The role of AI in supply chain optimisation and transformation

[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 supply chain ai use cases 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.

This rings true if one considers the 6 sub-streams of AI and its unlimited potency in maximising the potential of human endeavours. To link our demand forecasts to stock, we also needed to predict the supply of platelets. Supply comes from donations from the brilliant British public, which works by appointment. So predicting the supply, with all these factors built into the AI model is as important as understanding the demand. “We have had a positive experience with the data. The initial filter was helpful – from other data sources we had huge amounts of data. I really liked the way the data was structured – helpful and easy to merge with the way we structure our data.”

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By using AI to optimise your inventory, you can make more informed decisions about how much stock to hold and when to replenish it. AI algorithms can analyse data from across your supply chain, including sales data, production schedules, and shipping times, to determine the optimal inventory levels for your business. But how can we implement it to make our production and supply chain processes increasingly more efficient?

• Autonomous fleets would enable travellers to access the vehicle they need at that point, rather than having to make do with what they have or pay for insurance and maintenance on a car that sits in the drive for much of the time.
• This, combined with supply chain disruption and concerns around sustainability, means that optimising processes and operating more efficiently is key.
• Actors within each supply chain will have differing but overlapping obligations to assess and mitigate these risks, and some will have more responsibility than others.
• AI-powered route optimization software can analyze this data in real time and provide businesses with timely insights for cost savings and improved service quality.

[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.

Examples of different kinds of AI supply chains

Inaccurate demand forecasts can lead to overstocking or understocking, which have profound implications. Overstocking ties up capital in unsold inventory and increases storage costs, while understocking can lead to missed sales opportunities and damage customer relationships. Moreover, these challenges are not isolated incidents but can ripple effects throughout the supply chain. For instance, inaccurate demand forecasts can disrupt production schedules, leading to inefficiencies and increased costs. They can also impact supplier relationships, as unexpected changes in order volume can strain these partnerships. Additionally, AI-powered demand forecasting can reduce supply chain errors and minimize monetary losses.

With AI-powered demand forecasting tools, you can incorporate a wider range of data sources and generate more accurate predictions. This allows you to adjust your inventory levels, production schedules, and shipping logistics to meet demand in real time, reducing the risk of overstocking or inventory shortages. In short, AI-driven demand forecasting represents a significant step forward in supply chain management.

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’ 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.

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’.

Supply chain management is another aspect of the retail business that can be transformed and improved using BI technology. Ultimately depending on their business requirements, retailers can use BI to measure customer satisfaction, track conversions, calculate LTV, or build customer decision trees (CDTs). Sephora, a French personal care and beauty products retailer, continuously monitors customers’ actions after browsing a particular product page on a website or mobile app. While the first allows for a deeper understanding of the patterns that influence a customer’s product choice, the second helps predict the next steps a retailer should take with the consumer to encourage them to buy more. In the simplest scenario, retailers can use BI to analyze the effectiveness of their marketing campaigns based on parameters such as traffic, number of website visitors, and sales volumes. With this data, Nike can predict consumer demand and determine how many products should be produced and delivered at particular locations.

With the sheer volume of medical data available, NLP can quickly analyse and extract relevant information, saving time and resources that would otherwise be spent manually analysing data. This can lead to faster and more accurate diagnoses, more effective treatments, and improved patient outcomes. Additionally, NLP can help researchers identify patterns and insights that may not have been apparent before, leading to new discoveries and breakthroughs in medical science. In addition, AI can help address specific challenges in the pharmaceutical supply chain, such as identifying the most efficient shipping routes to minimize transportation costs and detecting counterfeit drugs to prevent them from entering the supply chain. By leveraging AI technologies, pharmaceutical companies can improve the efficiency, accuracy, and safety of their supply chain operations, delivering better outcomes for patients.

Just like you wouldn’t use a hammer to drive a screw, or a rake to dig a hole, the same principle applies to data science tools as well. ML is a great development, but it’s important to be aware that there are many types of analytical problems to solve across such complicated and fluid environments as the supply chain, each one requiring a specialized scientific and approach to reach the best solution. Manhattan’s Demand Forecasting, with promotional planning, uses the most advanced data science and machine learning models so that you can extrapolate the value of your hidden data and create the most accurate forecasting models possible. And more importantly maybe, how does it actually work in the context of supply chain science? If AI is essentially the intelligence, ML is the implementation of the compute methods that support it. ML is the workhorse and enabler of AI through its algorithms which provide systems with the ability to automatically learn and improve from experience without being explicitly programmed.

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 https://www.metadialog.com/ 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.

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.

• Pharmacovigilance management company, MyMeds&Me, in partnership with conversational AI company, OpenDialog, has developed a chatbot, called Phoebe, that helps patients and healthcare professionals report adverse drug reactions in a conversational manner.
• Ability to get answers through AI forecast explainability and natural language querying will help demand planners breeze through their demand plan analysis, reducing the time needed for fine-tuning and adjusting demand plan from days to minutes.
• People often imagine that robotics are more of a futuristic possibility, but instead these are already changing industries across the world.
• It allows for end-to-end visibility, which can help organizations map the most cost-effective delivery routes, playing out a variety of scenarios that take into account possible weather problems or other disruptions.
• With the rise of eCommerce and online transactions, cybersecurity threats are becoming increasingly common in the retail supply chain.

While investment in AI may seem expensive now, PwC subject matter specialists anticipate that the costs will decline over the next ten years as the software becomes more commoditised. Eventually, we’ll move towards a free (or ‘freemium’ model) for simple activities, and a premium model for business-differentiating services. While the enabling technology is likely to be increasingly commoditised, the supply of data and how it’s used are set to become the primary asset. FourKites will reportedly offer data on shipments being stored or transported in real time, while the Visilion system is intended to provide information on the cargo’s location and condition.

Paige.ai’s forecasting capabilities have the potential to transform the field of healthcare by enabling earlier interventions and more personalized treatment plans for patients. In an increasingly complex and interconnected global marketplace, managing supply chain risks has become a top priority supply chain ai use cases for organizations across industries. Generative AI has revolutionized several industries enabling new possibilities and advancements. In the Banking & Financial Services (B&FS) sector, its algorithms are utilized for fraud detection, risk assessment, and personalized customer experiences.