How Machine Learning Can Maximize Inventory Management

PG: How can retailers apply AI/ML models to data?

SH: Data plays a fundamental role in the development and success of AI/ML-driven inventory management solutions in many ways.

• Data Collection. Master and transactional data, supplier information, inventory levels, pricing records, customer transactions, and external factors like weather are gathered from point-of-sale systems, sensors, online sales records, and other sources.
• Data Preprocessing. Raw data needs preprocessing for AI/ML analysis. This means cleaning errors, filling gaps, and ensuring a uniform format. Feature engineering is also part of preprocessing, enhancing predictive capability by selecting relevant features.
• Data Analysis. Data is subjected to exploratory data analysis (EDA). For inventory management, EDA might reveal seasonal demand patterns, identify the impact of promotions, or highlight products with consistently high or low sales.
• Model Training. AI/ML models are trained on preprocessed data to learn patterns, relationships, and trends. For inventory management, models may include demand forecasting algorithms, dynamic pricing models, and inventory optimization techniques.
• Model and Feature Drift Monitoring. This is important for two reasons: One, the distribution of the data the model was trained on often changes over time, so if the model is not retrained regularly the forecast will be off; and two, macroeconomic conditions of unseen events like a global pandemic might completely change the feature distribution.
   

PG: What should grocery retailers consider when selecting a technology partner or solution?

SH: There are a few questions they should ask themselves:

Does the partner have grocery retail experience? 
Partnering with a vendor well-versed in grocery retail is essential for success. Data reveals that 70% to 80% of model development involves feature engineering. It’s crucial to collaborate with a solution provider who not only possesses industry expertise but also understands its intricate nuances.

Does the partner understand the problem you are looking to solve? 
Whether the challenge is increasing revenue, reducing waste, working more efficiently, or all of the above, the partner should be able to bring experience in solving that problem to help your organization.

Can the partner help in analyzing, cleansing and augmenting your data? 
It is essential to ensure that all data is accurate, complete, and representative of the business’s operations. The partner should also have access to additional data related seasonality, weather, product master, and other data that can assist in the modeling.

Does the solution fit your company workflows? 
Great insights are only powerful if used to drive change in a business. The solution should be user-friendly and intuitive for staff to navigate. Recommendations and outputs must be presented in a straightforward and easily understandable manner for the end user.