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Ensuring dependable manufacturing data for BI analytics

Effective business intelligence systems need a steady diet of clean, up-to-date, and reliable information.

Knowledge is indeed power for manufacturers. With the right business intelligence (BI) analytics tools, companies can optimize their production lines based on actual demand and identify the suppliers that are the most -- and least -- profitable partners.

But what if the underlying data is dirty? Launching a bold business initiative based on information that’s inaccurate, outdated or incomplete is like using a broken GPS in your car: it’s always “recalculating,” and you never get where you want to go.

The consequences of poor data can be worse than inconvenient -- they can put companies at a competitive disadvantage.

“We’re past the point where companies compete only on low prices and efficient supply chains,” said William McKnight, president of the McKnight Consulting Group based in Plano, Texas. “Analytics is the new battleground where companies are competing today, and if they are not doing a good job, it’s reflected in the bottom line.”

Fortunately, mature tools and best practices exist to help manufacturers ensure their data is reliable and complete enough to grow their businesses and reduce costs.

New opportunities in BI for manufacturing

BI systems are a hot topic because they hold the promise of helping executives spot new opportunities ahead of their competitors and address production problems before they get out of hand. New analytics software and data warehouse options enable companies to get BI up and running quickly and avoid some of the implementation hurdles that once gave analytics a reputation for poor return on investment.

Vendors of enterprise applications are joining in with so-called “real-time BI”: analytics built into ERP, for example, that gives business managers dashboards showing key performance indicators.

But there’s a dangerous flip side. Making analytics more accessible may just mean it’s easier for planners to make faulty decisions. To avoid this trap, organizations must invest up-front work in making the underlying data reliable. That’s not easy when critical business information constantly changes throughout the day and resides in separate applications for financials, sales forecasting, production, customer relationship management and inventory management.

Not only are each of these components gathering information separately, the programs and the people that use them may not always see data the same way. That’s why some organizations can’t answer one of the most fundamental questions of all: “How many customers do we have?” Without formal data management discipline, one system may identify a client by name while another uses a customer ID number.

Life gets even more confusing when different departments have unique definitions of what constitutes a customer. In accounts receivable, a customer may be any organization that receives a bill, while in sales it might be limited to paying clients and prospects. No wonder "single source of truth" is the go-to phrase in the marketing materials of data management technology vendors.

BI data management challenges for manufacturers

But manufacturers that invest sweat equity in up-front data management for their ERP environments can see payoffs in more profitable supplier relationships and better-run back offices.

For example, if branch locations separately buy components from the same supplier, the manufacturer might miss out on volume discounts. A disciplined approach to BI data management will identify this lack of coordination and allow the company to negotiate group-purchasing agreements. Similarly, data discipline will help the accounts receivable department find incorrect or outdated contact information that slows down payments.

Transitions Optical Inc, an optical products manufacturer based in Pinellas Park, Fla., found benefits like these when it layered business analytics on top of reliable data about its international finances. For years, pieces of the financial puzzle had been reported by five plants and nine global centers that relied on three regional ERP systems. This made it difficult to consolidate financial information and provide executives with an accurate data foundation for planning and forecasting. With better data management, the company streamlined its financial reporting activities; that shortened its budget cycle and now allows it to perform enterprise-wide planning.

Accurate data can underpin a number of other areas for manufacturers, including:

Customer data integration (CDI): A combination of technologies and practices that consolidate customer information held across an enterprise into a single consolidated view. At its most basic level, CDI can help manufacturers understand the size of their customer base and know which clients are the most important.

Product Information Management (PIM): Another blend of technologies and practices, this time working to consolidate and better manage inventory data. Among the targets for PIM data are ERP systems and planning solutions used by partners in the supply chain.

Demand signal repositories (DSRs): First employed by consumer products companies, DSRs are now used by manufacturers in other sectors to centralize sprawling data sets from a variety of areas, such as inventory management applications, point-of-sale systems and customer-loyalty programs. Potential benefits include savings from increased inventory turns and reduced inventory inefficiencies.

Disciplined data management requires some up-front technology investment and coordination between business and IT staffs. But it doesn’t have to be an exercise in delayed gratification. Done right, the paybacks can come in months rather than years.

“If you are strategic about it, [business intelligence systems] can pay for themselves in six months or less,” McKnight said.

Next Steps

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