The Dow Chemical Company transforms its business with analytics

The Dow Chemical Company succeeded for years selling commodity chemicals, but when the company decided to transform the business by transitioning toward solution-based products, investing in green technology and growing globally, it needed a more analytical approach. SAS® Analytics helps the company choose the right products and markets, forecast sales, and reduce costs. Dow says projects supported by SAS have helped increase its ROI substantially.

Customer Success Video

Check out this video to learn more about DOW Chemical Company and its successes with SAS.

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Customer Viewpoint
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Dave Kepler
Executive Vice President, Business Services

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Tim Rey
Director, Advanced Analytics

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Ed Noack
Global Leader, Diamond Value Chain Consulting

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Darrell Zavitz
President, Supply Chain and Customer Services

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Jan Behnke
Global Business Director, Amines and Kaolins

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Eddie Kennedy
Director, Corporate Marketing Sites

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The Midland, MI, company has always been data driven. But, as it sought to do more than supply the raw materials to companies that created finished goods – along with expanding its global footprint – it needed a sophisticated approach to studying potential markets, analyzing costs and creating forecasts. Rather than approach the issue piecemeal, Dow created a Business Services unit that provides advanced analytics assistance for the entire organization.

"What SAS has been able to do for us is take the statistical methods we know can solve a business problem, help with the math for decision support and help us surface the results in a technology that enables us to do that effectively," explains Tim Rey, Director of Advanced Analytics at Dow.

Choosing SAS®
When Dow first decided to put together an advanced analytics team nine years ago, Rey and Dave Kepler, Dow's Chief Information Officer, looked at multiple products, concluding that SAS was the best alternative for Dow – particularly in terms of forecasting and providing a full range of analytic offerings. "It became evident that we needed to stick with the SAS environment to bring consistency to the processes and methods we use," Rey says.

Finding analytic success
For all projects to which Dow has applied analytics, the combined results have produced a substantial return on investment. A sample of the successes:

• Sales forecasts are generally accurate to within 10 percent, versus an error rate that was, in some cases, as high as 40 percent in previous years.
• Business unit leaders typically know if they'll make their targets by Day 12 of every month, allowing them to adjust strategy accordingly.
• Regional exchange rate risk models help Dow make sound decisions about where to buy raw materials and how to price finished goods.
• Distribution optimization projects assist Dow in moving products from 179 manufacturing facilities to hundreds of thousands of destination points at the right time without excessive inventory holding costs.
• A human resources supply/demand model helps the company hire just the right talent at the right time.

"We can go back and show significant savings," says Kepler, a leading proponent of analytics. "And it helps with margin expansion. It's all about how we can be in the market with the right product at the right time and get focused on that, and then go back to measure our success, model future success and predict what we need to do next."

Modeling, working with customers and building better supply chains
Modeling is particularly critical, and Dow works not only with its own units but with customers. "It's relatively cheap to build a model, compared to building plants and hiring people to staff them," explains Ed Noack, Global Leader for the Diamond Value Chain Consulting Group at Dow. "Now is simulation 100 percent accurate? No, but it is better than going on a hunch. Using analytics points us in the right direction so we can narrow it down and make better decisions, faster."

Noack often works with Dow customers internally and externally on supply chain issues. "We help people design and modify their existing or new supply chains. We help people grow their businesses by helping them understand what it means to start selling new products in China or Brazil."

Eddie Kennedy, Director of Corporate Marketing Sites, does similar work with internal customers. "We try to better align sales to production and inventory management between us so we're actually helping the customers with their revenue and their cost plans as well. Now it's still their choice [whether to buy from Dow] but they usually appreciate your insights."

Dow has also been able to use SAS to maintain control of energy costs. In its most recent annual report, Dow notes that 41 percent of its production costs are related to expenditures for energy and hydrocarbon feedstocks. As anyone who has tracked energy prices in recent years knows, this presents some significant challenges. Dow's analytics efforts have gone into reducing the use of energy and hydrocarbons, finding alternatives, and figuring out more efficient ways to ship its products – or produce them closer to the end consumers.

"In order to maintain our margins, you have to keep freight and logistics costs under control; you have to keep your raw material costs under control," explains Rey, who used SAS, for instance, to figure out how to get the best rates for shipping via rail, along with modeling oil and gas costs.

Keeping the global financial crisis from hurting Dow
For Dow, the real value of analytics became apparent as the economy started to collapse in late 2007. Information was being pushed out daily to business units that could then use it to make adjustments that kept the company's fiscal performance strong. "You know [business] cycles are going to happen," says Rey. "It's a matter of predicting when they're going to happen, and then making the right decisions so that you're either not losing as much money going down in the cycle or you're making more money coming out of it. The quality and timeliness of the models [created with SAS] help us do that."

"The only difference and competitive advantage that companies have going forward is if they make better decisions than other companies," says Kepler. "And with that, then how you collect data and how you make the decisions off that data are really what's going to differentiate you from your competition."

The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.

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