Estimated reading time: 2 minutes, 58 seconds

Big-Data1As a supply chain professional, you’re probably scratching your head about the whole Big Data thing, that is unless you’re an early adopter and have already reaped its benefits or suffered through start-up issues. I bet you’re questioning how and when you should stick your toe into the water.

 

In a previous article (read it HERE), I referenced ideas GXS’s Mark Morley expressed about leveraging Big Data for visibility, auto-replenishment, and preventive maintenance, but those are high level objectives that could require lots of planning and investment. What you should be asking at this point is whether Big Data is even needed to reach your goals.

 

With its massive datasets and the need to utilize special tools for analytic purposes, Big Data may well be overkill for much or all of your needs. But one real outcome of all the Big Data hype is the increasing expectation that solid information is the foundation of the best business decisions. The fact that Big Data is being so heavily discussed is in itself focusing attention on what’s required to turn data into usable information. You should think about your business needs and how to identify and obtain the data that can provide them.

One area I’d recommend looking at to get a feel for the use of data in your supply chain would be your scorecarding process. As you know, the scorecard is a common tool used in supply chains of all sizes in an attempt to assess the quality of vendors. It usually measures indicators like on-time delivery, timeliness of delivery of electronic documents, error rates, and a plethora of other things. I’ve seen simple scorecards with 5 measurements and others that are long and (sometimes overly) complicated. What I don’t recall seeing, though, is a scorecard that all parties agree is a totally accurate representation of quality. Is there any way to improve it so that it’s both more comprehensive and more accurate?

Whether you’re on the vendor or customer side of the process, thoroughly analyze what’s being measured. What’s your comfort level with each metric? Which ones are most important to you? To your partner? Which are tied directly to company/departmental goals or KPI’s? Are there any missing categories that should be there but aren’t due to inaccessibility of data?

The decision to invest in the people, processes, and technology to harness the power of Big Data in your organization will be determined by the scale of the problems you need to address, the volume, velocity, and variety of the data involved, and the ROI you expect. Start small and scale up from there. One of the valuable things I’ve learned in my readings about Big Data is that it enables you to play out scenarios or examine ‘what ifs’ unencumbered by technological limitations. If you had access to every bit of internal and external data you could imagine, what questions about your supply chain could you answer? Looking at your scorecard is a way to start small and to begin thinking about improvements or gaps.

It’s obvious that Big Data isn’t for everyone. But, you know what? You probably don’t need it to make improvements that will have an impact in your operation. If you can truly identify projects you feel Big Data can address, the tools and processes are maturing quickly so you should go for it. However, you should feel quite comfortable in dealing with ‘little data’ as well.

 

Last modified on Monday, 05 May 2014
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