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Demand forecasting is a critical business requirement to support a range of business functions, including logistics, production and finance. It is only with an agreed and considered forecast that functions can adequately plan capacity, inventory, labour and cash-flow.
When forecasting demand, it is key that all elements that can impact sales are identified, assessed and incorporated. Consequently, demand forecasting is not only a statistical exercise, but also requires cross-functional input from teams including sales, supply chain, finance and production.
Many businesses fail to have a cross-functional forecasting process, and often rely only on statistics. This can lead to a lack of business confidence in the forecast and it is not uncommon to find different departments developing their own isolated forecasts; sales, supply chain, production and finance will often see the future very differently!
A good demand forecast can only be the result of a good demand forecasting process. That process needs to be fast and dynamic for all participating functions, and it needs to have cross-functional consensus. The first step needs to be a statistical ‘baseline’ expectation which is then passed through each department for sense-checking and input. Varying additions or constraints may need to be applied, including marketing promotions, production capacities, logistics capacities, supplier issues and inventory constraints.
Depending on client requirements we can take several approaches to developing a demand forecast. To give some guidance on a typical demand forecasting approach, we have detailed below 7 steps that we often follow:
Defining the hypothesis for future sales demand is an important first step. The hypothesis is likely to be formed from a strategic intention alongside suppositions on the economic environment, market opportunity and competitor performance.
With the hypothesis defined, further consideration can then be given to what data will need to be collected to prove/disprove the supposition. This may take the form of tracking sales calls made, volume demanded by targets, sales executives’ perceived probability of success, conversion rates into actual sales, forecasting of future demand from new clients, or tracking how that forecast compares to quantity actually demanded.
At Step 2, our consulting team will convert the business hypothesis and supporting assumptions that were generated into a mathematical model, and will design a data capture process with templates. The templates will incorporate automatic data validation as well as useful dynamic dashboards for the stakeholders to observe.
Additionally, a consolidation tool to collate the data being tracked can be created, allowing for easy assimilation of data points.
Using the Data Tracking Templates from Step 2, our supply chain consultants will test the validity of the hypothesis as data develops, and potentially collect alternative data in the event of early detection of variances.
Any data or useful insights that could be used to help the sales team will be passed on, and additionally, dashboard reporting of performance on a monthly basis will be generated, so that different approaches can be compared and used for performance evaluation / improving sales techniques.
In this step the consulting team will conduct a final analysis of how sales levels have evolved and generate the the baseline forecast.
A scenario analysis will also be run to assign probabilities to various events that could impact sales levels on the mid-to-long term horizon. Typical event variables could include substitute products causing a change in equilibrium on the supply-demand curves or regional economic conditions resulting in a shock to aggregate demand.
The outputs from this scenario analysis will be run through a Monte Carlo simulation in order to generate a prediction interval for the baseline forecast, within which it is likely future sales will fall.
Outputs from this modelling include a One-at-a-Time analysis tornado chart, identifying the key parameters and events that impact the forecast, as well as a cumulative probability distribution output showing the range and probabilities of various sales outcomes.
Our consulting team will, using the models and the data from the preceding four steps, design an easy to use forecasting tool to allow the client to continue tracking and forecasting sales independently.
Where deep integration of forecasting tools may be required with a clients ERP system, or integration with MRP functionality, then the consultants can support system selection as a Step 6.
There are a host of forecasting systems available on the market, from standalone simple to use fixed algorithm systems, through to highly advanced adaptive algorithm systems specifically design for use with major ERP platforms such as Oracle and SAP.
Using the learning from the previous steps our consultants can develop a systems requirements template and match this to the most appropriate systems available in the market.This ensures that any systems purchase meets the exact client requirements, without purchasing any unnecessary functionality.
Forecasting demand is not purely a statistical or systems driven exercise. It is critical to have a collaborative forecasting process ensuring there is cross-functional input from the sales, supply chain, finance and production teams.
Without a collaborative demand forecasting process being put in place there is likely to be a lack of both accountability and confidence in the demand forecast.
Our consultants will design, in conjunction with the stakeholders, a detailed forecasting process. This forecasting process, if required, can then also be incorporated within the company’s S&OP process. Please read our section on the S&OP design and implementation.
Contact our supply chain consultants to discuss any aspect of demand forecasting and demand planning. From statistical modelling to S&OP process development and implementation, we’re here to provide the support you need. Call us on +256709299755 or use our contact form:
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