AI for Supply Chain Management in 2023 – It’s All About How You Use It

AI Has Taken Supply Chain Management by Storm

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.

How Technology Is Changing Supply Chain Management

Real-Time Supply Chain Visibility

AI enables live tracking and monitoring of inventory, shipments, and production processes. The resultant visibility helps:

• Proactively identify bottlenecks.
• Optimize workflows.
• Make informed decisions to ensure efficient operations.

Enhanced Decision-Making

AI analyzes vast and multifaceted data, e.g., historical trends and market conditions, to provide actionable insights. This information helps:

• Come to decisions faster and confidently.
• Determine optimal inventory levels.
• Adjust production schedules.
• Select ideal transportation routes.

Efficient Inventory Management

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:

• Prevent stockouts and overstocking.
• Reduce holding costs.
• Improve overall inventory management efficiency.

Demand Forecasting Accuracy

By leveraging advanced statistical models and ML techniques, AI examines historical data and generates accurate demand forecasts. Meaning that with AI, organizations can:

• Optimize production plans.
• Align inventory levels.
• Effectively meet customer demands.

Proactive Issue Resolution

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:

• Proactivity.
• Minimized disruptions.
• Risk mitigation.

Optimal Routing and Logistics

AI algorithms optimize transportation routes by reviewing traffic conditions, fuel costs, and delivery priorities. This leads to:

• Efficient logistics operations.
• Reduced transportation time and expenses (up to 15%).
• Better customer service through timely deliveries.

Predictive Maintenance & Equipment Reliability

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:

• Prevent breakdowns.
• Reduce downtime.
• Increase assets’ reliability.

Streamlined Supplier Management

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:

• Identify reliable suppliers.
• Optimize sourcing decisions.
• Improve supplier relationships.

Continuous Process Improvement

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:

• Inefficiencies.
• Bottlenecks.
• Advancement areas.

Enhanced Customer Service

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:

• Provide personalized and quick customer support.
• Address inquiries.
• Resolves issues promptly.

Process Automation

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:

• Stronger focus on strategic activities.
• Elevated productivity.
• Cost savings.

Sustainability & Green Initiatives

AI-powered systems can help organizations reduce environmental impact. For example, they can:

• Optimize transportation routes to minimize fuel consumption.
• Identify opportunities for waste reduction.
• Support the adoption of renewable energy sources.

10 Trends Driving Supply Chain Transformation

AI Solutions for Businesses Advancement: Case Studies

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 Supports Hospitality and Restaurant Businesses’ Essentials

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 Refines a Retail Business’ Forecast System

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.

Taulia Drives Sustainability for an Automotive Company

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.

Efficio Secures Better Healthcare with a Government Ministry

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 Overhauls a University’s Spend-Categorization

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.

Proactis Upgrades Supplier Management for a Housing Company

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.

B2BE Advances a Logistics Business’ Efficiency

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.

Scanmarket Strengthens Procurement for an Agriculture Cooperative

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.

Revamping Your Supply Chain Management Software: Solutions for a Scaling Product

AI Implementation 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.

Crowded Solution Landscape

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.

Difficult Scalability

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.

Black Box vs Explainable AI

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.

Short-Sighted Optimization

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.

Compartmentalized AI

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.

Accuracy of Data

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.

Complex Testing

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.

Inside the Complex World of Business Software Testing

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:

• Machine learning.
• Data science.
• Programming.
• Domain-specific knowledge.
• Regulations and ethical considerations.
• Effective communication abilities.

Preparing for AI Implementation

The approach businesses take to set up AI implementation varies. But some universally beneficial insights help make the process easier.

Making the Right Choice

• Conduct thorough research and evaluation of available AI solutions.
• Define clear objectives and requirements for own SCM.
• Seek recommendations and insights from industry experts/consultants.

Ensuring Smooth Scaling

• Invest in robust data infrastructure and storage capabilities.
• Implement data collection and integration mechanisms.
• Leverage cloud-based platforms that provide scalable computing power and storage.

Securing AI Transparency

• Focus on using AI techniques that offer interpretability and transparency.
• Implement model explainability techniques.
• Develop guidelines and governance frameworks to decipher AI recommendations.

Safeguarding Comprehensive Change

• Take a holistic approach to optimization, considering E2E processes and correlations.
• Use AI solutions that optimize the entire supply chain rather than individual components.
• Incorporate scenario planning and what-if analysis to evaluate the impact of changes.

Integrating Platforms & Systems

• Establish data-sharing agreements and partnerships with internal and external stakeholders.
• Adopt standardized data formats and integration protocols to facilitate data interoperability.
• Invest in collaborative platforms that enable real-time data sharing and communication.

Protecting Data Quality

• Apply data governance practices to ensure data accuracy, completeness, and integrity.
• Regularly validate and clean data to minimize errors and inconsistencies.
• Implement data quality monitoring and feedback mechanisms to find/mend issues promptly.

Assuring Effective Testing

• Form a robust test data strategy involving diverse and accurate datasets to evaluate AI models.
• Collaborate with QA professionals to design meaningful test cases and address limitations or biases in AI solutions.
• Embrace a continuous learning and adaptive approach to monitor AI model performance, update test cases, and stay aligned with emerging technologies and regulatory requirements.

To Sum Up

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.