You can’t open a major tech news site these days without encountering the words “big data.” Tech journalists have written thousands of gigabytes about this emerging trend in IT, but what is it? More importantly, how can you leverage this fascinating technology into the world of supply chain optimization?
What is Big Data?
The easy question first: what is big data? The answer is both obvious and complicated — big data is comprised of huge data sets that can be analyzed by powerful, logarithm-fueled computers to yield valuable information about a given sector or processes. This includes trends, informational patterns, and non-intuitive links in both the business field and in the social sciences with regards to human behavior.
As explained by a recent IBM report, “Big data is being generated by everything around us at all times. Every digital process and social media exchange produces it. Systems, sensors, and mobile devices transmit it. Big data is arriving from multiple sources at an alarming velocity, volume and variety.” Big data has become even more meaningful with the emergence of the Internet of Things (IoT) — as more machines are connected into cloud computing arrays, more meaningful data can be produced and added to the “mind-meld” of Big Data.
Big data is also transforming how business leaders are getting things done with new insights into what might otherwise be a medley of indecipherable numbers, formulae, and text. Remember the old saying, you can’t see the forest for the trees? With Big data, we can not only see the forest and the trees but can also study and analyze individual leaves, growth patterns, signs of disease, and future sapling predictions.
Furthermore, using big data allows businesses to enhance customer interactions and operational efficiency. In the realm of procurement and supply chain optimization, big data is already lifting the industry to new heights.
How Can Big Data be Used?
The research report “Big Data Analytics in Supply Chain: Hype or Here to Stay? Accenture Global Operations Megatrends Study” notes, “Big data is having an impact on organizations’ reaction time to supply chain issues (41%), increased supply chain efficiency of 10% or greater (36%), and greater integration across the supply chain (36%).”
Writing for Forbes, big data expert Louis Columbus notes that more than 60% of supply chain executives view big data as “a disruptive and important technology, setting the foundation for long-term change management in their organizations.” Big data looks to be an especially powerful game-changer in terms of scheduling logistics, allowing SCO managers to engage end-to-end visibility all along the chain, which, in turn, empowers better decision-making when seconds count.
Supply chain optimization professionals also leverage big data’s real-time capabilities, removing the guesswork from shipping and receiving by allowing managers to observe every step of the process instantly.
In addition to improvements in supply inventory and replenishment techniques, big data gives managers a crystal ball with predictive models and data-driven demands statistics. You will basically know what your customers are thinking and can predict their future behavior, allowing you to “own” the entire supply chain end to end.
Much like the rise of the Internet did in the late 90’s, big data is primed to enact a full-scale transformation across all industries and sectors going forward — especially supply chain optimization.
Not a Magic Wand
However, big data implementation is not a magic wand, dissolving SCO challenges with a “poof.” Building a solid big data strategy into your operation requires research, expertise, and buy-in from your team. Writing for the Hass School of Business, University of Berkeley, supply-chain researcher Nada R. Sanders says, “Despite all of the hype, many companies have not adopted big data analytics, and remain unsure about how to utilize the large amounts of data generated during their operations. Other companies utilize big data in a fragmented way that doesn’t represent a strategic, coordinated effort. These two complications prevent analytics from being used to improve strategy and competitiveness and from benefiting the supply chain.”
Sanders concludes: “Companies that can properly utilize these analytics will continue on in newfound efficiency, customer satisfaction, and competitiveness, and those that don’t will be left behind.”