Infor (Sweden) AB
Climate love
Climate love
Sven Nilson
70 w
This is how retailers and manufacturers can reduce food waste. Fantastic opportunity.
About a third of all food produced is wasted. It is not sustainable. Not in a world where over 900 million people suffer from hunger. It is also not financially sustainable. But by optimizing the entire supply chain – from the farmer to the table – we can reduce both food waste and costs. The key is to use data in a smarter way with industry-specific cloud solutions. Cloud solutions enable transparency But to achieve this, all stakeholders in the supply chain need to work together and this is not possible without cloud solutions. - Today, many of the parts in the chain are digitized, but they are not connected to each other. By streamlining and optimizing every part of the supply chain - and connecting them in the cloud - we create transparency so that actors can share data and use data that would otherwise not be available, says Marcel Koks, expert in Food & Beverage at Infor. Read the entire article in Swedish or with Google Translate https://www.di.se/brandstudio/infor/sa-kan-handlare-och-tillverkare-minska-matsvinnet/ About Infor Infor is a global leader in business cloud software products for companies in industry specific markets. Infor builds complete industry suites in the cloud and efficiently deploys technology that puts the user experience first, leverages data science, and integrates easily into existing systems. Over 65,000 organizations worldwide rely on Infor to help overcome market disruptions and achieve business-wide digital transformation. https://www.infor.com/about
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There are many ways how a modern digital Enterprise Application Platform can contribute to food loss and waste reduction, generally by making the food supply chain more data driven. To name a few examples: - Use machine learning in forecasting, for instance to consider the weather forecast and other parameters, to make the forecast more accurate and responsive. This is especially important for fresh food products with a short shelf life. Demand sensing and extrapolation of demand within the day are critical as well. - Minimize product and ingredients going beyond the use by date with an optimized inventory and supply plan that considers shelf life. Maximize yield from ingredients in processing with machine learning. We have an example of a cheesemaker that uses this to maximize the kilograms of cheese produced from the raw milk. The machine learning model looks at many parameters, such as butterfat and protein contents in the raw milk, humidity, acidity, temperature, process duration, equipment settings, etc. to determine the optimal output. The model helps to adjust the process to the optimal output if yield is lower than it should be. View the video: https://www.infor.com/news/amalthea-uses-infor-integrated-ai-cheese-quality-yields. - Dynamic best before dates calculated by machine learning based on quality characteristics of ingredients and processing conditions. Important is also the monitoring of farming, transportation, storage and processing conditions, and the equipment state. These are only a few examples. I hope this helps and please let me know if you have any questions.
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60 w
This is a fantastic solution for tackling food waste!
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61 w
There are many ways how a modern digital Enterprise Application Platform can contribute to food loss and waste reduction, generally by making the food supply chain more data driven. To name a few examples: - Use machine learning in forecasting, for instance to consider the weather forecast and other parameters, to make the forecast more accurate and responsive. This is especially important for fresh food products with a short shelf life. Demand sensing and extrapolation of demand within the day are critical as well. - Minimize product and ingredients going beyond the use by date with an optimized inventory and supply plan that considers shelf life. Maximize yield from ingredients in processing with machine learning. We have an example of a cheesemaker that uses this to maximize the kilograms of cheese produced from the raw milk. The machine learning model looks at many parameters, such as butterfat and protein contents in the raw milk, humidity, acidity, temperature, process duration, equipment settings, etc. to determine the optimal output. The model helps to adjust the process to the optimal output if yield is lower than it should be. View the video: https://www.infor.com/news/amalthea-uses-infor-integrated-ai-cheese-quality-yields. - Dynamic best before dates calculated by machine learning based on quality characteristics of ingredients and processing conditions. Important is also the monitoring of farming, transportation, storage and processing conditions, and the equipment state. These are only a few examples. I hope this helps and please let me know if you have any questions.
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61 w
Dear Sven Nilson Thank you for getting your climate love to level 2! We have reached out to Infor (Sweden) AB and requested a response. I will keep you updated on any progress! /Adam We Don't Have Time
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61 w
Wonderful we don't need to waste any food while others are suffering on the same
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62 w
Food should not go to waste when we have other people starving.
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70 w
Using data in a smart way,will help alot.
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70 w
Interesting indeed! I was listening to a podcast this morning and this came up and glad to this review!!!
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61 w