Tesser Insights Case Study: Optimizing Organic Food Production and Distribution for a Leading Producer

Client: A large organic food producer and distributor with a wide variety of products and customers, including online stores, grocery stores, restaurants, and individual consumers.



Challenge: The client was facing a challenge in predicting demand for their products, resulting in stockouts and excess inventory. They had a large amount of data from sales, shipments, and production, but were struggling to extract insights from it and use it to make informed demand and supply planning decisions.

Solution:

Gathering and Analyzing Data: Tesser Insights worked with the client to gather data from various sources, including sales data from different regions, product shipment data, and production data. They performed data analysis to identify patterns and correlations in the data, using techniques such as time-series analysis, regression analysis, and machine learning.

Developing Predictive Models: Based on the insights gained from the data analysis, Tesser Insights developed predictive models to forecast demand for the client's products. The models included time-series analysis using ARIMA models, and machine learning models such as random forest, decision trees, and neural networks. Tesser Insights also developed supply planning models to optimize production and inventory levels.

Validating and Refining Models: Tesser Insights worked with the client to validate the accuracy and effectiveness of the predictive models. They tested the models against new data and compared them to other methods of analysis. Based on feedback and new data, Tesser Insights refined the models to improve their accuracy and effectiveness.

Providing Actionable Insights: After the predictive models were validated and refined, Tesser Insights provided the client with actionable insights that could be used to inform their demand and supply planning decisions. They recommended specific production schedules, identified products with high demand, and provided early warning for potential stockouts or excess inventory.

Continuously Monitoring and Updating Models: Tesser Insights continued to monitor the performance of the predictive models and updated them as needed based on new data and changing business needs. This ensured that the insights provided remained accurate and relevant over time.

Results: Using the insights provided by Tesser Insights, the client was able to optimize their production and inventory levels, reduce stockouts, and increase overall sales and profitability. They were also able to identify potential issues before they became major problems, allowing them to address them proactively.

Conclusion: Through the use of advanced analytics and predictive modeling, Tesser Insights helped the client gain insights from their data and make more informed demand and supply planning decisions. By following the steps outlined in this example, Tesser Insights was able to help their client reduce stockouts, optimize production and inventory levels, and increase sales and profitability. The key to success was the collaboration between Tesser Insights and the client, with both parties working together to gather and analyze data, develop and validate predictive models, and apply the insights gained to real-world demand and supply planning decisions. Continuous monitoring and updating of the models also ensured that the insights remained accurate and effective over time. This example highlights the value of using advanced analytics and predictive modeling to gain insights from data and make informed business decisions.