Deep Learning & AI Use Cases and Customer Success Stories

Evidence your sustainability efforts to shareholders, combat forced labour, and improve overall business performance with multi-tier supply chain visibility. Underpin reporting for the US Uyghur Forced Labour Prevention Act, UK Modern Slavery Act and Scope 3 carbon emissions with supply chain mapping and analytics. Use multi-tier supply chain visibility to pinpoint your exposure to geographic regions and individual suppliers. Identify relationships between organisations in your market, where you share common suppliers and how geographical footprints of supply chains compare.

Don’t miss the opportunity to gain deeper intelligence faster than peers using our AI engine and integrating it into your existing supply risk mapping solutions. Reduce supply chain disruptions by anticipating red flags originating from your sub-tiers and by identifying common suppliers below Tier 1 on whom you have a high dependency. The goal was to map the supply chain structure, understand how disruptions may cascade and impact this structure, and then use this knowledge to inject resilience after predicting hidden dependencies and supplier deliveries (see Figure 1).

Business process management and use of data

Foundation models are worth considering as a separate element of an AI supply chain, as they can make it harder for regulators to assign responsibilities, and more challenging for sectoral regulators to identify the boundaries of their remit. The EU’s AI Act will significantly rely on the production of technical standards for AI systems by bodies such as CEN and CENELEC. To ensure effective regulation, regulators and policymakers will need to incentivise transparency and information flow across the supply chain.

Products fitted with GPS trackers can communicate to the manufacturer and customer, allowing its location to be pinpointed at any time – and even allowing for other actions to be taken in case of delays. One company making waves in this space is sennder, a logistics company that is digitalising and automating the entire delivery process. Cloud technology refers to data being stored on remote servers via the internet rather than, say, on your computer or within your company’s building. Cloud platforms means that companies can outsource this part of their operations – and have the data visible across various locales. However, as with the IoT, this can introduce security issues – which is critical to consider given the jump in cybercrime since the pandemic began. Another example of AI in supply chain management is inventory intelligence where AI can balance inventory more accurately to reduce stockouts, and improve customer satisfaction and loyalty.

Strive to create traceability, accountability and buy-in

So where we used to have manual processes for registration of documents, checking documents, processing of documents, pay, accept, refuse, we’ve now fully automated those steps. So, actually the ultimate goal of using this technology is to leverage technology to optimise business processes and to improve customer service. AiCure uses AI to monitor patient medication adherence and modify dosage according to patient input during clinical trials. They use machine learning to predict how patients will respond to medication and track changes in patient health and response. This enables them to optimize dosage to enhance efficacy while reducing undesirable side effects, resulting in a more comprehensive understanding of the patient’s treatment experience.

[87] Joanna J Bryson, ‘The Past Decade and Future of AI’s Impact on Society’, Towards a New Enlightenment? [81] Martijn Schoonewille and others, ‘Introduction New Algorithm Regulator and Implications for Financial Sector’ Lexology (5 January 2023) accessed 20 January 2023. [67] Noam Kolt, ‘Algorithmic Black Swans’ (2023) 101 Washington University Law Review 31 accessed 10 March 2023. [58] Central Digital and Data Office and Centre for Data Ethics and Innovation, ‘Algorithmic Transparency Recording Standard Hub’ (GOV.UK, 5 January 2023) accessed 22 March 2023. [57] Alex Godson, ‘Nine Cities Set Standards for the Transparent Use of Artificial Intelligence’ (Eurocities, 19 January 2023) accessed 21 March 2023.

Ebook: O’Reilly: “Machine Learning for High-Risk Applications: Techniques for Responsible AI”

’ If they produce these products, maybe their models are compatible, so they supply the same OEMs. It’s also important to consider how data from different sources can be integrated to provide a dynamic overview. In this article we look at some of the top use-cases for artificial intelligence/machine learning in the Consumer Goods& Retail industries, and how to identify use-cases within an organisation. Transportation costs typically make up a significant portion of total supply chain costs, with key factors being drivers and fuel.

It also has the ability to self-learn from user actions and automatically execute corrective measures. In conclusion, Artificial Intelligence has become an essential tool for businesses to maximize the efficiency of their supply chain management. AI can quickly analyze large amounts of data, automate tasks, forecast demand, optimize routes, manage inventory, reduce costs, and help with worker shortage solutions.

At Acuvate, we can help you streamline sales and operations with the Microsoft Dynamics 365 Supply Chain Management module. These systems can handle more complex queries, learn from each interaction, and even handle multilingual support seamlessly. This response offers a lens into the complementary roles that different AI systems can play within the logistics ecosystem.

For this we used our AI consulting army of data scientists and developers as well as our AI platform to deliver this solution. The most common blood products have a short shelf life, in fact, platelets only last 7 days. Hospitals need a stock of blood in different blood types, antigens, collection methods and more, so they can meet the patients’ needs and ultimately save lives.

By identifying these biomarkers, Foundation Medicine is working towards improving cancer treatment outcomes by developing more targeted and effective therapies tailored to individual patients. To be valuable in the supply chain, AI should have access to real-time data and external data, it should be able to support the ultimate goal irrespective of the constraints, and all engines should be highly scalable, autonomous, and https://www.metadialog.com/ assist decision making. The integration and consolidation of different data sources is obviously a challenge for many organizations that lack a proper data strategy. Traditional methods have typically relied on historical sales data, using statistical models to extrapolate this data into the future. These models, such as time series analysis and causal models, have been the mainstay of demand forecasting for many years.

These threats can include everything from phishing attacks to ransomware and can have serious implications for your business. By leveraging the power of AI, you can stay ahead of potential issues and ensure your business runs smoothly at all times. supply chain ai use cases However, today at Manhattan, this is not the case, and ML and AI are both very much science fact, rather than science fiction, with many of our ML/AI solutions already delivering demonstrable and positive impacts for customers all over the globe.

FourKites and Sony launch data partnership for supply chain monitoring

Many companies or public sector bodies deploying AI systems will, however, need information about the practices and policies behind its development from further up the supply chain to comply with their legal responsibilities. When issues are spotted, they will also need to have mechanisms in place to communicate those problems back up the supply chain to the supplier who is best placed to fix the problems. In this explainer we use the term ‘foundation models’ – which are also known as ‘general-purpose AI’ or ‘GPAI’.

• Conversely, if they had legal authority to do so, regulators could place the onus on the upstream code developer.
• According to new data from analysts Retail Systems Research (RSR), the most successful retailers are recognising the role of next-generation technologies, such as digital twins, artificial intelligence (AI) and machine learning, to stay ahead of the game.
• Nevertheless, procurement teams across industries have been hesitant to adopt large language AI models in their mainstream processes.
• Word is getting out – one report predicts that AI software will be worth more than $17 billion by 2028, citing the fact that AI-enabled supply chains are 67% more effective than those that don’t use AI.
• Don’t let your company’s hard-earned reputational capital be lost due to risks hidden within your supply chain.
• This ensures that you have enough inventory on hand during peak seasons while minimising inventory levels during slower periods.

[3] Department for Science, Innovation and Technology, ‘A Pro-Innovation Approach to AI Regulation’ accessed 15 May 2023. Today’s executives need to be prepared to invest in AI for more than a few months’ worth of quick fix. It has to be part of a mindset where forward-thinking leaders want to embed the long-term benefits of modern technology into their business.

We have 20 years of experience in building innovative and industry-specific software products our clients are truly proud of. Greater usage would also open up a massive source of data, which could pave the way for more customised tariffs and more efficient supply. Making the most of supply chain and production opportunities requires all parties to have the necessary technology and be ready to collaborate. Only the biggest and best-resourced suppliers and manufacturers are up to speed at present.

The customer has an invaluable input to Route and Fleet planning, whilst having access to data. Trying to resolve today’s complex retail challenges is more difficult if retailers focus on siloed capabilities. Delivering a connected commerce experience from browse to fulfilment requires an optimised customer offer of localised assortment, cognitive inventory management and demand-aware pricing, delivered via an optimised operations network designed to lower the cost to serve. Blue Yonder’s Commerce and Order Management (OMS) microservice solutions supply chain ai use cases redefine how commerce happens – delivering meaningful customer experiences and removing lengthy upgrades and technical obstacles that get in the way of business transformation. The client is a leading provider of supply chain consulting, software and fourth-party logistics services. Aiming to bring more business value to their clients, they strive to optimize logistics execution, using Machine Learning for automation of exception prediction and data processing from different suppliers and thus facilitating the process of decision making.

This reduces cost and improves customer service by ensuring that deliveries are made on time. AI-enabled automated tools are an invaluable asset to efficient supply chain management. By automating time-consuming tasks such as inventory management, demand forecasting, and route optimization, AI-based applications can help businesses save time and money. For instance, bots enabled with computer vision and AI/ML can automate repetitive tasks in inventory management, such as scanning inventory in real-time. The current challenges of pharmaceutical supply chains include issues such as a lack of transparency, inefficient inventory management, and a fragmented distribution network.

Additionally, AI can assist in the virtual prototyping of medical devices, allowing developers to test and refine designs in a digital environment, reducing the cost and time required for physical prototyping. By leveraging AI to optimize the product design, companies can accelerate the medical device development process, reduce costs, and bring better products to market faster. Supply chains have been a prime area for the application of AI, due to the vast amounts of critical business data and processes involved. Supply chains have evolved over the years, with emerging technologies and innovations that enable businesses to optimize their operations, reduce costs, and improve customer satisfaction. Yet, while statistical models have been used in processes such as inventory management, forecasting, production planning, and scheduling, there hasn’t been a significant shift in the industry beyond improving algorithms.