After leaving the Metaverse funeral, everyone jumped on the AI party bus. The confidence in artificial intelligence is now stronger than ever. And the renewed zeal for enhancing business efficiency and productivity motivates more companies to implement AI solutions.
But as we’ve learned from the past years – there’s no magic potion to make your venture an almighty entity. And AI is no exception. Yet, unlike the Metaverse, AI’s active development gives the technology a solid base for advancement. Plus, its current benefits are acclaimed in all industries.
Still, let’s avoid getting ahead of ourselves and planning for a utopian world. Instead, we shall focus on where AI stands now and what it means for the backbone of any business – the supply chain.
AI is the hot ticket in supply chain management (SCM). Gartner predicts that over 75% of SCM software providers will implement AI solutions into their products by 2026. Why is that? Because if you do not use AI tech, you are missing out.
AI enables live tracking and monitoring of inventory, shipments, and production processes. The resultant visibility helps:
AI analyzes vast and multifaceted data, e.g., historical trends and market conditions, to provide actionable insights. This information helps:
Early AI adoption led to a 35% decrease in inventory levels, according to McKinsey. AI optimizes inventory by considering demand patterns, lead times, supply constraints, etc. So, with AI managing information mixtures, companies can:
By leveraging advanced statistical models and ML techniques, AI examines historical data and generates accurate demand forecasts. Meaning that with AI, organizations can:
Well-trained AI solutions can monitor supply chain processes and detect anomalies/potential issues in real-time. Also, the technology can alert stakeholders to any deviations. Thus resulting in:
AI algorithms optimize transportation routes by reviewing traffic conditions, fuel costs, and delivery priorities. This leads to:
AI can predict equipment failures and maintenance needs upon integration with sensor data and maintenance records. From McKinsey’s survey, 61% of executives from manufacturing noted lowered costs due to AI solutions. So, by proactively scheduling upkeep, companies can:
AI assesses supplier performance data, quality records, and market intelligence to revamp supplier selection and management. Also, AI can locate potential risks in the supply chain, such as disruptions due to weather events or geopolitical factors. Such thorough and broad investigation helps:
AI systems can suggest optimization strategies by learning from data. Hence enabling organizations to refine their processes and drive performance upgrades. AI derives information from all corners of the supply chain, thus promptly identifying:
McKinsey’s report showed a 65% lift in service levels among early adopters of AI. Evidently, the technology enhances customer satisfaction and cuts the burden on customer service. For instance, AI-powered chatbots and virtual assistants can:
As noted by Forbes, 64% of companies see AI as a productivity boost. Mainly because it can automate repetitive and manual tasks, e.g., data entry, invoice processing, and order tracking. For businesses, this means:
AI-powered systems can help organizations reduce environmental impact. For example, they can:
AI solutions can drive business growth, enhance efficiency, and unlock untapped potential for many sectors. But it’s not the product that ensures triumph. It’s how companies use it. Let’s review some success stories, examining the wins of smart collaborations.
Orderly aids numerous businesses in managing inventory and ordering processes. Starbucks, Nestle, Morrisons, McCormick Schwartz, and many others are among their clients. The company’s solutions helped overcome the troubles of manual data entry, inaccurate inventory counts, and time-consuming ordering processes.
Vuealta aided Circle K in achieving an accurate and unified forecast to better predict stock levels. Circle K managed demand and supply forecasting with Excel and ERP solutions. And these often led to stress and disagreements across teams. With Vuealta, Circle K could save money by reducing inventory and using resources efficiently.
Bridgestone partnered with Taulia to advance sustainable practices. This partnership brought better visibility of the entire supply chain. Also, it applied a program to encourage its suppliers to adopt sustainable practices. And Bridgestone achieved a state-of-the-art supply chain that set a new standard for ESG solutions.
The UK Vaccine Taskforce (VTF) needed to implement a digital supply planning transformation to run over 600 supply and demand scenarios. Its collaboration with Efficio enabled the administration of over 170 million vaccines. Efficio’s expertise and embedding into the VTF allowed for a better understanding of barriers, risks, and opportunities to facilitate supply chain resilience.
Rosslyn worked with the University of Pittsburgh to provide spend analytics and procurement services. The joint effort resulted in significant cost savings, improved supplier relationships, and increased efficiency. With Rosslyn’s help, Pitt University could gain greater visibility into spending and make data-driven decisions to optimize its procurement processes.
Orbit Group engaged with Proactis to improve its supplier and contract management. Proactis’ tools helped Orbit Group manage suppliers and contracts consistently across locations. The solution allowed Orbit Group to streamline its sourcing events, reduce costs, and improve compliance. The partnership resulted in improved supplier relationships and increased efficiency in procurement.
IDS Group collaborated with B2BE to streamline supply chain processes. Process automation led to the efficient exchange of documents with trading partners. The collaboration optimized supply chain costs, coordinated goods movement, and enhanced performance. Thus bringing forth improved efficiency and effective supply chain management for IDS Group.
Lantmännen teamed up with Scanmarket to effectively structure supplier data. Scanmarket’s source-to-contract platform with built-in integrations helped overcome the challenge of efficient strategic sourcing. And their solution helped Lantmännen optimize its procurement processes and enhance supply chain management.
The aspects AI can help with are infinite. Mostly, they produce successful results. But crucial elements of success are the capabilities and “fit” of the solution. So before companies rush to apply AI, they should be well-informed about the upcoming challenges.
AI gave an edge to tech giants. And now, it’s also becoming a seemingly easy upgrade for SMEs and start-ups. Yet, when it comes to implementing AI solutions, it’s not all rainbows and ponies.
In 2018, Walmart had troubles with its AI-powered inventory management system. The algorithm could not accurately track inventory levels and monitor stock in a timely manner. As a result, some stores experienced stockouts, while others had excess inventory. This led to suboptimal supply chain performance and upset customers.
So when organizations decide to use available AI products or develop their own, they should consider potential issues.
With so many AI-based solutions, it can be difficult to choose the right one. Decision-makers should be familiar with possible options and fully understand the mission of AI in their firm.
AI requires large amounts of data to work effectively. And it can be challenging to create algorithms, prediction models, and insights analysis. To secure fruitful AI, businesses need proficient and experienced specialists, suitable budgets, and dedicated teams.
Certain machine learning techniques are easy to understand. But neural networks are more complex and “mysterious.” If companies don’t know how AI arrives at its decisions, it can be tricky to know when to trust the data or perform reviews.
Every process and change should involve the entire supply chain. Taking a holistic view of the supply chain is essential to achieve effective and sustainable optimization. Here, understanding how different processes and changes interact and affect each other may get complicated.
Supply chains are inherently cross-functional and cross-enterprise. In other words, the data needed to operate them is scattered among in/external partners. Companies attempting to implement AI in a fragmented fashion while ignoring the big picture will get poor results.
To work effectively, AI-based solutions need accurate data. If the data is false or incomplete, the AI system can’t make proper judgments. Making a high-quality data bank is an effortful task, which many may find demanding.
Software testing services are critical for AI systems’ accuracy, reliability, fairness, and robustness. But testing can be exacting due to the need for diverse test data, the black box nature of AI algorithms, and evolving models. Also, there is a lack of specialized QA resources and skilled professionals.
Essentially, successful AI implementation comes down to niche expertise and talented specialists. For example, the most common uses of AI are automation of IT/business processes and security and threat detection, as per IBM. To use AI for such solutions, companies need people with versatile skills encompassing:
The approach businesses take to set up AI implementation varies. But some universally beneficial insights help make the process easier.
To maximize AI perks, businesses need to overhaul their entire supply chain ecosystem. To be able to do that doesn’t mean relying on technology. Just the opposite – it’s all about people. Artificial intelligence is indeed a wonder. But professionals who can adapt, improve, and deliver are a much more valuable asset.
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