Enhancing Statistical Forecasting: Mitigating Limitations through Innovative Approaches

In today’s fast-paced and highly competitive business environment, effective supply chain management plays a pivotal role in the success of any organization. A cornerstone of supply chain management is forecasting, a strategic process that empowers businesses to anticipate demand, plan resources efficiently, and make informed decisions. Among various forecasting methods, statistical forecasting stands out as a widely used and reliable approach that leverages historical data to predict future trends.

It offers several advantages, such as simplicity, ease of implementation, transparency, and a proven track record of success. However, like any forecasting approach, statistical forecasting is not without its limitations. These limitations can impact the accuracy and reliability of forecasts, potentially leading to suboptimal outcomes and missed opportunities. In this post, we will explore some of the key limitations of statistical forecasting and delve into innovative ways to mitigate its limitations using ABC-XYZ classification, driver-based forecasting, and other methods. 

Insufficient Handling of Irregular products:

Statistical forecasting relies on the assumption that patterns detected in historic sales will reoccur in the future. However, this is not always the case due to complex seasonality and irregular demand patterns. Poor accuracy can result for seasonal products and products with irregular demand patterns, leading to unreliable forecasts, inventory imbalances, and operational inefficiencies. It also threatens the overall credibility of the statistical forecast in communication with other departments involved in demand management process like Sales, Marketing or Finance.

In order to address this issue, experienced supply chain professionals employ ABC-XYZ classification. It is a powerful tool for segmenting products based on their value (ABC) and demand predictability (XYZ). By classifying products into different categories, companies can apply tailored forecasting approaches to enhance accuracy. ABC-XYZ classification is a cornerstone of EyeOn Planning services approach to demand planning. Each category can have its own set of models which are the most suitable for the given product group. The classification is also used to direct the efforts of planners or account managers to the products where it is needed the most where their knowledge can improve accuracy. On the other hand, products where potential value-add of manual intervention is low are forecasted using automated/no-touch methods. 

Demand Volatility and External Factors:

Traditional statistical models often fail to incorporate external factors such as economic conditions, competitor actions, or commodity market conditions, leading to inaccurate forecasts during periods of demand volatility. Driver-based forecasting involves identifying key external drivers that influence demand and incorporating them into forecasting models. This approach helps address the limitations of statistical forecasting by considering external factors and understanding demand drivers.

EyeOn Planning Services can help you to identify the external drivers influencing demand, such as weather, calendar and public holidays (e.g. Chinese New Year), consumer confidence index, or other economic indicators. Incorporating these drivers into the forecasting model for the whole portfolio of for some parts of it allows for a more comprehensive view of future demand fluctuations. 

Limited Handling of New Product Introductions (NPI):

Forecasting new product launches using historical data can be challenging as there is limited relevant historical information to base predictions on. This can lead to overstocking or stockouts during the initial stages of a product’s lifecycle. Collaboration between supply chain, marketing, sales, and finance teams is vital for accurate NPI forecasting. Regular meetings and data-sharing practices enable teams to stay informed about factors that might impact demand. Enrichment & consolidation solutions play the vital role in the successful data-sharing process.

EyeOn Planning Services has multiple years of experience creating enrichment and collaboration environments utilizing Jedox technology. These solutions provide solid foundation for the demand planning process not only for NPIs, but for other products where additional manual inputs are required. 

Lack of Incorporating Causal Relationships:

Statistical forecasting typically focuses on identifying patterns in historical data without explicitly considering the causal relationships with other variables. This limitation can be particularly problematic when forecasting complex business environments where decisions need to be made well in advance. Quality of forecasting can be easily improved while reducing the amount of manual changes needed using driver-based forecasting calculated on internal drivers.

Machine learning techniques applied by EyeOn Planning Services can improve the quality of your forecasting. Knowing the data that is available within an organisation, including order book composition, promotions and marketing campaign data, and planned price changes, machine learning techniques applied by EyeOn Planning Services can provide richer data set to the calculation model which results in forecast that covers not just your baseline, but your whole portfolio of products. 

Sensitivity to Outliers and Anomalies:

Supply chain statistical forecasting is sensitive to outliers and anomalies in historical data, which can distort predictions and lead to inefficient resource allocation. A single unexpected event, such as a natural disaster or a sudden change in customer behavior, can significantly impact the forecast accuracy. Addressing outliers and anomalous data points appropriately is essential for maintaining robust forecasts, however these processes can be very time consuming. EyeOn developed a special technique to deal with this kind of events in automated way. This machine learning based Tetris outlier correction logic substantially improves history cleansing process efficiency by replacing monotonous work with automated algorithms. 

Each limitation in statistical forecasting poses unique challenges to supply chain management. However, by employing suitable mitigation mechanisms, businesses can enhance their forecasting capabilities and make informed decisions in an ever-changing market. Over the years, EyeOn Planning Services has developed multiple efficient and proven technics that have helped multiple companies across all industries to better navigate uncertainties, enhance competitiveness, and build a more resilient supply chain ecosystem.

Want to know more? Get it touch with Andrey Averyanov or contact us via the button at the bottom of this page.

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