Big Data and the Digital Supply Chain

An increasing number of supply chain analysts are stressing the importance of creating a data-driven supply chain (aka a digital supply chain). “The amount of information and improvement possible through big data can be overwhelming,” writes Kevin Jessop, a Marketing Project Manager at Cerasis. “Unfortunately, this may lead some supply chain managers or executives to simply avoid the topic until a more cohesive understanding of its possibilities can be made.”[1] According to Leandro DalleMule, Chief Data Officer at AIG, and Thomas H. Davenport (@tdav), a Professor in Management and Information Technology at Babson College, avoiding the topic of how to best use big data could be a crucial, if not fatal, mistake. “More than ever,” they write, “the ability to manage torrents of data is critical to a company’s success.”[2]

Getting a Handle on Big Data

Jessop reports, “The majority of companies have not defined a big data strategy, and others are barely starting to notice.” DalleMule and Davenport add, “Cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions — and less than 1% of its unstructured data is analyzed or used at all. More than 70% of employees have access to data they should not, and 80% of analysts’ time is spent simply discovering and preparing data. Data breaches are common, rogue data sets propagate in silos, and companies’ data technology often isn’t up to the demands put on it.” Even though some pundits believe big data is old news, the fact of the matter is companies are still wrestling with how best to collect, store, analyze, and use it. In other words, they lack a big data strategy. DalleMule and Davenport insist, “[Companies will never achieve their full potential] in the absence of a coherent strategy for organizing, governing, analyzing, and deploying an organization’s information assets.” They assert a good data strategy must consider both defense and offense. They explain:

“Data defense and offense are differentiated by distinct business objectives and the activities designed to address them. Data defense is about minimizing downside risk. Activities include ensuring compliance with regulations (such as rules governing data privacy and the integrity of financial reports), using analytics to detect and limit fraud, and building systems to prevent theft. Defensive efforts also ensure the integrity of data flowing through a company’s internal systems by identifying, standardizing, and governing authoritative data sources, such as fundamental customer and supplier information or sales data, in a ‘single source of truth.’ Data offense focuses on supporting business objectives such as increasing revenue, profitability, and customer satisfaction. It typically includes activities that generate customer insights (data analysis and modeling, for example) or integrate disparate customer and market data to support managerial decision making through, for instance, interactive dashboards.”

They explain their framework in great detail and I recommend you read the entire article. I want to concentrate on how a good data strategy can foster an effective digital supply chain. Edwin Lopez (@EdwinLopezT37) notes, “Big Data has returned to the spotlight as an end-all solution to supply chain problems, but using data to solve issues has proven far more elusive than collecting it. Or, as the Financial Times put it in 2014, for many ‘Big Data has arrived, but big insights have not’.”[3] He notes that data is not generally the problem. He writes, “Applying Big Data for the supply chain requires a deeper sense of purpose. After all, supply chain managers are already drowning in information to take in and report.” What supply chain professionals are looking for are insights. To glean those insights they need a cognitive computing platform that gather, integrate, and analyze both structured and unstructured data. As Lopez notes, “Big Data is not only the ability to process more information, but the ability to innovate, automate and use data for enhanced decision-making. The toolkit is meant to be applied, not simply possessed.” Cognitive computing is a big part of that kit. A good data strategy complemented by the right cognitive computing platform can put a company on the road to success.

How Digital Supply Chains Use Big Data and Advanced Analytics

Davenport and Zahir Balaporia (@ZBalaporia), a Solutions Partner on the FICO Optimization team, write, “Businesses across many industries spend millions of dollars employing advanced analytics to manage and improve their supply chains. Organizations look to analytics to help with sourcing raw materials more efficiently, improving manufacturing productivity, optimizing inventory, minimizing distribution cost, and other related objectives.”[4] As business leaders know, the speed at which businesses operate today is often measured in minutes or seconds. Manual processes simply can’t operate at those speeds; which is why cognitive computing systems are being employed at the heart of digital supply chains. “If you want to deliver your product to your customer’s doorstep faster than ever before,” writes the staff at CSCMP’s Supply Chain Quarterly, “you don’t need your trucks to travel at supersonic speeds. You just need to make your decisions faster. That’s one reason Noha Tohamy argues in a recent Gartner Research report that it is ‘increasingly unrealistic’ for supply chain organizations at big companies to think they can operate without advanced analytic solutions. Only with these types of solutions will companies be able to examine large sets of structured or unstructured data to acquire deep insights, make predictions, or generate recommendations.”[5] Deborah Abrams Kaplan (@KaplanInk) explains, “Using large data sets for analysis and planning purposes, those in the supply chain can react faster to changes at different points along the chain.”[5] She discusses three ways big data and advanced analytics are being used today to make digital supply chains a reality. They are:

  1. Real-time tracking. “The internet of things (IoT) allows companies to track what’s leaving their shelves in real time, whether at a warehouse or retail store. Add in Big Data coming from social sources (e.g., Facebook, Twitter), news, events and weather, companies can better predict and plan future inventory instead of relying on historical data. … For example, a store running a weekend promotion can track sales on a real-time basis, versus once daily. Taking into account current sales, along with social media responses to the promotion and potential weather events, the company can quickly adjust their supplies and warehouse shipping plans.”
  2. Supplier sourcing. “Maintaining large data sets allows companies to more easily track their suppliers and make changes quickly.”
  3. Customer segmentation. “Using customer data, retailers can segment their buyers and markets to offer them the most customized products and services. … Using Big Data, companies can also adjust the supply chain by market, providing each store with specific items of interest to their buyers. While this isn’t a new concept for retailers, with Big Data, there’s a wealth of information available, which can be parsed more specifically.”

Those are just a few of the many ways data can help improve supply chain performance.

Summary

Richard Weissman, Director of the Organizational Management Program and Center for Leadership at Endicott College Gloucester, told Kaplan, “While Big Data is important, it shouldn’t be the overall decision maker.” He explains, “Supply chain is still a people business. Those people need data to do their jobs, but it doesn’t replace the person doing the jobs. Those analytics are great, tracking is good and the automating of processes through technology is wonderful. But there needs to be someone behind the data. Data won’t replace a relationship or a person walking around a factory floor. The data won’t call your supplier in the middle of the night. Data won’t be restocking the shelves, though it may give you insights on to that.” People, processes, and technologies have always played an important role in business. The big data era won’t change that. What it will change is how people, processes, and technologies interact to make enterprises operate more efficiently and effectively.

Footnotes
[1] Kevin Jessop, “How Do Supply Chain & Transportation Leaders Get Started Using Their Big Data Strategy?” Cerasis, 16 November 2016.
[2] Leandro DalleMule and Thomas H. Davenport, “What’s Your Data Strategy?Harvard Business Review, May-June 2017.
[3] Edwin Lopez, “What is Big Data, and why does it matter to supply chain?Supply Chain Dive, 13 February 2017.
[4] Thomas H. Davenport and Zahir Balaporia, “The Analytics Supply Chain,” DataInformed, 20 February 2017.
[5] Staff, “Running a large supply chain without advanced analytics is ‘increasingly unrealistic,’ says Gartner Research,” CSCMP’s Supply Chain Quarterly, 20 April 2017.
[6] Deborah Abrams Kaplan, “4 trends in how supply chains are using Big Data,” Supply Chain Dive, 13 February 2017.

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