The Smart Forecaster
Pursuing best practices in demand planning, forecasting and inventory optimization
What is to blame for having too much of the stuff you don’t need and not enough of the stuff you do need? Demand and supply variability are often blamed. These problems are significant and seems impossible to overcome leaving many organizations to simply accept misallocated stock as a cost of doing business. However, the real problem it isn’t simply late supplier deliveries and unpredictable demand. These are supply chain planning “facts of life” and it’s how your company addresses them that counts. Watch Greg Hartunian’s vlog to hear his thoughts and what you can do about it.
Smart Inventory Planning and Optimization automatically calculates the stocking policy that yields the best return for your business considering holding costs, ordering costs, and stock outs. To see it in action, register below to watch a 12 minute demonstration.
The three types of supply chain analytics are “descriptive”, “predictive”, and “prescriptive.” Each plays a different role in helping you manage your inventory. Modern supply chain software lets you exploit all three helping you to reduce inventory costs, improve on time delivery and service levels, while running a more efficient supply chain.
We just need to feed our demand histories into our new statistical methods, and we can start planning more effectively. Not quite: it’s about the technology and the process. You are investing in a new business process to develop forecasts for driving business strategy and inventory planning decisions.
No, not that kind of regime change: Nothing here about cruise missiles and stealth bombers. And no, we’re not talking about the other kind of regime change that hits closer to home: Shuffling the C-Suite at your company. In this blog, we discuss the relevance of regime change on time series data used for demand planning and forecasting.