Integrating AI and IoT for Enhanced OEM Supply Chain Efficiency and Transparency

Artificial intelligence (AI) has significantly reduced the workload for businesses while driving numerous technological advancements. Similarly, the Internet of Things (IoT)—a network of interconnected devices—has proven invaluable in managing big data. Although AI and IoT are distinct technologies with unique characteristics, AI systems can leverage IoT data to enhance their functionality.

Integrating AI and IoT into original equipment manufacturer (OEM) supply chains requires careful attention and sustainable strategies. OEMs are responsible for most of the components in a final product, though this responsibility varies by industry, product type, and agreements with brands. AI can now serve as the foundation for improved functionality within these supply chains.

However, before we explore the integration of AI and IoT in OEM supply chains, we must first examine the current state of the OEM industry.

Common Challenges of OEM Supply Chain Industry

Supply Chain Complexity and Globalization

OEM supply chains are inherently complex, involving multiple tiers of suppliers, manufacturers, and logistics providers. The multitude of services in a single project increases the risk of complications and mismanagement. Despite these challenges, many OEMs still rely on traditional methods such as supplier relationship management (SRM), enterprise resource planning (ERP) systems, and third-party logistics providers (3PLs). They also optimize logistics routes and employ freight forwarders to handle international shipping and customs authorization. These traditional approaches, however, often fall short of managing the complexity and scale of modern supply chains effectively.

Lack of Transparency

The involvement of numerous participants in the supply chain makes it challenging to track daily activities. This complexity hinders monitoring supplier performance, follow-up on buying resources and goods movement, and compliance with standards and regulations. Currently, some OEMs attempt to enhance transparency through regular performance reviews, supplier scorecards, and basic tracking systems like GPS trackers and barcode scanning. However, these methods are often insufficient for comprehensive transparency.

Risk Management Difficulty

OEM supply chains face numerous risks, including supplier delays, political instability, fluctuating demands, logistics issues, and environmental, financial, and climate risks. While some challenges can be addressed with traditional methods, many risks are inevitable and natural. Mitigating these dangers is challenging, but diversifying supplier bases, conducting regular risk assessments, and maintaining safety stock can help prevent significant damage.

Customer Expectations

Today’s customers demand higher levels of customization, product variety, and on-time delivery services. The OEM industry employs strategies such as configure-to-order and build-to-order to manage and fulfill these needs. It also uses customer relationship management (CRM) systems to understand better its customers. However, these traditional methods often fall short of meeting human demands.

Using Inefficient Methods

The OEM industry still relies heavily on outdated methods such as manual processes, organizational silos, time-consuming business practices, paper-based documentation, and human reporting. These traditional methods consume more time, reduce efficiency, and slow responses to market changes. Therefore, adopting AI and IoT remains the best bet for tackling these challenges.

The Role of AI and IoT in Supply Chain Management

As a result of adopting AI and IoT, the OEM industry is poised to see significant advancements in transparency, efficiency, and risk management. Below are some specific roles AI and IoT play in OEM supply chain management:

Real-Time Tracking and Monitoring

Integrating IoT devices such as sensors, GPS trackers, and Radio-Frequency Identification (RFID) tags enables real-time tracking and monitoring throughout the supply chain. These devices collect and transmit data on goods and services. Real-time tracking helps reduce losses caused by misplacement, damage, or theft during transit. It also allows for proactive responses to unexpected issues, such as adjusting shipment schedules to avoid disruptions caused by traffic jams or changes in climatic conditions. IoT technology assists companies in improving logistics, customer satisfaction, and ensuring the punctual delivery of goods. This, in turn, enhances transparency, especially in complex global supply chains where mismanagement could have significant impacts.

Predictive Maintenance

Predictive maintenance is crucial for companies where equipment reliability is essential for continuous manufacturing, transportation, and services. It uses IoT sensors to collect data on the condition and performance of equipment. AI algorithms then analyze this data to predict when maintenance will be needed. These AI systems detect signs of wear and tear or other issues requiring quick replacement. Examples of AI and IoT systems used for predictive maintenance include IBM Watson, Siemens MindSphere, and GE Prefix. By employing these technologies, OEMs can extend equipment lifespans, reduce maintenance costs, and perform maintenance only when necessary.

Demand Forecasting and Inventory Optimization

AI is highly effective in demand forecasting due to its capability to analyze extensive datasets, including historical sales and market trends. It also evaluates external factors, such as economic indicators and weather patterns. Companies leveraging accurate demand forecasting can maintain optimal inventory levels, avoiding overstocking and stockouts. AI algorithms can identify complex patterns and correlations that traditional methods may overlook, enhancing the accuracy and reliability of forecasts.

Inventory optimization facilitated by AI also reduces logistics costs and minimizes the risk of stockouts, which can adversely affect sales and trust within the supply chain. Overall, integrating AI and IoT technologies increases supply chain flexibility, enabling companies to align supply with market demand more precisely.

Enhanced Decision-Making

With the proliferation of data collection systems, organizations now face the challenge of managing vast amounts of data, often making big data a hindrance to effective decision-making. IoT and AI-powered systems enhance decision-making by analyzing large datasets from diverse sources and generating actionable insights. These systems can process data faster and more accurately than humans, identifying subtle trends and opportunities hidden within big data. AI algorithms excel at extracting meaningful patterns and correlations, enabling more informed and strategic decisions.

Artificial intelligence also automates routine and repetitive tasks such as order processing and invoicing, freeing human resources for more complex and strategic activities. By providing real-time insights and automating decision-making processes, AI allows companies to respond more quickly to changes in demand, supply chain disruptions, and other dynamic factors. This agility is crucial for maintaining a competitive edge. The synergy between AI and IoT ultimately leads to more efficient, transparent, and responsive supply chain management.

Effective Risk Management

AI’s role in risk management is crucial. It uses data from IoT systems and various sources to identify vulnerabilities and risks in the supply chain. AI also analyzes economic indicators, political events, weather forecasts, and uncertainties that affect supply chain operations. This helps OEMs reduce the likelihood of costly disruptions.

Effective risk management is essential for OEMs to maintain operational continuity. Despite unforeseen challenges, they can protect their market position by meeting their commitments to customers. However, the challenge lies in effectively implementing these tools for OEM supply chain enhancement.

Strategies for Effective Implementation of AI and IoT in OEM Supply Chain Enhancement

It is essential to implement sustainable strategies when integrating IoT and AI into OEM supply chains. These strategies must be scalable and thoroughly practical. Below is a four-step process for OEMs to ensure effective AI and IoT integration:

Conduct a Readiness Assessment

To maximize the potential of AI and IoT, an equipment manufacturer must perform a comprehensive readiness assessment. This assessment identifies strengths, weaknesses, and risks in the implementation process, determining the preparedness of an OEM supply chain before integration begins.

Implementing a Readiness Assessment in the OEM Supply Chain

OEMs can follow this step-by-step process to ensure no critical data is omitted during the readiness assessment:

Define Objectives and Scope: Clearly state the goals of the assessment, focusing on the integration of specific AI and IoT systems. Identify which areas of the supply chain require deeper evaluation, such as retail, logistics, or R&D.

Set up a Cross-Functional Team: Assemble a team with members from finance, ICT, logistics, and human resources. Assign specific roles to each member to prepare for future evaluation meetings and discussions. A cross-functional team provides detailed assessment reports from diverse expert perspectives.

Conduct Current State Analysis: Assess previous supply chain methods and processes to identify outdated practices. Analyze the current factory structure to evaluate the feasibility of implementing new changes. Evaluate existing technology and identify systems needing modification, upgrading, or replacement.

Evaluate Resource Availability and Accessibility: Determine the resource requirements for implementation. Assess the funding needed by each department and test employees’ capabilities to ensure access to the necessary technology.

Risk Management: Identify and assess expected and unexpected risks, including supplier delays, logistics issues, and technological and financial risks. Develop proactive methods to prevent expected risks and alternatives to mitigate unforeseen dangers.

Stakeholder Analysis and Engagement: Identify stakeholders affected by the changes and hold regular workshops to inform them of the goals and objectives. Ensure stakeholder awareness and support for the new initiatives.

Develop a Detailed Project Plan: Create a comprehensive project plan with the help of technical experts. This plan should include implementation timelines, schedules, and evaluation metrics to guide the project to successful completion.

Use Performance Metrics and Evaluation: Establish key performance indicators (KPIs) to measure implementation performance. These metrics will help evaluate the success of the project and identify areas needing adjustment.

Following the readiness assessment, the next step is securing the AI and IoT technologies necessary for enhancing the OEM supply chain.

2. Invest in the Right Technology

Despite technological advancements, many supply chains still rely on outdated technologies. A recent survey revealed that 69% of respondents found their investments in operations technology did not meet expectations, contributing to the slow adoption of AI and IoT. This slow implementation may also result from selecting inappropriate AI and IoT systems. Therefore, OEM executives must understand the key components of AI and IoT systems to make informed investment decisions.

Key Components of AI and IoT Systems

Here are some of the major components of AI and IoT tools:

• Machine Learning: is essential for identifying patterns by analyzing big data, and assisting supply chains in making data-driven decisions. It processes historical data to improve logistics and optimize operations.
• Predictive Analysis: Utilizes machine learning and existing data to forecast unexpected changes and future trends, including potential disruptions in the supply chain.
• Natural Language Processing (NLP): Allows machines to comprehend human language, enabling functionalities in digital assistants and chatbots. It is critical for interpreting and responding to text and speech inputs.
• Computer Vision: Enables AI to identify and interpret visual objects and images, useful for tracking goods in warehouses or during transit.
• Digital Twins: Virtual representations of physical objects. They use AI to analyze data for testing, integration, and monitoring. For instance, Volvo uses digital twins to test different parts of their vehicle designs.

Major AI and IoT Systems for OEM Supply Chains

Several AI and IoT systems can enhance the transparency and efficiency of supply chains. Below are key systems applicable to the OEM industry:

RFID Tags (Radio-Frequency Identification)

Function: Uses radio waves to identify and track items, ensuring inventory accuracy and protection against counterfeit markets.

Application: Items are tagged in the factory, tracked through shipment, and verified upon arrival at the store, enhancing visibility and accuracy.

GPS Trackers

Function: Monitors items from the manufacturer to the final destination, facilitating accurate asset tracking.

Application: Used primarily for logistics operations to minimize delivery delays and provide real-time location insights.

Environmental Sensors

Function: Monitor environmental conditions such as temperature, humidity, gas levels, and more.

Application: Integrated into the supply chain to assess environmental conditions before logistics operations and ensure the safe transport of sensitive items.

Smart Shelves/Intelligent Shelves

Function: Equipped with Bluetooth, NFC, cameras, computer vision, RFID technology, and weight sensors to automatically monitor inventory.

Smart Containers

Function: Equipped with sensors to monitor vibration, humidity, movement, temperature, and door openings, gathering real-time data during transit.

Application: Used in shipping operations to prevent damage and loss of goods. Smart containers improve logistics by ensuring product safety and tracking.

3. Testing AI and IoT with Pilot Projects

Pilot projects are crucial for minimizing resource wastage and effectively testing AI and IoT integration in OEM supply chains. These projects help determine the viability and impact of specific AI and IoT systems within various segments of the supply chain. To execute an effective pilot project, OEMs should follow these detailed procedures:

State Objectives and Develop a Detailed Implementation Plan:

The first step involves clearly defining the objectives of the pilot project. Unlike a readiness assessment, the goals here can be as specific as evaluating the efficiency of a new product or gathering detailed feedback from stakeholders. After establishing the objectives, a comprehensive implementation plan should be developed. This plan must outline the project schedules, pilot site locations, roles and responsibilities of team members, cost estimates, and the AI and IoT systems to be tested.

Select the Right Pilot Site:

Choosing an appropriate and scalable location for the pilot project is essential. The site must have sufficient infrastructure to support the IoT technology being tested. Factors to consider include space availability, technological compatibility, and environmental conditions. The selected location should facilitate accurate testing and provide a controlled environment to evaluate comprehensively the AI and IoT systems’ performance.

Engage Stakeholders:

Stakeholder engagement is a critical component of a successful pilot project. After securing the pilot site, it is important to involve all relevant stakeholders. This includes informing them about the project, its objectives, and expected outcomes. Engaging stakeholders early helps align the project with broader organizational goals and secures their support.

Test the AI and IoT Systems on Specific Segments:

OEM supply chains encompass various stages, including planning, sourcing, production, logistics, and distribution. Each stage may benefit from different AI and IoT systems. The pilot project should be segmented to test specific AI and IoT technologies at each stage of the supply chain. For instance, machine learning algorithms could be tested in the planning stage to optimize forecasting. At the same time, RFID tags and GPS trackers could be evaluated in the logistics stage for real-time tracking. This targeted approach allows OEMs to assess the effectiveness of each technology in its respective segment.

Evaluate and Analyze Results:

Upon completing the pilot testing phase, a thorough evaluation and analysis of the results are necessary. Performance metrics, such as key performance indicators (KPIs), should be used to measure the success of the AI and IoT systems. The analysis should focus on operational efficiency, cost savings, and risk reduction. Detailed data collection and analysis will provide insights into the strengths and weaknesses of the tested technologies. Positive outcomes from the pilot project justify the broader implementation of AI and IoT tools across the supply chain.

Feedback Loop and Continuous Improvement:

Establishing a feedback loop is essential for continuous improvement. Gather feedback from all stakeholders and team members involved in the pilot project. This feedback should be analyzed to identify any gaps or areas needing refinement. Continuous monitoring and iteration based on feedback will enhance the implementation process and ensure that the AI and IoT systems are optimized for full-scale deployment.

OEMs can effectively test and integrate AI and IoT systems into their supply chains, ensuring that these technologies deliver the desired improvements in efficiency, transparency, and risk management by following these detailed procedures

4. Conduct Employee Training on AI and IoT

To ensure a smooth workflow, every employee in the supply chain must have at least a foundational understanding of AI and IoT systems. While new employees may come with AI and IoT experience, many long-term employees may not. Continuous training programs and practical sessions are essential to bridge this gap.

Strategies for Conducting Effective Employee Training on AI and IoT Systems

Conduct a Skill Gap Analysis: During the implementation phase, it is essential to develop practical tests to assess the current level of knowledge and skills in AI and IoT among employees. The results from these tests will identify specific areas where training is needed. This step ensures that training programs are tailored effectively to address individual and departmental needs.

Develop Comprehensive Training Modules: OEMs must create modular training programs include theoretical and practical components. These modules should cover various aspects of AI and IoT, from basic concepts to advanced applications. This practical exposure is crucial for employees to understand how to use these technologies effectively in their daily tasks.

Leverage Online Learning Platforms: Online study resources and e-learning courses on AI and IoT systems should be available to employees. Virtual workshops and webinars can also be organized, where employees with significant expertise share insights on current AI and IoT trends and practices. These platforms provide flexible learning opportunities and allow employees to engage with the material at their own pace.Monitor and Evaluate Training Effectiveness:

Continuous evaluation of the training programs is vital. Key performance indicators (KPIs) should be established to measure the effectiveness of the training and track employees’ competency levels. Regular assessments and feedback mechanisms will help identify areas for improvement and ensure that the training programs evolve to meet changing technological needs. Monitoring how well employees adapt to AI and IoT systems will also provide insights into the overall impact of the training.

Encourage Continuous Learning and Development: It is important to encourage employees to keep up with the latest advancements in AI and IoT. Providing access to advanced training programs, attending industry conferences, and participating in professional development courses will help employees stay updated. This approach ensures that the workforce remains competent in leveraging AI and IoT technologies to their fullest potential.

By following these detailed strategies. OEMs can ensure that their workforce is well-equipped to handle the integration of AI and IoT systems. Effective training improves individual performance and enhances overall supply chain efficiency and innovation.

Conclusion

The OEM industry is often characterized by slow changes, outdated methods, and resistance to adopting new technologies. OEMs must modernize their operations to remain competitive in an increasingly dynamic global market. Integrating Artificial Intelligence (AI) and the Internet of Things (IoT) into supply chains is a crucial step in this direction. These technologies significantly enhance efficiency, visibility, and product development capabilities. Companies aiming to differentiate themselves should meticulously identify supply chain challenges and leverage AI and IoT solutions to address them effectively.