{"id":18450,"date":"2020-07-08T09:00:57","date_gmt":"2020-07-08T07:00:57","guid":{"rendered":"https:\/\/www.specpage.com\/?p=18450"},"modified":"2020-07-09T09:57:48","modified_gmt":"2020-07-09T07:57:48","slug":"supply-chain-risk-management-ai","status":"publish","type":"post","link":"https:\/\/www.specpage.com\/supply-chain-risk-management-ai\/","title":{"rendered":"Powering Supply Chain Risk Management with AI"},"content":{"rendered":"
Move over chat bots. AI has countless applications besides taking humans out of the equation with sales and marketing conversations. While ecommerce is getting all of the AI<\/a> attention from tech media, the food and beverage industry is quietly employing more AI every month: more applications, more use cases, more manufacturers and brands jumping on board, more startups entering the scene.<\/p>\n As we\u2019ve covered before, there are many different applications of AI<\/a> within the food and beverage industry:<\/p>\n In this post, we\u2019re going to dive deeper into the application of AI for risk management<\/a> of the supply chain, specifically.<\/p>\n The supply chain refers to the entire network that comes together to produce a product: not just the supplier companies and the ingredients they provide, but also the systems and processes utilized by the manufacturer, and the people who work in supply chain management.<\/p>\n In all, the supply chain is commonly understood to involve and include:<\/p>\n The supply chain primarily refers to the individual network that comes together to create a single product, but when we talk about the global supply chain<\/a>, things get even more complicated, because we\u2019re talking about the world\u2019s food supply and how that gets affected.<\/p>\n In essence, a company needs to not only protect their own profits and prevent product waste, but should also ensure the minimal amount of waste to the food supply for the sake of consumers, who deserve to buy the products they need at their normal prices \u2014 not inflated prices caused by negligence. This is especially true during times of global hardship.<\/p>\n Risk management for the supply chain means the practices and systems put in place to reduce the risk of issues, failures, and waste in the supply chain.<\/p>\n All tiers and elements of the supply chain (supplier interactions, port and delivery issues, completed product delivery) should be taken care of.<\/p>\n This means that a cohesive supply chain risk management strategy doesn\u2019t only take into account what the supplier is in control of (delivering quality ingredients on time), but also factors that the supplier isn\u2019t in control of, such as delays due to inclement weather, strikes, or shutdowns.<\/p>\n For that reason, supply chain risk management is extraordinarily complicated. Fortunately, AI is growing in its capacity to help, and its utilization.<\/p>\n So, how can AI actually make risk management a little bit easy to achieve? Let\u2019s explore. But first, a few quick definitions of the underlying technology that is being used by software providers and leading manufacturers.<\/p>\n Both types of technology are being utilized by food and beverage companies to improve their capacity for risk management of the supply chain, and it\u2019s important to remember that AI includes machine learning as a subset.<\/p>\n The most common way that AI is currently being used in supply chain risk management is in the planning phase.<\/p>\n AI can help make decisions on what to order and when based on:<\/p>\n AI can help speed up the human calculations and decision making process so that there\u2019s no guessing when it comes to supply chain planning.<\/p>\n AI can also help detect potential risks to ingredients and products that human workers might not yet be aware of. For example, if there is a known recall of lettuce in a certain area, the AI platform can alert supply chain managers to check on ingredients coming from that area and determine if any other product might be affected.<\/p>\n When there are known issues with the supply chain, AI can help dampen the fallout. With product recalls costing hundreds of thousands to millions of dollars, it\u2019s important to prioritize public safety while also recalling the minimal amount of product as possible.<\/p>\n AI can accurate make decisions on what items to recall based on where they were manufactured and with what exact ingredients (from which suppliers), so that less product has to go to waste. By recalling individual products and not entire product lines, companies can also hope to experience less consumer backlash because they can speak with the right distributors directly, instead of announcing the issue to all distributors.<\/p>\n AI can also be used to predict issues with delivery. This could be for deliveries from a supplier to a manufacturer, and also from a manufacturer to a warehouse, distributor, or retailer. The benefit of predicting delivery disruptions is simply optimizing the supply chain. How can you prevent holdups? Do alternative plans need to be made? How can free up space if backups are causing issues? None of these questions can be answered in a timely manner if you\u2019re not able to predict disruptions before they occur.<\/p>\n \u201cThe resurgence of Artificial Intelligence (AI) has led to the investigation of machine learning techniques and their applicability in supply chain risk management. However, most works focus on prediction performance and neglect the importance of interpretability so that results can be understood by supply chain practitioners, helping them make decisions that can mitigate or prevent risks from occurring,\u201d write scholars George Baryannis, SamirDani, Grigoris Antoniou<\/a>.<\/p>\n In other words, current development work in AI for the supply chain is focusing too heavily on predictions and not enough on utilization or interpretation.<\/p>\n This is why the next frontier of AI (and probably the hardest and most important) isn\u2019t about the machine learning algorithms or predictions, but rather about the synergy between the technology and AI.<\/p>\n Making AI more useful is already growing in many industries, such as marketing, ecommerce, and retail, but it is still lagging behind in the food and beverage industry.<\/p>\n For the foreseeable decade, there will be huge growth potential with software companies that use AI to produce insights that are easy to interpret and act upon.<\/strong><\/p>\n The global market for artificial intelligence for the supply chain<\/a> is expected to grow 45.3% from 2019 to an estimated $21.8 billion by 2027.<\/p>\n There are already some AI startups and big players on the market that you can explore to learn more about your options for implementing AI at your company.<\/strong><\/p>\n The supply chain will always be an extraordinarily complex network to perform risk management on. With the help of AI, risk management professionals can come to important decisions faster.<\/p>\n\n
What is the supply chain?<\/h2>\n
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What is risk management for the supply chain?<\/h2>\n
How does AI improve risk management for the supply chain?<\/h2>\n
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Below are the top ways that AI is powering risk management:<\/h2>\n
Supply chain planning<\/h3>\n
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Detecting potential risks<\/h3>\n
More accurately identifying at-risk products<\/h3>\n
Predictions on delivery disruptions<\/h3>\n
Where AI for supply chain risk management needs improvement<\/h2>\n
What good is AI if supply chain professionals can\u2019t utilize it?<\/h3>\n
Technology that helps you utilize AI for supply chain management<\/h2>\n
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