Big, big, big data. But, no "information." The focus on better utilizing the data that resides deep in our extended supply chain continues to capture the attention of analysts, academics and practitioners alike. Data analytics is the science (sometimes art) of assembling and examining raw data to help draw conclusions and take decisions by transforming data into information. Such valuable information helps us prove or disprove models, simulations or theories.
A recent Accenture study of organizations in the United States and the United Kingdom revealed that the adoption of analytics is growing and that, compared to a similar study conducted three years ago, the use of analytics as a primary predictive tool has tripled. Sadly, less that 25 percent of the respondents said they were very satisfied with the business outcomes delivered by their analytics investments and only 39 percent said that the data they generate is relevant to their business strategies.
So why this growing appetite for analytics? This is a subject I recently discussed with Dr. David Simchi-Levi from MIT over lunch. I hypothesized that there were three driving forces that are causing additional momentum for supply chain executives globally to start chasing the myriad of solutions being offered to tackle the "analytics" challenge. Putting such theories forward to an industry leader and veteran such as Dr. Simchi-Levi on this subject was a risky venture! Nevertheless, I proposed these three driving forces include:
- Reliance on the information provided by traditional supply chain execution systems is simply not sufficient for tackling more strategic supply chain considerations in the "new norm" of increasing complexity
- Our globalized supply chains are increasingly vulnerable to internal and external risks, controllable and uncontrollable and with demonstrated devastating stakeholder impacts resulting from significant disruptions
- The retrieval and repository process and costs are becoming more attractive with new tools and the pervasive use of the "cloud"
In his column, The Lean Supply Chain on Industryweek.com, Paul Myerson states:
"The supply chain is a great place to use analytic tools to look for a competitive advantage, because of its complexity and also because of the prominent role supply chain plays in a company's cost structure and profitability. Supply chains can appear simple compared to other parts of a business, even though they are not. If we keep an open mind, we can always do better by digging deeper into data as well as by thinking about a predictive instead of reactive view of the data."
Clearly there is an increasing level of interest in the subject of analytics but that science and technology has some catching up to do. Wow, the gauntlet has been thrown down to the enabling technology vendors. But what's the prize of playing in this game? For the vendors, it's clear. Tapping into a definitive need where today the solutions are deemed immature, outdated, and unsophisticated is an attractive proposition.
But what about for the practitioner? The potential of digging deeper and broader into the supply chain data provides additional hope of efficiencies, cost savings, and increased customer satisfaction—in short, a competitive advantage! And perhaps even a better predictability that will lead to substantial de-risking or our supply chains? Can this investment be the insurance that we are looking for. Invest now, save later? Organizations are beginning to recognize the real need to develop analytic capabilities to turn that supply chain data into actionable insights that can provide that necessary competitive edge to jump ahead of the competition.
The future is bright in my opinion. Today, analytics are often applied relatively well at the "execution layer" of the supply chain impacting short-term decision making which can have a positive effective on ensuring some level of enhanced customer satisfaction. Good yes! Game changing? Not so much!
Currently, analytics are not sufficiently applied at the layer of supply chain design and to an even lesser extent at 'strategic' supply chain decision layer. The challenge is to even up the speed of development and capabilities at the higher, strategic layers of the organization taking advantage of the speed of prolific data now available through the advances being made in cloud technology. It is necessary for leading organizations to predict the future events in time to make corrective actions rather than just being able to drill down into the past in search of root causes for performance failures.
An increase in supply chain maturity requires the operationalizing of this capability. Analytics continues to play a larger role in predictive capabilities. Without having such real-time predictive capabilities we cannot possibly do a satisfactory job of risk mitigation.
In March 2013, MIT and Accenture announced a five-year research collaboration to develop advanced analytics solutions. The alliance's research aims to close the gap between the advance of analytics technologies and their successful application in specific industry and government environments. "Through our collaboration with Accenture we believe we can make important progress in creating new knowledge and in tackling some of the many data challenges faced by organizations today," said Dr. Simchi-Levi. So it seems my lunchtime companion is dedicated to the effort. I am looking forward to what this collaboration will deliver in terms of advances back to the industry. It will be significant!