 May 2015.
[7] Regenia Sanders and Jason Meil, “Big Data: The Latest Rage in Supply Chain Management,” CFO, 23 November 2015.
[8] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[9] Bill DuBois, “Big Data and the Supply Chain: One Important Means of Moving Forward,” 21st Century Supply Chain Blog, 22 January 2016.
[10] Renee Boucher Ferguson, “Are Predictive Analytics Transforming Your Supply Chain?” MIT Sloan Management Review, 18 December 2013.
