The Seven Principles of Effective Supply Chain Planning

By Peter A. Bolstorff
APICS Magazine – 2015

Question: Is best practice supply chain planning simply a function of incorporating more leading practices?

Answer: No.

Leading practices in supply chain planning abound. A simple Google search yielded more than 54 million results. Those practices include everything from sales and operations planning to material requirements planning and demand management to vendor-managed inventory. And new principles in supply chain planning are constantly emerging, including demand shaping, demand sensing, and agile response.

Rather than incorporate as many leading practices as possible, the best supply chain planning organizations pick appropriate leading practices as dictated by the markets they serve. Those practices are integrated together with the organization’s chosen technology platform to achieve desired competitive performance.

Sustained results from the best supply chain planning organizations include

  • 15 percent stockkeeping unit mean absolute percent error (MAPE) (for high profile products) and 20 percent weighted MAPE (covering the entire family)
  • 10 percent improvement in manufacturing cost (labor productivity and product yield)
  • 15 percent improvement in inventory turns
  • 25 percent reduction in excess and obsolete inventory
  • 20 percent improvement in delivery reliability
  • 90 percent of new products launched on time
  • 50 percent faster response to significant unplanned demand events
  • 99.5 percent or more volume fill rates.

While not incorporating all leading practices, there are seven basic principles that the best planning organizations adopt:

  1. Systematic management of “master data,” including key data fields for items, customers, manufacturing resources, and suppliers
  2. Synchronized long-term, tactical, and execution planning processes, planning horizons, and intervals for data refresh
  3. Mature collaborative processes for both key customers and suppliers reconciling forecast, orders, and usage or sell through
  4. Data-oriented understanding of the inputs to the forecast including forecast error, cumulative bias, lift, new products, and year-end volume variation
  5. Intense focus on “point-of-sale” or “sell-through” data (as opposed to sales orders and “sell in”)
  6. Disciplined product lifecycle management process bridging the gap between product development and supply chain
  7. A continuous improvement approach to understanding consumer or user behavior.