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Transforming Your Supply Chain Using Big Data Analytics

November 20, 2014


“It’s no secret that, in today’s global business environment,” writes Joseph Roussel, a France-based PricewaterhouseCoopers partner, “superior supply chain performance is essential to competitive advantage.” [“Making the Supply Chain Everyone’s Business,” IndustryWeek, 9 May 2014] Roussel’s assertion is based on hard facts not personal opinion. “According to research by PwC’s Performance Measurement Group (PMG),” he explains, “there’s a strong correlation between superior supply chain and superior financial performance. Specifically, companies with top-flight supply chains can realize 50% higher annual sales growth and 20% higher profitability than their competitors. That makes sense, given the supply chain’s role in driving breakthrough innovation, customer satisfaction and operational efficiencies.” The point that Roussel wants to make, however, is not just that supply chain performance is important for a business but that supply chains are now so complex that no single executive is capable of managing them. He writes:

“A supply chain consists of a vast range of activities and interactions that touch virtually all of a company’s functions, as well as those of suppliers and customers around the world. No executive — however skilled, innovative, or persuasive — can drive top supply chain performance singlehandedly. Because the supply chain is critical for revenue growth, it should be the concern of the entire senior management team, not just the COO or CSCO. This includes the head of sales and marketing, who relies upon the supply chain to deliver products in a way that is consistent with the overall customer value proposition; the head of strategy, who depends upon it for expansion into new markets; and the head of product development, who relies on the supply chain to achieve time-to-market and time-to-volume goals.”

There is a single thread that ties all of these functions and individuals together — big data analytics. A lot has been written, including by me, about the challenges companies face when the data they rely upon is siloed inside traditional business divisions. If corporate executives aren’t using the same data to understand what is happening, corporate alignment is impossible to achieve. A good big data analytics system will provide all corporate executives with a single version of the truth around which strategies and plans can be made and implemented. Although that sounds easy, it’s not. Richard Sharpe, CEO of Competitive Insights, told the SupplyChainBrain staff, “Big data is all about taking information from a variety of different sources, both structured and unstructured, and using that information to make fact-based, meaningful decisions.” [“Creating Supply Chain Value with Big Data Analytics,” SupplyChainBrain, 5 August 2013] The article continues:

“To get to that goal, companies must clear a number of hurdles, however. The first ‘daunting task’ is to figure out how to effectively collect, analyze and distribute data from many different sources so that it makes sense and is actionable, he says. Once that is accomplished, the next barrier is to get cross-functional agreement that the data fairly represents each department’s operations. The important thing to keep in mind about big data is that it includes information not only from large, structured data bases that companies know well but also data from new sources, primarily mobile devices, Sharpe says. ‘There has been an exponential growth in the amount of data from these new sources, and it is being captured in a variety of ways,’ he says. ‘The challenge is to combine that unstructured information with the structured information and provide additional insights that enable a company to make better decisions.’ For example, he says, if a company is able to understand which customers or delivery locations are profitable and which are not, it can develop very different strategies for serving these customers, he says. ‘The same is true for products.'”

Ray Major (@majorbi), Chief Strategist of Halo Business Intelligence, points out that, when it comes to supply chain data, there is a continuum that goes from single source data to big data (see the graphic below). Major argues that the closer a company comes to leveraging big data the more business intelligence value it will gain. [“Understanding the Supply Chain Data Continuum,” Halo Business Intelligence, September 2014]



Dan Woods (@danwoodscito), Chief Technology Officer and editor of CITO Research, offers a unique perspective about companies should pursue big data analytics to achieve the goals mentioned above. He calls it the “distributed data supply chain.” [“Why Building A Distributed Data Supply Chain Is More Important Than Big Data,” Forbes, 27 June 2013] Don’t be confused by the headline of his article. The data he is discussing is big, whether it’s distributed or not. He explains:

“It is time to stop the stampede to create capacity to analyze big data and instead pursue a more balanced approach that focuses on finding more data sets and understanding how to use them to improve your business. The goal should not be to create one big factory that can handle any data set, no matter how big. Instead, we should be seeking to create an extended supply chain that accepts data from a wide variety of sources, both internal and external, processes that data in various nodes of the supply chain, passing data where it is needed, transforming it as it flows, storing key signals and events in central repositories, triggering action immediately when possible, and adding data to a queue for deeper analysis. The era of the massive data warehouse is coming to an end. The era of a distributed data supply chain is just beginning.”

All of the experts cited above would probably agree with that assessment. The whole point of collecting and analyzing data is to gain actionable insights. To do that, you need to obtain the right data and use the right techniques to analyze it. In Accenture’s latest technology vision entitled “From Digitally Disrupted to Digital Disrupter,” Woods’ point is reinforced. The Accenture study discusses what it calls the Data Supply Chain, which involves “putting information into circulation.” Analysts know that data and information are not the same things. Information is gleaned from data and that gleaning process is not as straight forward as many people believe. The Accenture study notes, “Data ecosystems are complex and littered with data silos, limiting the value that organizations can get out of their own data by making it difficult to access. To truly unlock that value, companies must start treating data more as a supply chain, enabling it to flow easily and usefully through the entire organization — and eventually throughout each company’s ecosystem of partners too.” That sounds a lot like Woods’ distributed data supply chain.


To deal with all of the complexity involved, the Accenture study asserts that the next step is cognitive computing. “As the volume and variety of data grow,” the study reports, “so too do the scale and complexity of the data supply chain, making it increasingly difficult to add to and get value from data as it is manipulated.” It continues:

“What if … machines could be taught to leverage data, learn from it, and, with a little guidance, figure out what to do with it? That’s the power of machine learning — which is a major building block of the ultimate long-term solution: cognitive computing. Rather than being programmed for specific tasks, machine learning systems gain knowledge from data as ‘experience’ and then generalize what they’ve learned in upcoming situations. Cognitive computing technology builds on that by incorporating components of artificial intelligence to convey insights in seamless, natural ways to help humans or machines accomplish what they could not on their own. At its most advanced, cognitive computing will be the truly intelligent data supply chain — one that masks complexity by harnessing the power of data to help business users ask and answer strategic questions in a data-driven way.”

As President and CEO of a cognitive computing company, Enterra Solutions®, I obviously see things the same way. Roussel concludes:

“Industry leaders know that the supply chain, deployed as a strategic asset, can drive better top- and bottom-line performance. They understand that all relevant functions have to be adept at using the supply chain to achieve their strategic priorities, whether entering new markets, launching new offerings or creating greater efficiencies. … In today’s fast-changing global markets, creating value for the customer — and differentiation from the competition — demands an integrated approach to supply chain operations. From the CEO down, the supply chain is everyone’s business because, ultimately, the customer is everyone’s business.”

The only way that you can make the supply chain “everyone’s business” is by providing them with the tools they need to understand, collaborate, and take action. Big data analytics is one of those tools.

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