for push processes, a manager must forecast what customer demand will be in order to

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

5 Steps to Meliorate Demand Forecasting Accuracy

Introduction

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

one. Ascertain The Sales Pipeline

The sales pipeline is a critical input into all types of demand forecasting procedure, as it informs the business of new customer wins and potential drivers to short term demand. The planning squad needs to know almost new wins in order to forecast future sales for the business. For this reason, you should brand sure that all members of the sales team document their pipeline in a consistent manner.

Is Your Sales Squad Overstating Wins?

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

I in one case worked with a business concern in which the sales squad routinely classified prospects as 'wins', when in fact they had non withal won the account.

While this action started with merely 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 issue of this , the business's demand forecasting process nearly always overstated client demand. By the fourth dimension the management became enlightened of this practice, their supply concatenation had already congenital an enormous corporeality of excess inventory in apprehension of future demand. The inventory eventually aged, and the business concern was forced to write off and dispose of the inventory as it expired. In brusk, this made inventory management particularly difficult.

Because a reliable sales pipeline is so important in brusk term demand forecasting, y'all may cull to incentivize the sales team based on whether their pipeline reporting proves to be accurate. In my example to a higher place, the sales associates were strongly motivated to study client wins, as it afflicted their status on a leader board. However, this resulted in over-reporting of customer demand. In plow, this faulty sales data generated an inaccurate forecast of future demand. Yous should instead motivate the sales team to aid you accurately forecast short term demand for your products, even making it part of the bonus structure.

Assign Probabilities

The pipeline should reflect both the likelihood of client wins, besides as the expected timing based on stage of each prospect. I encourage businesses to adopt consistent practices for quantifying the brownie of the pipeline. You can and then apply risk-adapted probabilities when you lot begin to forecast demand resulting from new wins. Sales prospects in the early stage should accept very little or zero impact in the demand forecast. Once the prospect has engaged with agile contract negotiation, you may begin incorporate the likely outcome in your demand forecasting procedure.

How this would work in practice.

You accept a sales prospect has indicated an involvement in purchasing ane,000 quantity of a production per calendar month, and take just begun negotiating pricing and payment terms with your sales team. You typically realize sixty% success rate for customers at this stage of the sales bike. For this reason, yous could forecast demand for 600 units for the side by side month. Once the prospect has made a buy club, you lot and then include the total 1,000 units in your need forecast.

two. Inquire for Client Forecasts

Your customers have the all-time information on the growth prospects for their own businesses. While you tin can ever perform atime series assay of historical data, y'all will still exist guessing on the likely growth of these customers. If your largest customers are willing to share their long term forecast of expected purchases, you lot can comprise this cognition into your own demand forecast model.

Customers are frequently reluctant to provide a need forecast of their future demand, every bit they don't want to be held accountable when bodily demand is different from their forecast. No 1 wants to give estimates if they aren't required. Every bit a consumer, have you chosen a contractor and asked for an estimate for a major house remodeling project? They generally will decline, and tell yous they demand more details before even thinking of giving you lot a quote. The same holds true for your customers – They will hesitate to requite you an gauge of long term production demand before they actually demand the product.

And then, how practice you encourage your customers to begin forecasting demand for their own businesses?

Exchange for Pocket-size Price Concession

First, y'all could offering a pricing concession to customers who provide you with forecasting data. You can probable afford to give some toll concession for this, as more accurate demand forecasting results in ameliorate inventory planning for your business. You likely take the ability to sell at a slightly lower toll, while still saving money on warehouse costs. Everybody wins!

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

3. Statistics Predicts Customer Need

Using Historical Sales Information

Statistical methods allow you to employ past sales data to create a tendency project of customer demand in near existent time. While a simple times series model has shown to increase accuracy of about demand forecasting processes, only 20% of demand planners use statistics in their demand forecasting models. This is probable because statistics is a little overwhelming for many employees. Your demand planner would nearly certainly be more than 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 fundamental supply chain employees. Discover an online class, or even make plans for your employees to attend a course at the local university. Any route y'all have, your demand planner will demand to know the fundamentals of statistics prior to deploying statistics-based software in your concern. They should be comfy 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 bones statistical methods in your need forecasting. As an case, if your business concern makes plumbing supplies, you may find that your historical sales data is highly correlated to the level of new structure starts. Or, if you make components for the automobile industry, yous may find that demand for your products is highly correlated with consumer purchases of new vehicles.

At that place is well-nigh always an manufacture or economical indicator that tin can exist used as a predictor for your sales. You just have to find information technology, and develop methods for applying it to your own demand forecasting.

Past incorporating industry and economic indicators, it is quite possible yous tin increase the accuracy of your demand forecasting past 10% or more. This activity lonely can dramatically decrease the corporeality of stock and make your existing inventory management practices even more constructive.

You lot tin further refine your statistical analysis by implementing a software packet designed specifically for demand forecasting. Software packages aren't able to identify economic/industry predictors equally described to a higher place, since that requires existent humans that know your business. It requires man judgement to brand decisions as to which industry data is well-nigh relevant. However, these packages practise excel at performing time series assay with historical data. Specifically, software is platonic at quantifying seasonal factors and order trends, both of which can further improve your forecasting.

4. Collaborate Across Functions

Information technology's not but 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 procedure.

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

Sales leadership should as well be included – Remember, the client pipeline is a key component of demand forecasting, and yous should ensure sales leaders empathise how you are including the pipeline data in your curt term demand forecasting process.

five. Eliminate Unprofitable SKUs

Almost all businesses have an excessive number of production variants, many of which actually generate losses to the business. At a macro level, about 20% of a company's product portfolio generates 3 quarters of its revenue. These are the high-volume products that are core to the business strategy, and accept highest profit margins and highly predictable demand.

Stepping down a level, the side by side 30-40% of products are still assisting, simply generate lower volumes with slightly higher volatility. This middle tier of products generates nigh a quarter of the greenbacks flow for the concern.

Finally, the bottom 20-30% of the product portfolio generates little revenue. This is frequently referred to as the 'production tail' and demand for these products is highly volatile. This bottom level of products frequently lose money, as it'due south hard to predict need for these products.

Businesses often don't realize that these are actually money losers, because they may in fact generate acquirement and gross turn a profit from these products. Yet, when you factor in the real cost of the production tail, such as warehouse cost and inventory scrap costs, they inevitably lose coin.

By eliminating unprofitable product variants, your customers may begin purchasing your most pop products instead – They may have bought a specific product variant simply considering they were unaware of other options. Fifty-fifty if they don't, y'all are withal better off by eliminating products that generate losses.

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

Summary: Demand Forecasting is a Journeying

Recollect, need forecasting is equal parts art and science: You will never reach 100% accurateness; by nature forecasting is never perfect. However, a skillful goal is to use bodily sales data to continuously deploy and refine methods that yield the nearly authentic forecasting of demand. Like any other procedure, you won't always see results in the brusk-term. Still, if you make plans for steady forecast comeback over fourth dimension, yous will gradually run into the benefit through lower inventory and higher client fill up rate.

Do You Like What You're Reading?

I help businesses reach Perfect Clarity in their demand forecasting process. I place the root cause of forecasting issues, and help you lot deploy aBest-in-Class planning process to help you better manage inventory in the hereafter.

About the Author

Bryce Bowman

Bryce has over two decades of leadership roles in finance and supply concatenation. 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 at present helps companies solve inventory issues through better planning.

Need Planning Consultant

I provide Articulate Analytics into your need planning process.

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

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