Artificial Intelligence (AI), Machine Learning (ML) and Robotic Process Automation (RPA) has emerged as a transformative force in modern business, particularly in operational and supply chain management. Organisations are increasingly integrating these new technologies to enhance efficiency, reduce mundane tasks, increase predictability, and responsiveness. However, the integration of these emerging technologies into (legacy) IT systems necessitates careful planning to avoid compliance, security, and other IT risks and concerns. This article explores how operational and supply chain leadership is able to partner with IT to successfully implement these emerging technologies to improve operations and supply chains while ensuring robust security. It highlights the collaboration between business and IT to achieve this synergy.
Enhancing Operational Efficiency
AI, ML and RPA have started to significantly enhanced operational and supply chain effectiveness. Here are some examples:
Predictive Maintenance: AI/ ML-driven predictive maintenance is able to use multiple variables to foresee equipment failures before they occur, reducing downtime and extending machinery lifespan. Sensors and Internet of Things (IoT) devices collect real-time data, which AI/ ML algorithms analyse to predict potential issues. RPA can then trigger various workflows, notifications and other tasks to take action.
Demand and Supply Forecasting: Using AI/ ML enables supply chain departments to analyse historical sales and purchase data, market trends, and external factors (e.g., weather patterns, economic indicators) to forecast demand more accurately, supporting optimal supply needs. RPA is able to create the required sales and purchase orders to manage the stock requirements in the warehouse. This helps in optimising inventory levels, reducing stockouts, and minimising excess inventory.
Eliminate Routine Task: Introducing RPA with AI/ ML capabilities, allows automated invoice processing, order matching, variance analysis and action management, while keeping staff members, customers and suppliers updated of their order, invoice or payment status.
Supply Chain Visibility: AI enhances visibility across the supply chain by integrating data from various sources, incl. suppliers, logistics providers, and other internal/ external systems. This allows for real-time tracking and more informed decision-making, allowing pro-active actions before shipments are delayed or worse, "lost". The use of sensors and IoT devices will increase the accuracy of this supply chain transparency.
Logistics and Transport Optimisation: Embedding AI across your transport and logistics processes enables the optimisation of routing and scheduling, reducing transportation costs and improving delivery times. RPA in combination with ML algorithms allow for dynamically adjusting routes based on traffic conditions, weather, and other variables.
Supplier Performance Management: Using AI improves supplier performance evaluation, as certain KPIs and other financial and non-financial performance metrics can be constantly monitored to predict potential risks such as delays or quality issues. This enables pro-active supplier management, better supplier selection and more effective risk management.
Business and IT Collaboration
The successful deployment of these emerging technologies requires close collaboration between business units and their IT department(s). Certain organisations introduce a Business Relationship Manager-role. This role - a very familiar role for UE - is solely focussed on this strategic and operational alignment in bringing these two worlds together to drive a coherent, single-minded and innovative approach to delivering these highly valuable technologies. Here’s a step-by-step guide for BRMs, operational and supply chain leadership to work effectively with their IT department(s):
Step 1: Establish Clear Objectives and Goals
Define Business Objectives: Clearly outline the goals of adopting AI, ML, and RPA. This could include improving demand forecasting, optimising inventory, reducing downtime through predictive maintenance, or automating repetitive tasks.
Align with IT Strategy: Ensure these goals and objectives align with the broader IT strategy and organisational goals.
Business Case: Create a business case that clearly outlines the project expectations, approach, results. This allows the sponsor(s) to evaluate the technology and business impacts.
Step 2: Embed Cross-Functional Teams
Create and Embed Collaborative Teams: It's imperative to create and embed cross-functional teams that include members from operations, supply chain, and IT. This ensures that all perspectives are considered.
Designate Leadership Roles: Assign leadership roles within these teams to oversee the project and ensure accountability. This is often where the BRM-role is placed to ensure that the members of these cross-functional teams are the right people to make decisions.
Step 3: Conduct Needs Assessment and Feasibility Study
Determine Future State: Evaluate the future state of operational and supply chain requirements, the current issues and pain points that AI, ML, and RPA should address.
Feasibility Study: Conducting a feasibility study to determine the technical and economic viability of implementing these technologies will provide both parties with clarity in how these emerging technologies will work. Document any learnings to ensure the next phase will be successful. If the feasibility study has not provided the expected outcomes, different technologies, processes, etc can be further assessed.
Step 4: Develop a Detailed Implementation Plan
Roadmap Creation: Once the feasibility study has been successful, develop a detailed roadmap outlining the implementation phases, timelines, resource requirements, and key milestones.
Risk Assessment: Identify potential risks and develop mitigation strategies. This should include security risks, integration challenges, and potential operational disruptions.
Step 5: Select Appropriate Technologies and Vendors
Evaluate Technology Options: Work with IT to evaluate different AI, ML, and RPA solutions. Consider factors such as scalability, compatibility with existing systems, security features, and vendor support.
Pilot Program/ Proof of Concept: Implement pilot program or Proof of Concept (PoC) to test the selected technologies on a small scale before full deployment.
Step 6: Ensure Data Security and Compliance
Data Protection Measures: Implement robust data protection measures, including encryption, secure data storage, and access controls.
Risk and Compliance: Embed a (tailored) risk and compliance model with relevant data protection regulations such as, ISO 31000, ITIL to support GDPR and CCPA.
Step 7: Integration with Existing Systems
System Integration: Work with IT to integrate AI, ML, and RPA solutions with existing IT infrastructure. Use integration platforms to facilitate seamless data flow and system compatibility.
System Log-in, APIs and Middleware: Utilise strict RPA-user accounts, secure APIs and middleware to ensure smooth integration and minimise disruptions.
Step 8: Develop Training and Change Management Programs
Staff Training: Develop training programs to upskill employees on using AI, ML, and RPA tools. This includes both IT professionals and end-users in operations and supply chain.
Change Management: Implement change management strategies to help staff members across the business and IT to adapt to these emerging technologies and processes.
Step 9: Continuous Monitoring and Improvement
Performance Monitoring: Establish metrics and KPIs to monitor the performance of AI, ML, and RPA implementations. Use analytics tools to gather insights and evaluate success.
Continuous Improvement: Regularly review performance data and feedback to identify areas for improvement and make necessary adjustments.
Step 10: Foster Ongoing Collaboration and Communication
Regular Meetings: Schedule periodic meetings - with clear agenda points (performance review) and actions - between operational, supply chain, and IT teams to discuss progress, address challenges, and share insights.
Feedback Loops: Establish formal and informal feedback loops to continuously gather input from all stakeholders and incorporate it into ongoing improvements.
Unique Excellence's - Case Study
This structured approach through which operational and supply chain leadership (and the BRM) is able to effectively collaborate with IT to adopt AI, ML, and RPA technologies, has ensured a smooth integration while addressing security and other IT concerns. Our UE team has experienced in the various project initiatives how this collaborative effort has helped achieve significant efficiency gains, financial benefits and other competitive advantages.
Leading Health & Wellness Business, focussed on growing their Asia-Pacific Market Presence in the Natural Health Industry
Objectives:
Goal: Improve demand forecasting accuracy by 30%.
Alignment: Introduced a cross-functional Sales & Operational Planning model, that required input from sales, finance, procurement, operations and IT
Form Teams:
Created a cross-functional team with supply chain and operational staff members, data scientists, and IT specialists.
Assigned a lead from each function.
Needs Assessment:
Current pain point:
Frequent stockouts and overstock situations across the various Asian countries.
Shipping delays due to misalignment between demand and supply
Cross-Asia shipments to make up for the stockouts
Feasibility: Introduced a ML-powered forecasting model that addressed these issues cost-effectively.
3-6 Month Implementation Plan:
Phase 1 - Pilot across two Asian Countries
Phase 2 - Full-scale implementation across all Asia-Pacific region
Phase 3 - Optimisation of S&OP model through variable analysis, updates, introduction of near-real-time data, etc.
Compliance and Risk:
Identify data security and integration as primary risks due to the use of external logistics and transport providers (3PL), withe the need to share accurate data
Select Technologies:
Evaluated various tools based on historic data capturing, analysing and scenario-planning ability
Pilot: Tested two technology solutions that would seamless integrate with the various existing (legacy) solutions on the two selected Asian countries.
Ensure compliance with the client's existing Risk Framework and GDPR by conducting regular audits.
Training:
Conducted various workshops for Asian sales teams, operations and supply chain staff managers on using emerging tech tools.
IT team receives training on managing and maintaining the new tech solution.
Monitoring:
Used MS BI/ Tableau to track forecasting accuracy and other KPIs.
Regularly review performance and make necessary adjustments.
Ongoing Collaboration:
Weekly S&OP meetings to discuss progress and challenges.
Continuous feedback from sales, finance, supply chain and operational, and IT specialists to improve the S&OP solution.
Next Steps
The UE team believes that AI, ML and RPA has the potential to revolutionise operational and supply chain management, offering significant efficiency gains, cost reductions and enhanced decision-making capabilities. However, the integration of these emerging technologies into an organisation's (legacy) IT systems must be approached with a strong emphasis on compliance, security and collaboration. By ensuring robust data security, addressing system integration challenges, implementing comprehensive cybersecurity measures, and fostering close collaboration between business units and IT, organisations will be able to harness the power of AI while mitigating associated risks.
Having our UE team work side-by-side with the business and IT teams have enabled us to know that this balanced approach enables businesses to innovate and remain competitive in a rapidly evolving technological landscape. For more information, please contact us on info@unique-excellence.com