The global supply chain was disrupted in the last two years by the COVID-19 pandemic. The delay in the delivery of goods has pushed the industry to apply the latest technology worth multi-billion dollars to minimize the supply chain and logistic disruption. In the next five years, the market for new technology services targeted on supply chains may be worth more than $20 billion annually. According to tech research firm Gartner, by 2025, artificial intelligence and data science will be used in more than 80% of new supply chain applications.
Nowadays, achieving a supply chain that operates at maximum performance at all times is nearly impossible. High internal and external complexity is the source of this. Traditional techniques for forecasting and resupply are unable to keep up with the volume of data generated by IoT devices and other sources, as well as the huge volumes of factors that affect supply chain. As a result, just-in-time production frequently depends on a supply chain that, while highly successful under ideal circumstances, lacks flexibility, or the capacity to respond effectively and quickly to changes upstream or downstream. Supply chain professionals are beginning to recognize how machine learning-based techniques may improve forecasting accuracy and resupply. Flexibility should be improved while the bullwhip effect is reduced. Supply chain optimization powered by AI can adapt quickly to changes in the product mix or the distribution network brought on by unforeseen occurrences in addition to concentrating on performance in a specific scenario.
According to a McKinsey study, users of AI-enabled supply-chain management have reduced their logistical costs by 15%, improved their inventory levels by 35%, and improved their service levels by 65%. These are preliminary results; the supply chain's potential for AI may lie in its capability to generate more targeted, predictive results. AI is not a miracle cure. It cannot solve all the problems within supply chains, warns Jonathan Wray, co-founder of Aible. However, at this time, the most fruitful use of AI in the supply chain is "in prioritizing optimal operations and testing various scenarios to design the best course of action."
Here are some examples of data science applications in the supply chain:
Interos, an operational resilience company with headquarters in Washington, DC, uses artificial intelligence and machine learning to analyze supply chain risks like financial, cyber, and even geopolitical risk in order to minimize supply chain disruption. They keep track on 400 million businesses across the globe using machine learning, alerting their clients to natural disasters, hacking, and other potential disruptions in real time. Interos evaluated the effects of the invasion of 500 American businesses with direct supplier contacts in Ukraine before the Russia-Ukraine war broke out in February.
In recent years, Walmart supply chain efforts utilizing AI are shifting from just anticipating sales demand - how much will sell that is currently in the shops - to predicting consumer demand in terms of what the customer would actually want to buy. This is done by evaluating data from a variety of sources, including Google searches and Tik Tok social feeds. When they are out of stock, Walmart overcomes this challenge by using artificial intelligence (AI) for grocery substitution, and makes sure that customers receive the things they require even if their chosen brand is out of stock. Since the implementation of this unique technology, Walmart claims that customer acceptance of substitutions has improved to more than 95%.
As we know, the more products in a warehouse, the higher the inventory cost for the supply chain. Grocery store Migros is one of the companies investing in demand planning. Migros used an AI-based inventory management solution to enhance order fulfillment procedures. To ensure that popular goods are accessible for omnichannel purchases, Migros is using an algorithm that determines each store's likelihood of fulfilling an online order. The organization cut inventory days by 11% and boosted inventory availability by 1.7% after deploying AI.
Despite the potential advantages of using data science in the supply chain, many companies remain reluctant to digitalize their supply chains. One of the issues was the lack of trust regarding the supply chain data. The volume, accuracy, relevance, and timeliness of supply chain data, as well as the technologies used to collect, process, and manage it, are frequently brought up to IT departments and digital transformation teams in the companies. Stakeholders in supply chains, including manufacturers, suppliers, distributors, carriers, retailers, end-of-life processors, and others, are battling an overwhelming amount of siloed data and finding it difficult to comprehend the "nuts and bolts" of one another's respective business situations.
To solve this issue, the supply chain stakeholders need to access the right data that they will use and have confidence in its precision and recency. Then, in order to help avoid costly issues down the line, the supply chain requires technologies that can spot anomalies early on. The data needs to be scientifically credible, therefore the platforms for their data collection methodologies should be assessed. And lastly, supply chain needs an advanced cybersecurity framework. A comprehensive risk analysis of the supply chain solutions under consideration, together with continuous employee and stakeholder awareness initiatives, can help prevent the financial and legal impacts of cyber vulnerabilities.
We now understand that data science is essential to the supply chain and can boost business productivity and efficiency. In many industries, data science can be leveraged to lower costs and boost profits; we can help you with these services for your own company. Feel free to contact us; we'll be your partner in digital transformation and technology!
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