Respect for Data Key to Inventory Planning

In my role at Direct Tech, interacting with both prospective and existing customers, I'm often asked to provide a list of inventory planning best practices. The one I always lead with — respect for data — invariably brings raised eyebrows and blank stares.

Respect for data … what does that mean? And, more to the point, what does it have to do with inventory planning?

Really, it's just another way of saying "garbage in, garbage out." Let me explain.

On the surface, "respect for data" does appear to be more of a marketing or database management concept. These areas mine vast amounts of data to provide analytical business recommendations. Their very existence hinges on having good data.

But when you think about it, isn't that also true for inventory planners? Let's break apart some of the key areas of inventory planning that absolutely depend on timely and accurate data:

• Product demand forecasting, the first step in inventory planning, is entirely data dependent. Incomplete or inaccurate capture or posting of product sales information will directly lead to inaccurate forecasts.
• Supplier reorder decisions depend on timely and accurate inventory levels — both on-hand inventory and quantity and expected receipt date of open purchase orders.
• Gross margin, particularly the portion related to markdowns from overstocks, is dependent on accurate item retail pricing and item costs.
• Staff efficiency is directly tied to accurate data. If every forecasting or purchasing decision is delayed because the planner is constantly having to double-check data details for accuracy, their time is wasted — and the timing of purchase order deliveries is also compromised due to the inefficient decision making.

The list goes on. Until you have data audit processes in place to assure you have reliable information from which to plan, you can't trust the result. Building assortment plans, creating demand forecasts or making inventory decisions without first validating the accuracy of planning data is like building a house on a faulty foundation. You might get away with it for a short time, but one day it will come crashing down.