Delivering for Best-in-Class Wholesaler-Distributors
November 7, 2016  |  ByPaul St. Germain, NAW Institute for Distribution Excellence Author
NAW-Analytics for the Wholesaler-Distributor - Wholesale Distribution Trends #8

The amount of data being produced today is increasing exponentially, and technology is allowing companies to analyze massive amounts of that data to garner new insights about customers and themselves. In many cases, the analysis and insights can be used to generate new forms of differentiation that can be used to disrupt existing markets. For example, Amazon has gained a patent for what it calls “anticipatory shipping,” a method to start delivering packages even before customers click “buy.”

The patent exemplifies the analytical capability for organizations to anticipate customers’ needs, even before customers do, and demonstrates one way Amazon hopes to leverage its vast trove of customer data to disrupt rivals.

Wholesaler-distributors can — and are — implementing the same kind of change and disruption.

Cognitive Analytics for the Wholesaler-Distributor

The next frontier of business analytics and systems is using cognition to improve outcomes. Current systems are static in nature. They perform based on a set of rules that are programmed initially. Although there may be some “intelligence” in the how the system performs under different business conditions, this is static and is the culmination of the knowledge gained over time by programmers. In contrast, cognitive computing infuses artificial intelligence into business systems.

In wholesale distribution, analytics has thus far used structured data gathered from companies’ ERP and business management systems, past order history, CRM systems, or external syndicated data. For years, distributors have been storing and have had access to hordes of structured data. However, 80% of the data in the world is unstructured—images, e-mails, social media posts, and more.

In this digitized world, 2.5 quintillion bytes of data are created every day. We can relate to this as we see the amount of digital data grow in our personal lives in the form of pictures, tweets, Instagram posts, personal e-mail communications, and the list goes on. The same is the case with enterprises, which store vast amounts of data in the form of images, videos, manuals, books, and product descriptions requiring natural language recognition.

This large amount of data, combined with a skills gap when it comes to accessing and analyzing it, is often a barrier to unleashing the power of cognitive analytics. But it is a barrier many businesses and technology services companies are working diligently to break through.

Understand, Reason, and Learn

Cognitive analytics systems have the ability to understand large volumes of data. For example, IBM’s Watson—a technology platform that uses natural language processing and machine learning to glean insights from large amounts of unstructured data—can read 800 million pages per second. This allows industries to speed up their analysis to weeks—a task that would have taken years in the past. A decade’s worth of data, for example, could be analyzed in weeks to derive new associations and trends. This large-volume processing has already started to pay dividends in the healthcare and financial services industries.

For distribution industries that are transaction heavy, it provides a unique way to use historical data over time to provide insights into ordering, sales, returns, and other transaction-related data. While this is impressive, it is still only a first step. Cognitive systems can reason as well. They have the ability to form hypotheses, make considered arguments, and prioritize recommendations to help humans make better decisions. Finally, they learn. They are not programmed; they are trained. Each interaction makes them smarter, leading to better analysis.

In addition to these traits, cognitive systems embed natural language processing (NLP). This allows companies to unleash the power of their business users, rather than relying on programmers to interpret business requirements. NLP is the ability for computers to interpret speech. This is not easy, as human speech is often ambiguous, but the advent of machine learning has helped move this forward in a big way. NLP devices will facilitate a more natural and frictionless way to communicate with technology.

In the brand-new 11th edition of Facing the Forces of Change®: Navigating the Seas of Disruption, you will find much more detail on all of these topics, including strategies and examples from leading distributors, along with suggested actions to understand and minimize the effect of disruption on a business, or present the opportunity to become a disrupter.

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Paul St. Germain, NAW Institute for Distribution Excellence Author

Paul St. Germain, NAW Institute for Distribution Excellence Author

While working for IBM, Paul St. Germain was responsible for managing IBM’s business and strategic initiatives within the global wholesale distribution industry. He researched critical issues and trends, developed IBM’s point of view on industry imperatives; guided IBM’s industry offerings and solutions; and engaged with wholesale distribution executives to help them transform their organizations.
Paul St. Germain, NAW Institute for Distribution Excellence Author

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