Why Better Data Is the Key to Demand Planning and Inventory Forecasts

How many units of your product will you sell in the next three months? How about next month? Without a formal demand planning process in place, predicting how well your products will sell is like throwing darts, with the wrong prescription in your glasses. If you've been in the game long enough, you can probably wing it and hit the board. But you'd be lucky to score a bullseye.

In the apparel sector, it seems you must hit the bullseye more often than not to stay competitive. Products cycle faster every year, customers shop online more and more but continue to rely on physical retail to feel and size up the products.

Thus there is pressure to match stock to anticipated sales as accurately as possible. Otherwise, a company risks carrying too much of one thing, eating up cash flow in excess inventory, with stock ultimately sold at markdown to make way for the next product.

But how close to the bullseye can apparel companies really get, even with a demand planning process in place?

Data in the age of supply networks
The technology exists to integrate production, warehousing, shipping and other processes, making it easier for companies to move away from the discrete, segregated, time-consuming steps of a supply chain and toward a supply network that is more responsive and adaptable to near-term trends.

Fortunately, technology has also accommodated a readier flow of information among those different points of a supply network. And demand planning requires data as the fundamental starting point to make better inventory forecasts.

Demand planning is the multi-step operational supply chain management process used to create more reliable forecasts. Effective demand planning can guide companies to improve the accuracy of revenue forecasts, align inventory levels with peaks and troughs in demand, and enhance profitability for a given channel or product.

Supply chain experts will say that companies are mistaken to shun demand planning. Even in industries such as apparel, where trends and tastes shift faster than static supply chains can anticipate, a supply chain expert will argue that demand planning is still of the utmost importance, for two reasons.

The first is that without demand planning, aspects of an organization will work toward goals according to their own priorities. Demand planning instead requires that the parts of an organization responsible for design, procurement, production, logistics, and sales and marketing "buy in” to a business plan, working toward one set of revenue and inventory targets. In other words, demand planning is vital to get an organization to act in unison.

The second reason is that without a demand planning process in place, an organization will lack the system it needs to monitor the variance between forecasts and actual sales, what companies need to fine-tune their forecasts and safety stocks. The more accurate that you are able to make your forecasts, the less inventory you will need to cover fluctuations in demand. This in turn allows you to deliver higher customer service, better control of working capital and better profitability.

Thus demand planning encompasses analysis of historic data, organizational tie-in and unity, and continued monitoring to adjust to reality.

But even with data forecasting has its limits
Then again, what's the point of gathering historic sales and production data, analyzing it and creating goals that your entire team agree to target when you don't even know if customers will want the same colors or styles next month that they wanted last month?

This is a valid caveat that creates an added conundrum for competitors in apparel retail. Fast fashion over the past eight years has perfected speed to market, with companies cycling in new products almost every other week. Companies like Zara and Urban Outfitters have accomplished this by consolidating aspects of their supply chains, using technology to create more flexible fulfillment from distribution centers and physical stores, and monitoring data to adjust to demand.

Demand planning is certainly a part of their success stories, but as necessary as demand planning is to work toward perfect order fulfillment, there is an obvious limit to creating inventory forecasts.

While experts recommend collecting more than two years worth of data on sales and inventory to create the statistical analysis needed for inventory forecasts, forecast accuracy diminishes with increasing time horizons. In other words, the further into the future you attempt to predict, the less likely your forecast will present an accurate target. This is one of the reasons why apparel companies that have reduced their lead times have been able to to increase the relevance and effectiveness of their forecasting, and thus optimize their stock levels.

Not just data, but expertise
The forecasting process should be based on historical demand and information from the market. To get the most out of the data, forecasting should take into account the impact of one-off events and promotions, differentiate between seasonal items, slow-moving products and fast moving products.

Forecasts should also be managed according to the value of the products, with high sales value items forecast more regularly than low sales value items, if forecasting resources are constrained.

Ideally, history of sales should be demand history, not actual sales history. The reason is that sales history may reflect what customers were "forced” to accept due to shortcomings in customer service or business performance, rather than what they had preferred at the time of purchase.

Finally, the demand planning process includes the critical element of management and stakeholder review. It isn't just the data that's important, but the experts in an organization that can make the best use of that data to create forecasts.

With the technology in place to collect data from across a supply network, companies will be in a better position to fine-tune forecasts, most effectively for shorter time horizons. Technology will only improve data collection and analysis over time. Theoretically, with the right demand planning process, expertise and tools in place, companies will even be able to predict not based on a particular item, but rather on a particular attribute of a product, such as style or color. 

Danny Ing is the founder and chief architect of Cin7, a cloud-based inventory management solution with offices in Seattle.