Why predictive planning is essential in the new normal

In the crossroads between old and new, agile forecasting is one of the most important factors of success for achieving business resilience, maintaining production and services, and keeping the supply chain running while enabling businesses to cope with daily uncertainties.

Agile forecasting is about harnessing the true value of real-time analytics, extending required data sources when needed, and running rapid release cycles. It is also about empowering key business users involved with analytical processes to look and plan ahead. Using a shorter timeline, but with a wider and more comprehensive perspective, will enable businesses to manage for, and to realize, best possible business outcomes.

In this blog we consider how effective forecasting can help organisations to effectively manage business processes and create new opportunities in a climate of significant disruption.

3 common forecasting challenges for businesses

Set the right assumptions

Setting the right assumptions that impact forecast accuracy and then translating them into forecast models is a key challenge. For example, how will the inflation rate change, what if there are changes in tax laws or geo-political changes (for example, Brexit?) Another important aspect to consider is how relevant historical data is for making predictions in a world with many new variables and challenges.

Data

Do you have sufficient data with enough historical depth and with the right quality to enable a reliable forecast? If data is inconsistent, or the master data is missing key information, this will likely result in a poor, unreliable forecast.

Release cycle

From the moment a forecast is ready for review, what happens if planners need to change anything in the forecast, compare it to other versions, and determine the best one? This process is often complex and requires lots of offline, manual work that is rarely processed in an integrated way.

COVID-19 specific flexibility: Forecasts require the flexibility to go through multiple iterations rapidly, and to be compared quickly to other versions. Additionally, planners must conduct a fast evaluation as to whether a forecast version is ready for release. As organisations are currently more inclined to adapt their business strategy, shift market focus, and change the decision-making process driving campaigns or production of new products, they need to be agile enough to quickly change forecast models, business assumptions, and data sources. Rapid adoption and flexibility in forecast modelling gives businesses the ability to manage change and react to unexpected factors.

In the new normal, how can businesses produce accurate forecasts?

The typical process for producing forecasts was based on historical data and closed statistical models, usually submitted on a monthly or quarterly basis. Now, historical data is no longer effective. Financial planners need real-time data and external data sources to support their forecast models. Data sources can include geographical, social media, and governmental data. This has a direct impact on statistical modelling and the way in which assumptions are formed. Moreover, forecasts are now based on a reduced timescale. Business questions such as ‘will our cashflow support our financial obligations next week or the end of the month?’ need to have solid, predictable answers.

What is predictive planning and how can it enable effective forecasting?

Predictive planning is based on proven time series machine learning algorithms that can better predict cash flow, expenses, headcount needs, sales trends, and supply and demand. The planning process is a combination of actuals, budget, and forecast inputs. With predictive planning in place, planners don’t need to look at external sources and use complex offline Excel sheet formulas: they can leverage the power of machine learning and predictive analytics to make better decisions using more accurate forecasts. Using a data-driven approach where all required data is in one place, easy to access, to generate, and to compare simulations, is key to enabling effective forecasting.

Predictive planning enables planners and business analysts to go beyond the common planning cycle, where traditionally the forecast is uploaded from an external data source – such as a spreadsheet or an external system, which is connected to the planning process. In that sense, predictive planning is embedded within the planning model and process, consolidating planning, reporting and analytics in one place to achieve the best predictions while using planned data.

From that perspective, running multiple predictive simulations can help to quickly evaluate  potential impacts of different lockdown policies, which could potentially better inform critical decision-making for businesses.