Quadrant Knowledge Solutions Market Insights research provides detailed insights on sophisticated forecasting techniques leveraging complex machine-learning algorithms can assist retailers, especially those selling items with short lifespans, to maximize sales and reduce wastage by effectively managing the inventory.
Grocery, especially for fresh items have a very complex supply chain, and it is aggravated by a sudden shift in consumer behaviour, preferences, seasonality, and a plethora of factors. It is difficult to match supply and demand for a diverse inventory that includes fresh and items with short shelf-life on one end of the spectrum and ambient products on the other. This is the result of poor demand sensing done by traditional forecasting models. In addition, sustainability has also taken centre stage internationally in recent times especially due to the COVID-19 pandemic. Initiatives involving the environment, society, and governance (ESG) are becoming more significant for the retail industry. It is important for retail organizations to not only sell eco-friendly products but also to minimize resource wastage by striving for environmentally friendly operations
Fresh item forecasting can alleviate most of the pain points for retailers if the forecasts are for spoilage of each product when placing an order, are derived by calculating shelf life, considering the unique features of each product, and estimating the optimal balance between spoilage and out-of-stocks. Spoilage forecasts for each product can assists retailers to order fresh items by considering their future demand along with the duration for which the item will be fresh depending upon other external factors such as weather. This keeps the ordered product fresh and as the inventory turnover is high the spoilage is reduced resulting in increased customer satisfaction. So, the key in making better decisions for spoilage forecasting is with knowledge of calculated shelf life.
According to Ankit Sharma, Analyst at Quadrant Knowledge Solutions “Consumer behavior plays a critical role in how the sales of a particular fresh item take place hence solutions can be designed with projection capabilities that take into account both individual customer habits and market demand at the time that goods are being prepared for shipping. Most of the shoppers nowadays want the best product with the best price and retailers are trying to strike a balance between shopper needs and their own needs for better margins, customer satisfaction & reduced wastage. Although systems backed by machine learning models can achieve a near-accurate forecast if systems are artificial intelligence-enabled, then it would provide a larger scope by leveraging features such as image recognition to increase accuracy.”
Table of Contents
- Options available to Retailers to deal with spoilage.
- Importance of Forecast Accuracy.
- Sophisticating Forecast process with additional variables.
- Final Word.
This Market Insights is a part of Quadrant’s Retail Forecasting and Replenishment (RF&R) practice
Author: Ankit Sharma, Analyst and Neelam Singh, Practice Director, Quadrant Knowledge Solutions.
