7 ways to improve demand planning accuracy for increased operational efficiency and profit
There’s no crystal ball that can tell you everything happening in the market so you can perfectly predict product demand. However, there are quite a few ways to improve the accuracy of a demand forecast so you can increase operational efficiencies and profit throughout your organization.
1. Question and communicate assumptions
The first step in the forecast process is to determine your assumptions, or starting values. Take care to derive these values statistically, analyze them, and communicate them. They are the basis of your forecast quality. Communicating them to your team members provides context for generating the initial forecasting model.
Some basic assumptions might include:
- Number of buyers in the target market
- Percentage of consumers in the target market that will purchase
- The timing of their purchase, taking economic cycles and holidays or seasonal events into account
- Pattern or frequency of repeat or replacement purchases
It’s important to leverage the collective knowledge and expertise of the various teams involved to validate these assumptions before you start the forecasting process.
2. Use a granular model
Your forecasting models, especially those used for predicting short-term demand, need to have enough detail and granularity on the purchasing patterns of the various segments within the market.
A granular model can inform:
- When customers will make a purchase
- The different purchasing behaviors exhibited by markets in different locations
- The optimal price point to help maximize revenue
Keep in mind that even though a high-level or aggregated demand plan is easier to create and in some cases most helpful, it may not have sufficient granular detail to address consumer behaviors or purchasing patterns at the sub-SKU level.
For example, a car manufacturer may be able to predict accurately the number of SUVs they sell in a year. However, if the demand planning team doesn’t take into account regional preferences, the company may fail to produce the correct number of models in the sub-SKU level and distribute them to the right locations.
3. Produce a range of forecasts
You can achieve a broader perspective for your demand plan by producing a range of forecasts that you can re-calculate frequently to reflect market conditions, changing assumptions, and probabilities.
Besides historical data, you can use the demand sensing approach to get a more accurate, daily demand plan for the short-term horizon (4-6 weeks) so you can respond quickly to immediate changes on both the supply and demand side.
You can easily generate a range of forecast models if you use a demand planning software that allows you to pull in real-time data and plug it into pre-determined models. You can then create forecasts to share with all relevant departments in your organization through a customized dashboard and reports.
4. Minimize delays
Delays happen when market demands are not reflected in the supply chain with minimal lapse time and they can severely impact the accuracy of your forecast. To minimize the impact of such delays, use fully integrated supply and demand forecasting models to increase adaptability and operational efficiency.
These models should preclude delay and shorten renewal cycle by allowing your team to compare stock across all locations and generate a detailed replenishment report for each product. Such reports help prevent stock-outs from occurring, especially during the most uncertain period immediately after a product launch.
An integrated demand-supply planning process also gives businesses the opportunity to respond to market trends in a nimble manner (e.g. “fast fashion,” for which retailers such as Zara, Topshop, H&M, etc. are well-known), which wouldn’t be possible without the software and technologies that utilize connected data to make short lead times possible.
5. Employ a variety of forecasting methods
While the most common and essential models are based on purchasing intentions, there are other models you can use to consider all factors from different angles and perspectives.
By applying different forecasting methods to different phases of a product life cycle, you can leverage the most appropriate historical data and market knowledge. The key is to choose the most effective and flexible models for your market, blend their best features, and shift between them to generate the most accurate forecast.
For example, historical demand is a great starting point for forecasting mature products with plenty of history. But for new products with little or no history, consider advanced techniques such as comparable forecasting to make use of historical data from similar products.
6. Give your forecast continuous reality checks
You should regularly check actual sales against forecasts based on quantitative and qualitative data as soon as it becomes available. Use any discrepancies you uncover to fine-tune future forecasts to ensure that they are grounded in real market conditions.
This reality check should also involve assessing your competitor’s sales and performance in the market. If you’re entering an emerging market, also consider how your market share will be impacted as competitors enter the space and the market expands.
Conduct these reality checks regularly and more frequently for a newly launched product or under rapidly changing market conditions. Your team needs to monitor sales and qualitative feedback diligently and re-forecast whenever necessary.
7. Improve your planning with integrated supply-demand forecasting
Accurate forecasting is fundamental to supply chain management, as well as to sales and operations planning for the entire organization. You can improve the accuracy of your demand forecasts by employing different models and drawing data from a variety of sources. The faster you can pull your data and share your forecasts with your entire organization, the more valuable the information will be for everyone involved.
You can do so effectively by using software such as Vanguard Software’s cloud-based Integrated Business Planning (IBP) and forecasting platform. This application allows demand and procurement teams to model and integrate data, forecast, collaborate, and report to maximize the impact of critical information and knowledge. It helps integrate supply chain management and demand planning so you can immediately share any update and prediction with all business units to make sure the organization is equipped to respond to the market in a nimble manner, helping increase profitability and the operational efficiency of the entire organization.
About Vanguard Software
Vanguard Software introduced its first product for decision support analysis in 1995. Today, companies across every major industry and more than 60 countries rely on Vanguard Software’s Integrated Business Planning (IBP), forecasting and advanced analytic cloud platform. Vanguard Software is based in Cary, North Carolina.