Seeing the future in an uncertain world: how AI supports the food supply chain
2020 showed us that successful companies are those that can see into the future. When markets are challenged because of disruptions like the Covid-19 pandemic, or other economic, geopolitical, environmental, or social reasons, the companies that thrive are the ones that build resilience and agility and upgrade their supply chains.
What if you were able to predict not only what customers want to buy in six months, but how much of it and from which retailers? Or which of your customers is most likely to switch to another supplier? You can do this today with predictive modeling.
By looking for patterns in the information that businesses already have, companies can turn that data into insights they can use to make better decisions and improve outcomes across their business. Some of the data used for predictive modeling includes pricing, sales, and OTIF/OS&D data. By adding real-time data collection of temperature and humidity via IoT sensors at the case or pallet level, companies can laser in on preventing food waste and loss, which currently totals over $35 billion worth of perishable food products annually.
Adding other publicly-available data can turbocharge models that are used to examine datasets for underlying patterns or causes, and predict outcomes. Weather, traffic, online pricing adjustments dependent upon product demand, and even price shifts for ride shares during peak hours and airlines adjusting pricing during certain days and seasons, all provide data for more accurate forecasting.
Predictive modeling used to be out of reach for most organizations, but advances in technologies like machine learning and AI have made it more accessible to small to midsize businesses. Today fewer than a third of U.S. businesses use predictive analytics, but the global market is expected to triple to about USD 21.5 billion by 2025, from $3.49 billion in 2016. More and more companies will be able to predict the future, based on good data.
Feeling good about AI-powered decisions
Within the food industry, predictive analytics is used to help with better supply chain visibility. It starts from the harvest period and reaches all the way to food retail stores. Predictive modeling is used to track and manage shipments and lead times to predict future disruptions before they happen and help logistics companies proactively manage their operations efficiently. It can help with dynamic pricing based on forecasting demand of product, seasonality, and what the customer is willing to pay, or online pricing adjustment dependent upon product demand.
Unlike historical prescriptive analytics that only looks at the past data, our supply chain predictive modeling allows supply chain companies to not only look at the past but anticipate and prepare for the future, utilizing data from the latest IoT sensors, cutting edge servers, and multi-cloud infrastructure to provide a comprehensive decision-making engine across the food supply chain.
Removing error and effort — not humans
Predictive modeling will not replace human beings in making the decision but it will reduce human error with collecting and inputting data. Network-connected sensors, powered by technologies like Bluetooth, 5G networks, and public-ledger data, can collect and share real-time temperature and location data all along the supply chain. Based on the data available, companies like Transparent Path will provide options that can be leveraged for making the most optimal decisions possible.
Shared data is key to accurate forecasting, essential for upstream supply-chain planning and execution, and reducing food and financial waste. Without alignment and visibility, food producers and processors have to generate their own sightline-limited forecasts based only on past data and the real or perceived availability of inputs of immediate downstream partners.
AI works best when combined with other technologies
In summary, four data improvements would create vast efficiencies that would reduce waste as well as billions of dollars in financial, reputational, and health risk.
- Continuously-connected IoT sensors can gather more granular food data, more cheaply. They can also create opportunities to solve for risk in real time.
- Edge computing can not only speed AI applications by spreading processing tasks across a variety of devices — it can also reduce cloud costs.
- A shared data ecosystem can create collaboration among partners to reduce the number of rejected loads.
- Blockchain-based data security (in which we encrypt data from our sensors) can help prevent fraud.
- Predictive and prescriptive modeling allows us to anticipate future incidents before they occur and can suggest operational improvements to better run your business.
Transparent Path can put these ideas into action for their customers, helping food companies see into the future. Farmers could share temperature and humidity data and predict a “freshness score” for their retail partners to get a better price for their product. Weather data can help plan delivery dates and routes for logistics companies to get their goods to the appropriate destination in a timely manner and reduce OS&D losses. Predict the future, and also protect the bottom line and company reputation, with predictive modeling.
About Transparent Path
Transparent Path has a big vision — to reduce food waste through supply chain improvements. We are one of the only companies to combine IoT, AI, and edge technologies, creating real-time visibility into the food supply chain for producers, processors, logistics, retailers, and consumers. Our goal is to use real-time data to create a more agile, more resilient, and more certain supply chain for perishables.
Invest in the future of the perishable supply chain! Learn more at NetCapital.
Originally published at https://www.linkedin.com.
