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Top 20 ways Artificial Intelligence is advancing life sciences

Blog AI Use-Cases in Consumer Goods & Retail

supply chain ai use cases

The field of health economics is complex, and companies need to navigate regulations and pricing negotiations with payers and government agencies, while considering factors such as research and development costs, production expenses, and market demand. AI can predict the value of a drug and determine the optimal pricing strategy by analysing vast amounts of data from various sources, including clinical trials, real-world evidence, and market trends. Machine learning algorithms can identify patterns and relationships in this data, allowing pharmaceutical companies to estimate the potential value of a drug and its impact on patient outcomes. AI can also simulate different pricing scenarios and predict their financial implications, enabling companies to make data-driven decisions on pricing strategy.

Artificial intelligence has the potential to transform many aspects of these industries but initiating projects with a clear understanding of the costs and benefits will lead to more successful outcomes. Acuvate Software is a global player in next-generation digital services and consulting with 14+ years of experience optimizing the supply chain and improving business efficiencies and revenue for numerous enterprises globally. As a Microsoft Gold Partner, we leverage all things Microsoft to build enterprise apps that support intelligent analysis, collaboration, and orchestration of information, to redefine sales, service, mobility, and experience. Owing to the fast-moving nature of supply chain management tasks, organizations must move processes like manufacturing execution systems, advanced warehousing, and other operations to the mobile platform.

Don’t underestimate the power of descriptive analytics to lead to more realistic solutions

As supply chain complexity grows, companies are using next-generation AI to gain a competitive edge and remain profitable. AI is proving to be a game-changer for businesses, whether they’ve already embarked on a digital transformation journey or are considering doing so. Let’s explore some of the cutting-edge AI use cases in supply chain management that can deliver immediate value without undertaking costly transformation initiatives. Drones and autonomous vehicles can help you streamline your supply chain operations, reduce logistics costs, and improve delivery times. With AI, you can make better, data-driven decisions that ultimately improve your bottom line. Plus, by automating certain aspects of supply chain management, you can free up time to focus on other important areas of your business.

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Monitor and track inventory to ensure that new shipments are ordered at the right time, supporting just in time production. Track your shipments across the whole lifecycle, including freight costings and warehouse management, and monitor your historical data to enable smart supply and demand based on trends rather than guesswork. Automate workflows for purchase order management and order prioritisation, reducing the impact on internal resources while creating a culture of operational resilience. Intelligent inventory visibility is also revolutionizing the way businesses search and view their stocks and products, empowering users with unparalleled accessibility and efficiency. The power of AI enables users to swiftly ascertain stock levels and product availability by merely typing their inquiries in natural language, similar to chatting with a friend. Whether it’s a query about products nearing expiration or the availability of limited-edition items across various regions, the AI assistant promptly delivers the desired results.

Supply Chain App

AI-powered systems can flag potential compliance issues, such as incomplete or inaccurate documentation, and provide alerts to compliance teams for further investigation. This can help companies to proactively identify and address compliance issues, reducing the risk of fines, legal action, and damage to the company’s reputation. There are several supply chain ai use cases companies using AI in medical devices for patient monitoring, including Medtronic, which analyses data from its insertable cardiac monitors. The company’s AI algorithms are designed to detect changes in a patient’s condition and provide timely alerts to healthcare providers, allowing them to take necessary action before a serious issue arises.

As soon as there is a material change that means the conditions no longer match those the model was trained under, companies can make really bad decisions really quickly. This is why all modelling at scale requires a data supply chain to ensure the data is real, valid, and formatted and structured correctly, as well as constant checking of whether circumstances have changed. QOMPLX works with enterprises to get their data supply chain in order and to integrate their many point solutions. Apply AI to the high volumes of data generated within supply chains to allow for analysis and insights that identify trends on where efficiencies can be realised.


This scenario implies that the company will have the staff needed to monitor and mitigate resulting risks, if those staff are retained beyond initial deployment (and this monitoring and evaluation is not outsourced to a third party). Figure 2 shows an example supply chain for a medical diagnostics system, where the developer and its hospital customers both process patients’ personal data to train and apply cancer detection models and voice recognition models. Depending on the supply chain, some companies (perhaps UK small businesses) supplying services directly to customers will not have the power, access or capability to address or mitigate all risks or harms that may arise. Creating an artificial intelligence (AI) system is a collaborative effort that involves many actors and sources of knowledge.

  • And Unicsoft helps to transform PoCs with the potential of developing into a solution as a feature of the existing product.
  • The conventional methods of demand forecasting, while having served their purpose in the past, are increasingly showing their limitations in the face of a rapidly evolving market landscape.
  • Like any technology, though, it continues to grow, and that can lead to more opportunities.
  • By using AI-enabled technology, businesses can reduce their logistics costs significantly.
  • The EU’s AI Act will significantly rely on the production of technical standards for AI systems by bodies such as CEN and CENELEC.

But there is little doubt that in the life sciences industry, AI will help to save many lives and reduce patient suffering. Sentispec’s Access AI platform uses visual data from video cameras to deliver timely insights into distinct opportunities for supply chain efficiencies and logistics load optimisations. Our cameras and AI algorithms can be applied to every critical point where product is planned, handled or stored in warehouses and depots.

Predictive Monitoring & Maintenance

Generating more accurate ETA predictions for a particular journey, based on internal and external data, to optimise delivery journeys and supply chain operations. Since 2018, Aramex has partnered with Inawisdom to unlock their data with Machine Learning, with a focus on last-mile delivery. ML models helped improve the accuracy of transit-time predictions by 74%, improving customer experience and reducing strain on the contact centre. Furthermore, procurement professionals also have security concerns regarding large language AI models. AI systems can be vulnerable to cyberattacks, which could put an organisation’s sensitive procurement data at risk of breach.

supply chain ai use cases

By harnessing the power of artificial intelligence and machine learning, businesses can overcome the limitations of traditional methods and navigate the complexities of demand forecasting with greater accuracy and efficiency. The Supply Chain Modeler is a powerful tool that allows businesses supply chain ai use cases to accurately predict operational results and compile logistics data by running various scenarios. With its predictive analytics algorithms, the Modeler can help supply chain companies make real-time decisions and leaner supply chains by forecasting inventory needs and rebalancing resources.

Recently, I was a guest speaker at The Data & Logistics Event hosted by the Transport Exchange Group. The topic I discussed was the importance of AI and Data Science in the Logistics and Supply Chain Sector and how it can help improve operations and efficiency. We are specialists in pallet racking, picking shelves, automated warehouses and logistics software. According to a study by Gartner — a global leader in market research and consulting — centred on around 200 IT and business professionals, 24% of organisations surveyed increased their investments in AI, while 42% kept them unchanged since the start of COVID-19. [70] Red Hat, ‘Siemens Improves Uptime and Security with Red Hat OpenShift’ (15 July 2022) accessed 22 March 2023. [27] Dylan Patel and Afzal Ahmad, ‘Google “We Have No Moat, And Neither Does OpenAI”’ (semianalysis, 4 May 2023) accessed 19 May 2023.

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How Zara uses AI in supply chain?

Zara utilizes advanced technology, such as RFID (Radio-Frequency Identification) tagging and real-time data analysis to optimize its supply chain. By tagging each item of clothing with an RFID chip, Zara is able to track inventory levels in real time and quickly respond to changes in demand.

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