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1. Departmental “Silos” Create Conflicting Goals
When a company’s director of inventory keeps tabs on safety stock, the head of production maximizes uptime at the plant, and the procurement manager drives down costs with a key supplier, you’d think that company would be in pretty good shape. But when a company monitors supply-chain performance solely by using departmental metrics, it may end up driving locally minded “silo” behavior at the expense of the overall smooth functioning of the chain.
The director of inventory, for example, might pull a day of safety stock out of the system to reduce carrying costs. That might go a long way to help him meet his departmental targets — but it could also jeopardize the company’s ability to respond to an unforeseen spike in customer demand. (Some companies, such as Sun Microsystems, go so far as to align their incentive programs with broader operational metrics.)
Even when metrics are in synch across the company, departments may fail to communicate properly, or fail to exchange critical information altogether. Sales and marketing, for instance, may offer discounts on certain products to drive demand, but without informing the manufacturing department. When manufacturing notes the increase in sales, it might ramp up production in the belief that demand has risen. (Demand has risen, of course, but only temporarily, and not at a price that the company plans to maintain.)
“You’ve got different people, a bunch of complex business processes, each with different objectives and tons of data, explains Mike Mansbach, a senior vice president at SeeCommerce, a provider of supply-chain applications. Without aligned metrics and good communication, “you start to have a lot of dysfunction in the supply chain.”
2. Dirty Data Obscures Your View
Ultimately communication can be no better than the information that’s being communicated. So when databases are poorly connected, or when they’re updated on different schedules, it can be nearly impossible to get a comprehensive view of supply-chain performance.
“If you make a decision before getting a full view from all your systems, it could be turn out to be disastrous, ” says Mansbach. Many companies, he adds, have gone to the opposite — and costly — extreme of “maintaining legacy systems simply because they don’t know whether they are valuable. They’re collecting data at very high costs in maintenance and licensing fees and labor that has little value.” (To combat the problem of “too much data,” some companies have turned to new methods of enforcing data-growth and data-retention policies.)
That dirty data — even seemingly minor inaccuracies like misspellings, typos, and missing information in database fields — can actually subtract value by throwing forecasts and calculations well off the mark. That’s especially true when dirty data is duplicated or fed into further calculations, creating the “bullwhip effect,” in which the amplitude of the error increases as you move further down the supply chain.