Probabilistic forecasting: handling uncertainty in supply chains

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Demand forecasting is an important part of supply chain management and has a significant role in synchronized planning and reducing the bullwhip effect. There are two predominant methods of demand forecasting–deterministic and probabilistic.

Deterministic forecasting

Deterministic forecasting is predicting that a specific thing, a fixed outcome, will happen. It assumes that data is distributed equally around the mean. This works well in situations where you are sure that the usual, historical pattern will repeat, such as in the case of fast-moving, commodity items. You forecast a number for a particular item based on historical data or present conditions, using simpler systems, such as spreadsheets.

This approach usually disregards demand variability or uncertainty by consolidating demand. As a result, the forecast misses the effects of specific variables. For example, the forecast for two products with the same historical sales data will be the same, despite having different order patterns or differing popularity levels at various locations. This could cause inventory issues.

Probabilistic forecasting

Market volatility and changing global economic conditions make demand unpredictable. Many products are affected by unforeseeable variables. Consequently you must consider a range of possible outcomes.

Probabilistic or stochastic forecasting determines the probabilities of a range of possible outcomes, by using advanced algorithms to analyze multiple demand variables. It offers more reliable forecasts where demand patterns are variable, where there’s limited order history, or when factors like seasonality affect demand.

In other words, it works well for long tail items, spare parts, and new product launches, because their demand can be lumpy and unpredictable. It allows for the imprecision and uncertainty in demand, as well as multiple scenarios, by offering a range of values or outcomes.

Probabilistic forecasting focuses on demand patterns and what causes them. Therefore, the latest trends or early signals that influence the forecast, such as the impact of social media or trade promotions, can also be added to the demand model. New technologies, such as artificial intelligence, will further refine the forecast by integrating all available data.

Probabilistic Forecasting for Inventory Optimization

Inventory optimization (IO) is a form of predictive analytics. Probabilistic or stochastic planning systems predict the apt amount and type of stock to carry.

As mentioned earlier, probabilistic forecasting doesn’t create an average forecast. It determines a range of outcomes, with the probabilities of their occurrences.

Forecasts based on details about the demand streams, such as order patterns, yield a better understanding than those based on averages.

Leveraging uncertainty

In supply chain management, costs are driven by extreme events. Extremely high demand causes stockouts and customer frustration. Similarly, very low demand results in dead inventory and inventory write-offs.

When the demand forecast is accurate, everything goes smoothly. However, demand planners are tasked to tackle some tough supply chain issues.

Probabilistic forecasting quantifies the uncertainty in a prediction, which helps you to assess risks and plan for contingencies. You are also able to optimize your inventory better, especially in the case of harder-to-forecast items.

Sensitivity analysis

A sensitivity analysis is used to determine how changes in an independent variable affect certain dependent variables, under a given set of circumstances.

It is often performed prior to probabilistic forecasting, as it indicates which variables affect demand the most and which ones have no effect on it. It helps predict the outcome of a decision, given a certain range of variables.

The interplay of variables provides insights on various scenarios and leads to informed decision-making. You can identify risk factors and mitigate them, based on the results of a sensitivity analysis.

Probabilistic forecasting offers not only better forecasts, but also leads to myriad benefits. For instance, you will be able to understand specific demand patterns, as a result of which you can develop better supply chains and achieve higher service levels.