Great Suppliers Make Great Supply Chains

As an analyst who covers supply chain management (SCM) and procurement practice across industry, I tend to keep my keyboard focused on the disruptive themes that continue to re-define it. That said, if you’re expecting me go on about the unprecedented growth of the SCM solution markets, the accelerated pace of innovation, tech adoption, social change, etc., don’t hold your breath. I can’t, as the data argue otherwise. Too many of us conflate diversification with acceleration –and there’s a difference.

The most notable, defining advances of the last decade (Amazon, Twitter, Google, etc.) share something in common: they do not require consumer investment. If you take those monsters out of the equation and focus on corporate solution environments, the progress, while steady, has not been remarkable. Let’s just say there remains plenty of room for improvement, especially in supply chain and procurement practice areas.

I fell onto this tangent unexpectedly. It happened while interviewing Mr. Dan Georgescu, Ford Motor Company, adjunct Professor of Operations and Supply Chain Management, a highly regarded expert in the field of automotive industry supplier development. “For supply chains to be successful, performance measurement must become a continuous improvement process integrated throughout,” he said. “For a number of reasons, including the fact that our industry is increasingly less vertically integrated, supplier development is absolutely core to OEM performance.”

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A Tale of Two Disciplines: Data Scientist and Business Analyst

data scientist and BA

The ability to use data to achieve enterprise goals requires advanced skills that many organizations don’t yet have. But they are looking to add them – and fast. The question is, what type of big data expert is needed? Does an organization need a data scientist or does it need a business analyst? Maybe it even needs both. These two titles are often used interchangeably, and confusion abounds.

Business analysts typically have educational backgrounds in business and humanities. They find and extract valuable information from a variety of sources to evaluate past, present, and future business performance – and then determine which analytical models and approaches will help explain solutions to the end users who need them.

With educational backgrounds in computer science, mathematics, and technology, data scientists are digital builders. They use statistical programming to actually construct the framework for gathering and using the data by creating and implementing algorithms to do it. Such algorithms help businesses with decision making, data management, and the creation of data visualizations to help explain the data that they gather.

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The Key to Analytics: Ask the Right Questions

People think analytics is about getting the right answers. In truth, it’s about asking the right questions.

Analysts can find the answer to just about any question. So, the difference between a good analyst and a mediocre one is the questions they choose to ask. The best questions test long-held assumptions about what makes the business tick. The answers to these questions drive concrete changes to processes, resulting in lower costs, higher revenue, or better customer service.

Often, the obvious metrics don’t correlate with sought-after results, so it’s a waste of time focusing on them, says Ken Rudin, general manager of analytics at Zynga and a keynote speaker at TDWI’s upcoming BI Executive Summit in San Diego on August 16-18.

Challenge Assumptions

For instance, many companies evaluate the effectiveness of their Web sites by calculating the number of page hits. Although a standard Web metric, total page hits often doesn’t correlate with higher profits, revenues, registrations, or other business objectives. So, it’s important to dig deeper, to challenge assumptions rather than take them at face value. For example, a better Web metric might be the number of hits that come from referral sites (versus search engines) or time spent on the Web site or time spent on specific pages.

TDWI Example. Here’s another example closer to home. TDWI always mails conference brochures 12 weeks before an event. Why? No one really knows; that’s how it’s always been done. Ideally, we should conduct periodic experiments. Before one event, we should send a small set of brochures 11 weeks beforehand and another small set 13 weeks prior. And while we’re at it, we should test the impact of direct mail versus electronic delivery on response rates.

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