Abstract
Food production is a complex process where uncertainty is very relevant (e.g. stochastic yield and demand, variability in raw materials and ingredients…), resulting in differences between planned production and actual output. These discrepancies have an economic cost for the company (e.g. waste disposal), as well as an environmental impact (food waste and increased carbon footprint). This research aims to develop tools based on data analytics to predict the magnitude of these discrepancies, improving enterprise profitability while, at the same time, reducing environmental impact aiding food waste management.A food company that produces liquid products based on fruits and vegetables was analyzed. Data was gathered on 1,795 batches, including the characteristics of the product (recipe, components used…) and the difference between the input and the output weight. Machine Learning (ML) algorithms were used to predict deviations in production, reducing uncertainties related to the amount of waste produced. The ML models had greater predictive capacity than a linear model with stepwise parameter selection. Then, uncertainty is included in the predictions using a normal distribution based on the residuals of the model. Furthermore, we also demonstrate that ML models can be used as a tool to identify possible production anomalies.This research shows innovative ways to deal with uncertainty in production planning using modern methods in the field of operation research. These tools improve classical methods and provide production managers with valuable information to assess the economic benefits of improved machinery or process controls. As a consequence, accurate predictive models can potentially improve the profitability of food companies, also reducing their environmental impact.
Highlights
Food waste is a global issue that has raised social and governmental awareness in the last years
Recent examples of the application of big data in food production include the use of radio-frequency identification (RFID) sensors to support food manufacturing [49,50,51], supply chain management [52,53]
In the case study analyzed in this research, we have shown how Machine Learning (ML) algorithms provide estimates of parameters of the production process that are more reliable than those obtained using classical statistical models
Summary
Food waste is a global issue that has raised social and governmental awareness in the last years. One third of food produced for human consumption worldwide is lost, equivalent to approximately 1300 million tons each year. This implies that the production of 30% of the agricultural surface of the planet (about 1400 million hectares) and 250 million m3 of water are wasted [1]. Food waste has a societal and environmental impact being an important contributor to climate change. For these reasons, governments and other agencies have dedicated efforts towards reducing the amount of food that is wasted worldwide
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
