How can manufacturers ensure that every order is efficient and profitable while saving money or even taking on new business?
Does business intelligence (BI) for manufacturers hold the answers?
"I think most leading thinkers correctly believe a lack of information is what got us into this mess in the first place, so we had better start converting raw meaningless data into meaningful and actionable information so we can get out of this mess sooner rather than later," said Boris Evelson, principal analyst of BI for Cambridge, Mass.-based Forrester Research.
Understanding manufacturing and business intelligence
When it comes to manufacturers, though, there are really two kinds of intelligence -- manufacturing intelligence (a.k.a. enterprise manufacturing intelligence) and business intelligence for manufacturers.
"Business intelligence and manufacturing intelligence are related in the sense that BI is all-encompassing, covering everything from weather forecasts to consumer behavior," said Dan Miklovic, vice president of manufacturing industries advisory services at Stamford, Conn.-based Gartner Inc. "Manufacturing intelligence focuses in on the manufacturing value chain from ideation through after-delivery service, even to retirement and recycling in some cases, but with a real sweet spot of manufacturing itself."
More specifically, manufacturing intelligence (MI) tends to be more about analyzing what's happening on the production floor in real time or near real time.
"What is happening on the conveyor belt? How does it compare to yesterday? Are there errors? Are things getting stuck somewhere? Is everything flowing according to plan?" Evelson illustrated, noting that this kind of operational intelligence is designed to eke out efficiencies in the manufacturing process. If you're talking about measuring and tracking machine state performance, odds are you're looking at manufacturing intelligence.
From shop floor to top floor?
"If you want to look at the higher level of product and process performance, understanding how certain materials work at certain times or understanding how a process will affect the supply chain, that's the crux," said Simon Jacobson, a research director for Boston-based AMR Research. "The point you begin integrating the data with the supply chain, it becomes a broader architecture where you start to involve multiple data sources and multi-site performance to track how well manufacturing is or isn't performing."
The idea, of course, is to connect MI types of data with traditional enterprise data -- after all, companies that manufacture have something to sell, and how can executives make the best decisions with data that shows only a portion of what's going on? More importantly, what if we're talking about companies with multiple manufacturing plants?
"The competitive advantage that we're seeing manufacturers try to achieve is to holistically manage their manufacturing operations across the global network -- the large manufacturers that have 90 to 100 plants," said Matthew Littlefield, a senior research analyst for Boston-based Aberdeen Group.
"If they are able to evaluate and manage those plants with a common set of metrics for each plant, that's really viewed as a competitive advantage for those manufacturers. One of the traditional problems is the silo approach, where each of their facilities has [its] own set of metrics, [its] own set of processes, and manages those processes in [its] own way," Littlefield explained.
"If you have a solution that can normalize across those manufacturing facilities that are generally distributed globally, that's viewed as a competitive advantage that can be achieved by these systems," he added.
For BI implementations, a manufacturer needs to have at least three to five good-sized facilities before it will see much return on investment in manufacturing BI initiatives, he said, though any manufacturer can benefit from highly focused MI solutions that can be implemented at lower price points.
Still, is there room for intelligence in well-running plants?
"More advanced companies are now taking a next look and are saying, 'We have our production line in control, we know what's happening there, we have the system alerting us. So now, what can we do with that data? How can we use the data and use the information to compete and conquer our competitors?' " Evelson explained.
And teasing out competitive advantage is key in today's small, commoditized world. Whether a company is producing a commodity or not, are there some key advantages manufacturers can uncover by using BI strategies?
"At the higher BI level, it ranges from better demand forecasting -- by [better] identifying customer needs, for example -- to improved sourcing through a better understanding of supplier performance," Gartner's Miklovic said, noting that common use cases for BI in manufacturing include R&D productivity/innovation and warranty/quality areas.
In addition, if management has a clear understanding of how difficult or risky it is to produce a given product, they might not sign contracts that appear profitable on the surface but really aren't the smartest moves. For example, if a high-volume, big-contract sale requires changes in supply chain orders and plant floor labor, the profitability of that big-ticket order may plummet. In this scenario, it may be more profitable to take on a lower-volume, lower-ticket order that ironically carries a much higher profit margin -- but you need data and tools to help you measure the pros and cons.
Does business intelligence for manufacturing offer even more opportunity?
All manufacturers, no matter what products come off their assembly lines, share common areas of opportunity. A good place to start is understanding customer needs and buying behavior to reduce costly inventory. Can you change operations in a plant or multiple plants so you can take on even more business?
"If you drive this level of information integration and availability that shows the tradeoffs at the supply chain level with what's going on in manufacturing, you can start shifting capacity -- or make better decisions around capacity -- but you can also really gain [insight] into multiple site performance," Jacobson said. Basically, opportunities that are blurry when looking at a single plant can become clear when you're looking at several.
The million-dollar question: Can BI tools magically uncover gold in hard times?
"No," Littlefield said. "Technology is not the answer; it's just a tool."
If a company tries to apply BI without a strategy, it could end up causing harm, and if the information revealed by BI tools isn't acted on, it's just a big waste of money.
"The key to success is getting the strategy and philosophy right, then applying the right tools to get the data to make the necessary decisions," Littlefield explained. "And finally, you have to use the tools to validate that the corrective actions taken have yielded the results you desired -- and expected."
Chris Maxcer is a freelance writer.