{"id":798,"date":"2018-01-30T14:30:43","date_gmt":"2018-01-30T19:30:43","guid":{"rendered":"http:\/\/lanhamassoc.com\/blog\/?p=798"},"modified":"2018-12-12T16:15:45","modified_gmt":"2018-12-12T21:15:45","slug":"what-do-new-years-resolutions-have-in-common-with-demand-planning","status":"publish","type":"post","link":"https:\/\/www.lanhamassoc.com\/blog\/what-do-new-years-resolutions-have-in-common-with-demand-planning\/","title":{"rendered":"What do New Year’s resolutions have in common with Demand Planning?"},"content":{"rendered":"

\"\"\"\"Whether you see them through or toss them out the window at the first opportunity, New Year’s resolutions are often based on a behavior or situation from the past that you’d like to change in the future. In a similar vein, if you want your demand planning efforts to succeed, the first thing you need to do is be sure you’ve got accurate historical usage data.<\/p>\n

Don’t set yourself up for failure<\/u><\/strong><\/p>\n

Too often demand planning systems fail because little attention is paid to the accuracy of historical usage. After all, if you don’t have good usage data to start with, you won’t have a good forecast.<\/p>\n

By usage data, we don’t mean what’s sold, or the total number of requests, or shipments. In this context, usage is a special history derived from actual sales, but adjusted based on when the customer actually wanted the product. It is a fine distinction, but rather than indicating what has moved through your warehouse, historical usage accurately reflects what the customer wanted (not necessarily what was shipped).<\/p>\n

If you disregard this subtle distinction, you could end up putting a lot of time into assigning formulas to usage data that is not consistent, and you’ll never get the forecast accuracy you desire.<\/p>\n

However, a forecasting system with a good best-fit formula selection process (like our Demand Planning system<\/a>) will perform very well when it works from consistent historical usage data. By putting in place a demand planning system that helps ensure historical data is accurate and consistent, companies can optimize their purchasing in order to effectively manage their largest and most costly asset – inventory.<\/p>\n

In a nutshell, the problem with many forecasts involves the data used to forecast future demand, not the actual forecast formula itself.<\/p>\n

Sales promotions — an example<\/u><\/strong><\/p>\n

Achieving historical accuracy can be very challenging in a number of areas. Sales promotions, for example are typically viewed as a good thing. Many times they are used to reduce an overstocking situation. At other times promotions may be done in cooperation with a vendor who is lowering your purchase price, and you wish to pass this along to your customers.<\/p>\n

So the increase in sales is great, but what does it do for your historical usage? Will your forecast be artificially inflated in the future?<\/p>\n

Here’s what we recommend:<\/p>\n