Are you a hero?
The executive suites at most companies are populated by leaders who became corporate “heroes.” These exceptional performers led—and continue to lead—transformative initiatives that drive revenue growth, reduce costs and increase shareholder value.
Most statistical forecasting works in one direct flow from past data to forecast. Forecasting with leading indicators works a different way. A leading indicator is a second variable that may influence the one being forecasted. Applying testable human knowledge about the predictive power in the relationship between these different sets of data will sometimes provide superior accuracy.
The unique challenges of inventory planning for spare parts, large capital goods and other infrequently or irregularly moving items drives the importance of finding smarter methods to forecast this kind of intermittent demand. Robert Bowman, Editor of Supply Chain Brain Magazine, and I discussed this topic at the October APICS conference in Denver, and video of our conversation is available at Supply Chain Brain‘s website.
Posted in Intermittent Demand
Tagged APICS, forecasting, intermittent demand, interview, irregular demand, large capital goods, service parts, spare parts, stocking, video, zero demand
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?
The destructive impact of Hurricane Sandy has been both staggering and instructive. Our thoughts and best wishes for rapid recovery go out to all who have suffered personal or economic loss or damage. Now, in Sandy’s aftermath, we find ourselves thinking about accelerating recovery and planning for the next unforeseen event.
Our work with clients in the heavily hit mass transit sector presented a sobering view of damaged infrastructure, heavy equipment, and losses of essential inventory. Those most affected have seen a crush of work as inventory managers take stock of what they have, what they need and procure a mountain of replacement parts and products. This uniquely massive replenishment cycle presents all sorts of opportunities and considerations. For those who are still in this phase, and to help our collective preparation for the Next Big Event, here are a few thoughts:
Morgan Drawbridge, South Amboy, NJ, following Superstorm Sandy
Photo courtesy njtransit.com
Posted in Business Policy, Intermittent Demand
Tagged damaged infrastructure, federal relief, forecasting, hurricane, insurance, intermittent demand, inventory, recovery, safety stock, sandy, service level, superstorm
Fluctuations in an inventory supply chain are inevitable. Randomness, which can be a source of confusion and frustration, guarantees it. A ship carrying goods from China may be delayed by a storm at sea. A sudden upswing in demand one day can wipe out inventory in a single day, leaving you unable to meet the next day’s demand. Randomness creates frictions that make it hard to do your job.
At first blush, it sometimes seems best to respond to randomness with the ostrich approach: head buried in the sand. You can settle on a prediction and proceed on the assumption that the prediction will always be spot on. The flaw in that approach is that it ignores statistical methods that allow us to make use of a wealth of knowledge about our knowledge itself—how confident we can be in our predictions, and what breadth of possibilities confront us. The efficient approach to tackling the problems that stem from randomness is not to ignore uncertainty, but to embrace it with eyes open.
Posted in Excellence in Forecasting
Tagged average, contingencies, forecasting, inventory, overstocking, randomness, raw materials, reliability, staffing, stocking, supply chain, uncertainty, understocking
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