From Fresh to Finish: Tackling the Challenges of Demand Planning for Low Shelf Life Products

Demand forecasting for FMCG products with low shelf life is HARD, as it has unique challenges due to the perishable nature of the goods. Here’s a deeper look at challenges involved and some ideas to resolve them -

• Shorter Time Horizons: Unlike products with longer shelf lives, perishable items have a limited window for sale. This requires demand forecasting models to operate within shorter time horizons like generate daily predictions instead of more common weekly or monthly.
  
  Opting for most granular data at daily level and deploying sophisticated neural architectures which can capture short-term fluctuations accurately will provide better results.
• Variability due to seasonality or events: Demand for perishable products can be highly seasonal and subject to sudden changes due to factors like weather conditions, holidays, and cultural events. Additionally, promotional activities, such as discounts or special events, can have a significant impact on demand for perishable FMCG products.
  
  Predicting these fluctuations accurately is challenging and requires sophisticated forecasting techniques along with capabilities to add inputs around promo events, external events and holidays.
• Expiry & Wastage: Maintaining product freshness and quality is crucial for customer satisfaction. However, keeping up with demand while simultaneously minimizing waste due to spoilage is a delicate balance. Overestimating demand can lead to excess inventory and increased waste, while underestimating can result in stock-outs and lost sales.
  
  Considering expiry dates and tolerance levels based on the nature of the business and creating inventory plan based on that will ensure that wastage is minimized.
• Demand Volatility: Perishable goods often experience more significant demand volatility compared to non-perishable items. External factors like sudden changes in consumer preferences or market trends can lead to rapid shifts in demand, making it challenging to predict accurately.
  
  Employing neural networks, known for their ability to achieve higher accuracies compared to alternative techniques, can effectively reduce the impact of volatility.
• Supply Chain Dynamics: The perishable nature of these products adds complexity to the supply chain, with factors like transportation, storage, and handling affecting product quality and shelf life. Supply chain disruptions, such as delays or quality issues, can further reduce the accuracy of demand forecasting.

There are two ways to solve these challenges-

1. Companies can establish a dedicated data science and analytics team to leverage advanced forecasting techniques such as time series analysis, machine learning, and predictive analytics. However, this method involves initial setup costs for hiring, training, and a longer time to value (often spanning months).
2. Alternatively, businesses can opt for a self serve product like TrueGradient AI, which empowers supply chain professionals to leverage advanced algorithms in a complete no-code way along with creating custom solutions as per their business needs within a matter of days.

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