The predictive nature of forecasting makes it a notoriously tricky process to manage. Stakeholders from multiple business areas with different perspectives, needs and often conflicting agendas, must come together and collaborate.
This post discusses seven key considerations that will help set you up for demand forecasting success.
1. Define how the forecast will be used
Start by determining how the business will use the forecast. This step is critical and will impact your process design, data requirements, and system configuration.
Documenting the business decisions and processes that the forecast needs to support will ensure that you meet all your requirements. Here are some examples where a business will benefit from forecast information:
- Short Term – order fulfilment, distribution between warehouses, production planning, purchase planning.
- Medium Term – balancing demand and supply constraints, sales and marketing tactics, promotions, supplier contracts, product lifecycle management.
- Long Term – identifying gaps to strategy, financial planning and budgeting, long-term supply contracts, segments to invest in or exit from.
2. Define relevant KPIs
Once you’ve determined how the forecast will be used, you’re ready to develop supporting KPIs. Take care to align all KPIs to your overall business goals. Poorly defined KPIs risk undermining what you’re trying to achieve as a business.
A good set of KPIs should be measurable, well understood by your organization and supportive of your overall business goals.
Forecast Accuracy KPIs such as Mean Absolute Deviation (MAD) and Mean Square Error (MSE) show the forecast’s overall accuracy and quality and inform planners what forecasting method performs best in a particular situation.
Understanding forecast bias (mean forecast error) is essential to improve demand planning over time. Including a forecast bias indicator will expose if there is a tendency to over or under forecast.
Knowing what success looks like will help guide your process design and system configuration.
3. Understand where detail and accuracy matters
It’s now time to determine the level of forecast detail and accuracy that’s required to meet your specified business goals. These levels will largely depend on your time horizons.
- Strategic Goals – Monthly or quarterly forecasts over a longer time horizon. High-level data is often sufficient, e.g. at the sales division and product category levels.
- Operational Goals – Daily or weekly forecasts over a shorter time horizon. More granular product, location and market information is usually required to fully support the planning of production, transportation, storage and sales.
A word of caution! There’s often a tendency to include as much detail as possible upfront. Although this can provide extra flexibility, it also adds risk in the form of:
- Information overload – Too much information can be intimidating to some users
- Focusing on the wrong thing – Consider a system that creates a weekly forecast by product and customer for a whole year—in most cases, forecasting at such a detailed level that far into the future will add little value. Also, the result is typically less accurate than a forecast generated at a more aggregated level.
- Unnecessary system load – Suppose a business has a large customer base managed at a customer segment level. The company can reduce both database size and processing time by excluding detailed customer data. Similarly, if a business effectively runs at a monthly level, adding daily or weekly information may not be needed.