Analyze This

Analyze This

In an age of abundant information, business analytics can help manufacturers turn their data into a competitive advantage.

Keith Henry, vice president, Manufacturing Global Industry Solutions, for Teradata, a Miamisburg, Ohio-based provider of data warehousing and business analytics products and services, recently talked with IndustryWeek about the growing importance of business intelligence and how manufacturers are making the most of the massive amounts of information at their disposal.

Q: When Teradata was founded 30 years ago, a terabyte-size database was considered large. Today, we're starting to see petabyte-size databases. Just how large is a petabyte, and what does that say about the enormity of information available to manufacturers?

A: A petabyte is almost a number too big to imagine, but it's technically 1,024 times a terabyte, which is one quadrillion bytes of information. To put that into PC terminology, if every PC had a 50-gigabyte hard drive, then you'd need 20,000 PCs just to hold 1 petabyte. And we've got customers now that have five to 10 petabytes and growing. So the information explosion is here.

Q: You've said that the "days of true operational business intelligence are here." What do you mean by that?

A: There's some new thought out there that the real competitive advantage going forward is going to be operational business intelligence (BI). By that I mean the ability to take your transactional and other corporate data that you need and be able to analyze that information and do it in near real-time and then to be able to drive the insight that you get from that operational BI down to the levels of decision makers. Tom Davenport's book "Competing on Analytics: The New Science of Winning" pointed out that companies like Wal-Mart, Harrah's Gaming and Discover [Financial Services] have spent a lot of time and money creating [BI] capabilities and they now consider that a competitive advantage.

Q: How are manufacturers putting into operation the business intelligence available to them?

Keith Henry, vice president, Manufacturing Global Industry Solutions, for Teradata

A: At Ford, the business challenge was the sheer magnitude and the speed of their supply chain. Just a few years ago they were managing 250,000 parts, which would break down into 892,000 SKUs, and all of those parts were flowing through their system at different velocities, and everybody was just focusing on today's backorder problems rather than trying to prevent tomorrow's problems. So [Ford and Teradata] created the data warehouse and put the necessary detailed information into it, and an analytics team worked on predicting and projecting the inventory levels at any point in the supply chain by any part number. They created a predictive alert system in order to notify everybody of critical stockout situations before they occur. Then, operationally, they would recommend priorities down to the line workers so they were able to reduce their inventories, reduce stockouts and improve the overall efficiency of their supply chain.

Another good example is the large grocery store chains, which have become very efficient at maintaining and analyzing the point-of-sale data rolling off their scanners. Increasingly it's daily, but some of the big retailers are now taking that data on a 15-minute interval. And they are literally making real-time decisions and building analytic models looking for patterns of behavior, A in the supply chain and B in the demand chain, or in the consumer behavior.

On the other side of that coin, you have manufacturers also interested in that point-of-sale data. Goodyear today is sweeping in point-of-sale from all of its retail outlets, and it's now using that to improve its supply forecasting. [Goodyear] has reduced its out-of-stock significantly, and it has streamlined its inventory positions, basically by using that same point-of-sale data just as the retailers do it.

Q: What is "predictive maintenance analytics" and how can this technology help manufacturers?

A: Without predictive maintenance, basically what you're looking at is "break-fix." Obviously that's hugely expensive, particularly for businesses like oil rigs, where being offline costs hundreds of thousands of dollars an hour. By having predictive asset maintenance, you can create a system where you maintain the rich detail around all your parts and then what their recommended maintenance schedules are, where parts are located, what is the best shipment route, the history of the part, etc. You also can identify equipment that's likely to fail, and then you can prioritize the problems based on their business impact -- some parts can fail and you can keep going, other parts fail and you're out of business. You can determine the root causes of failure and work to fix them. You can create automated reporting and then alerts that notify the maintenance people to schedule maintenance, and make recommendations to them that "while you're doing maintenance on one part, it's going to be very cost-effective to go ahead and do maintenance on these other three parts."

Q: Considering the accelerated pace at which technology is advancing, how do you envision manufacturers using business analytics 10 years from now?

A: Certainly we see it increasing. I think two factors will contribute to that: First of all I think it's going to be more real-time. Increasingly, equipment will be able to provide significant amounts of measurements, and networks are becoming pervasive, so the ability to collect near real-time information and do something with it is going to increase. And then the other big thing that may occur is it's going to be more collaborative. So within companies I think we'll see customers and suppliers sharing their analytics and making joint decisions, and that'll just increase the business payback significantly.

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