Yet manufacturing more SKUs also means more complexity in the business system at a time when these companies are already under pressure to become more efficient and cut costs. McKinsey estimates that complexity among food and beverage manufacturers alone is costing the segment as much as $50 billion in gross profit.
McKinsey said traditional approaches to simplification, such as cutting low-volume SKUs, address only one aspect of the business system and can produce unintended consequences. McKinsey said companies need a better approach to managing complexity, one that factors in commercial and operational perspectives and uses big data to set action plans that the entire organization can agree upon. The key isn’t to avoid complexity, but to learn how to manage and use it.
“In other words, good complexity more than pays for itself. Bad complexity, on the other hand, erodes profit, increases inventory, and makes the supply chain less agile,” said McKinsey.
Dave Padmos, Global Technology Industry Leader of Advisory Services at Ernst & Young, said that “winners” in the big data age will be those manufacturers that can master “out-of-control” data proliferation and analyze data for important business decisions. Padmos said companies will need an enterprise data strategy, must be able to align and integrate the data, and should find a way to standardize it for analysis.
A recent Kronos report said surviving the increasing complexity means manufacturers need to be able to manage the new world of analytics, adapt to data-driven manufacturing and turn the “plant of the future” into a competitive advantage. These complexities not only include data, but also labor and product complexities, as well. Nearly half of all manufacturing companies said they were under pressure to launch products quickly, and produce lower-cost products and more customized & complex products. “All of these complexities lead to rapid changes in how companies manage their business processes, human capital and information,” Kronos said. Kronos added that “complexity becomes controllable when you manage the data.”
Therefore, organizations must learn to rapidly turn this data into actionable information that can identify efficiencies and drive improvements. “Good products and operational efficiencies are still incredibly important, but a critical differentiator also will be taking advantage of the data that a manufacturer already has inside their company,” said Kronos.