In our travels around the industrial scene, we notice that many companies pay more attention to inventory Turns than they should. We would like to deflect some of this attention to more consequential performance metrics.
Smart Software President Gregory Hartunian
Do you know which items have too much or too little inventory? What if you knew? How would you go about cutting overstocks while still ensuring a competitive service level? Would you be able to reduce stockouts without incurring a prohibitively expensive inventory increase? How would these changes impact service levels, costs and turns—for individual items, groups of items and overall?
Smart Software President Nelson Hartunian, PhD
Tremendous cost-saving efficiencies can result from optimizing inventory stocking levels using the best predictions of future demand. Familiarity with forecasting basics is an important part of being effective with the software tools designed to exploit this efficiency. This concise introduction (the first in a short series of blog posts) offers the busy professional a primer in the basic ideas you need to bring to bear on forecasting. How do you evaluate your forecasting efforts, and how reliable are the results?
Dr. Greg Parlier (Colonel, U.S. Army, Retired)
Contributed to The Smart Forecaster by Dr. Greg Parlier (Colonel, U.S. Army, retired). Details on Dr. Parlier’s background conclude the post.
For over two decades, the General Accounting Office (GAO) has indicated that the Defense Department’s logistics management has been ineffective and wasteful, and that the Services lack strategic plans to improve overall inventory management and supply chain performance.
Posted in Business Policy, Guest Posts
Tagged analytics, armed services, decision support, efficiency, ERP, force readiness, innovation, inventory levels, logistics, management information, organizational design, performance analysis, supply chain, US Army
In order to reap the efficiency benefits of forecasting, you need the most accurate forecasts—forecasts built on the most appropriate historical data. Most discussions of this issue tend to focus on the merits of using demand vs. shipment history—and I’ll comment on this later. But first, let’s talk about the use of net vs. gross data.
Net vs. Gross History
Many planners are inclined to use net sales data to create their forecasts. Systems that track sales capture transactions as they occur and aggregate results into weekly or monthly periodic totals. In some cases, sales records account for returned purchases as negative sales and compute a net total. These net figures, which often mask real sales patterns, are fed into the forecasting system. The historical data used actually presents a false sense of what the customer wanted, and when they wanted it. This will carry forward into the forecast, with less than optimal results.
Posted in Excellence in Forecasting
Tagged accuracy, demand, demand data, efficiency, forecasting, gross history, net history, returns, sales pattern, shipment data, stock-out