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These five steps will help you increase accuracy of your demand forecasting process, resulting in less inventory waste.

Five Steps to Improve Demand Forecasting Accuracy

Contents
increase accuracy of long term demand forecasting with these tips

Introduction

Even small improvements to your demand forecasting process can have a huge impact on inventory management. Based on my experience, I identify some of the most common mistakes with forecasting demand, and provide the following tips to improve demand forecasting in your own supply chain:

1. Define The Sales Pipeline

Improve Demand forecasting accuracy with sales pipelines and market research The sales pipeline is a critical input into all types of demand forecasting process, as it informs the business of new customer wins and potential drivers to short term demand. The planning team needs to know about new wins in order to forecast future sales for the business. For this reason, you should make sure that all members of the sales team document their pipeline in a consistent manner

Is Your Sales Team Overstating Wins?

Sales managers drive their teams to report new wins, and it isn’t uncommon for the sales associates to exaggerate the customer demand in their sales pipeline. If this occurs, the demand forecasting team will predict higher future sales than is appropriate. 

overstated sales wins impacts demand forecastingI once worked with a business in which the sales team routinely classified prospects as ‘wins’, when in fact they had not yet won the account

While this action started with just a couple sales leaders, it became widespread practice among the remaining sales team, who felt compelled to reach the level of their peers. 

As a result of this , the business’s demand forecasting process nearly always overstated customer demand. By the time the management became aware of this practice, their supply chain had already built an enormous amount of excess inventory in anticipation of future demand. The inventory eventually aged, and the business was forced to write off and dispose of the inventory as it expired. In short, this made inventory management especially difficult.

Because a reliable sales pipeline is so important in short term demand forecasting, you may choose to incentivize the sales team based on whether their pipeline reporting proves to be accurate. In my example above, the sales associates were strongly motivated to report customer wins, as it affected their status on a leader board. However, this resulted in over-reporting of customer demand. In turn, this faulty sales data generated an inaccurate forecast of future demand. You should instead motivate the sales team to help you accurately forecast short term demand for your products, even making it part of the bonus structure.

Assign Probabilities

A probability-adjusted pipeline improves demand forecasting and enables better business decisionsThe pipeline should reflect both the likelihood of customer wins, as well as the expected timing based on stage of each prospect. I encourage businesses to adopt consistent practices for quantifying the credibility of the pipeline. You can then apply risk-adjusted probabilities when you begin to forecast demand resulting from new wins. Sales prospects in the early stage should have very little or zero impact in the demand forecast. Once the prospect has engaged with active contract negotiation, you may begin incorporate the likely outcome in your demand forecasting process.

How this would work in practice.

You have a sales prospect has indicated an interest in purchasing 1,000 quantity of a product per month, and have just begun negotiating pricing and payment terms with your sales team. You typically realize 60% success rate for customers at this stage of the sales cycle. For this reason, you could forecast demand for 600 units for the next month. Once the prospect has made a purchase order, you then include the full 1,000 units in your demand forecast.

2. Ask for Customer Forecasts

trend projection from large customers aid in demand forecastingYour customers have the best information on the growth prospects for their own businesses. While you can always perform a time series analysis of historical data, you will still be guessing on the likely growth of these customers. If your largest customers are willing to share their long term forecast of expected purchases, you can incorporate this knowledge into your own demand forecast model. 

Customers are frequently reluctant to provide a demand forecast of their future demand, as they don’t want to be held accountable when actual demand is different from their forecast. No one wants to give estimates if they aren’t required. As a consumer, have you called a contractor and asked for an estimate for a major house remodeling project? They generally will decline, and tell you they need more details before even thinking of giving you a quote. The same holds true for your customers – They will hesitate to give you an estimate of long term product demand before they actually need the product. 

So, how do you encourage your customers to begin forecasting demand for their own businesses? 

Exchange for Modest Price Concession

offer price concessions to customers who provide demand forecastingFirst, you could offer a pricing concession to customers who provide you with forecasting data. You can likely afford to give some price concession for this, as more accurate demand forecasting results in better inventory planning for your business. You likely have the ability to sell at a slightly lower price, while still saving money on warehouse costs. Everybody wins!

Alternatively, you could offer a guaranteed service level (or fill rate) to each consumer who is willing to forecast their product demand. For instance, you may have a typical fill rate of 94%. For those customers that assist you with forecasting demand, you may agree to guarantee a fill rate of 96%. Better forecasting helps you to have higher fill rates anyhow. Thus, this promise likely costs you very little.

3. Statistics Predicts Customer Demand

Statistics combines actual data with market research to improve demand forecasting

Using Historical Sales Data

Statistical methods allow you to use past sales data to create a trend projection of customer demand in near real time. While a simple times series model has shown to increase accuracy of most demand forecasting processes, only 20% of demand planners use statistics in their demand forecasting models. This is likely because statistics is a little overwhelming for many employees. Your demand planner would most certainly be more effective at demand forecasting if they understood how to apply statistical principles to historical data.

This is one of those cases where it is absolutely worth it to invest in education for your key supply chain employees. Find an online course, or even make plans for your employees to attend a course at the local university. Whatever route you take, your demand planner will need to know the fundamentals of statistics prior to deploying statistics-based software in your business. They should be comfortable with performing regression analysis, which is essentially a tool to build a trend projection based on historical data.

With this knowledge established, you can now employ basic statistical methods in your demand forecasting. As an example, if your business makes plumbing supplies, you may find that your historical sales data is highly correlated to the level of new construction starts. Or, if you make components for the automobile industry, you may find that demand for your products is highly correlated with consumer purchases of new vehicles.

There is nearly always an industry or economic indicator that can be used as a predictor for your sales. You just have to find it, and develop methods for applying it to your own demand forecasting.

By incorporating industry and economic indicators, it is quite possible you can increase the accuracy of your demand forecasting by 10% or more. This action alone can dramatically decrease the amount of stock and make your existing inventory management practices even more effective. 

You can further refine your statistical analysis by implementing a software package designed specifically for demand forecasting. Software packages aren’t able to identify economic/industry predictors as described above, since that requires real humans that know your business. It requires human judgement to make decisions as to which industry data is most relevant. However, these packages do excel at performing time series analysis with historical data. Specifically, software is ideal at quantifying seasonal factors and order trends, both of which can further improve your forecasting.

4. Collaborate Across Functions

By using time period data, other functions can help with the process of forecasting demand.It’s not just Supply Chain.

Frequently, demand forecasting is viewed as a ‘supply chain activity’, and is managed in isolation. This perception prevents other functions from contributing valuable input into this critical forecasting process.

The marketing leaders are typically charged with performing market research, and have the best knowledge about long-term trends in the marketplace. Further, they are aware of upcoming product innovations, and can provide insight into upcoming changes in the company’s product portfolio. For this reason, marketers should have substantial input into the long term demand forecasting process.

Sales leadership should also be included – Remember, the customer pipeline is a key component of demand forecasting, and you should ensure sales leaders understand how you are including the pipeline data in your short term demand forecasting process.

5. Eliminate Unprofitable SKUs

Improve demand forecasting by making critical business decisions about product portfolio.Virtually all businesses have an excessive number of product variants, many of which actually generate losses to the business. At a macro level, about 20% of a company’s product portfolio generates three quarters of its revenue. These are the high-volume products that are core to the business strategy, and have highest profit margins and highly predictable demand.  

Stepping down a level, the next 30-40% of products are still profitable, but generate lower volumes with slightly higher volatility. This middle tier of products generates about a quarter of the cash flow for the business.

Finally, the bottom 20-30% of the product portfolio generates little revenue. This is often referred to as the ‘product tail’ and demand for these products is highly volatile. This bottom level of products frequently lose money, as it’s hard to predict demand for these products.

Businesses often don’t realize that these are actually money losers, because they may in fact generate revenue and gross profit from these products. However, when you factor in the real cost of the product tail, such as warehouse cost and inventory scrap costs, they inevitably lose money. 

By eliminating unprofitable product variants, your customers may begin purchasing your most popular products instead – They may have bought a specific product variant simply because they were unaware of other options. Even if they don’t, you are still better off by eliminating products that generate losses. 

Let’s be honest – It is hard to make decisions to eliminate products. It is virtually always easier to make investment decisions that result in increased capacity or added breadth to your product line. It almost seems like a failure of the business when you take steps to eliminate any product. This is largely why businesses often fail to make the necessary decisions to prune the product line. However, by eliminating unprofitable SKUs you will undoubtedly increase profitability and cash flow in the business.

Summary: Demand Forecasting is a Journey

Remember, demand forecasting is equal parts art and science: You will never reach 100% accuracy; by nature forecasting is never perfect. However, a good goal is to use actual sales data to continuously deploy and refine methods that yield the most accurate forecasting of demand. Like any other process, you won’t always see results in the short-term. However, if you make plans for steady forecast improvement over time, you will gradually see the benefit through lower inventory and higher customer fill rate. 

Do You Like What You’re Reading?

I help businesses achieve Perfect Clarity in their demand forecasting process.  I identify the root cause of forecasting issues, and help you deploy a Best-in-Class planning process to help you better manage inventory in the future. 

About the Author

Bryce Bowman

Bryce Bowman

Bryce has over two decades of leadership roles in finance and supply chain. In his supply chain roles, he built reporting for multi-billion dollar supply chains. As Division CFO, Bryce established reporting and controls for a multinational industrial business. Bryce now helps companies solve inventory issues through better planning.

Demand Planning Consultant

I provide Clear Analytics into your demand planning process.

I isolate the Root Cause of inventory issues, and help you deploy best-in-class planning.

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