By Kurt Bender, SVP and Chief Digital Officer - BWXT, United States
Artificial Intelligence (AI) has generated industry attention as the latest technology that can deliver meaningful returns to organizations. In the manufacturing vertical, companies have sought to digitally enable processes that improve production lead times, inventory management, part manufacturing cycle times, and logistics process times. Technologies such as software-based applications, workflow optimization tools, electronic data capture and management, and reporting have been at the center of these process improvement initiatives. AI now adds a new capability to this toolbox.
Organizations are facing numerous challenges in rolling out AI. Due to the fast pace of change in AI technology, identifying an AI deployment strategy for successful implementation and adoption is critical. One approach to solving this is by focusing on process optimization for organizations to systematically identify areas needing improvement. Understanding the challenges that meaningfully affect the organization will draw traceability to solutions that deliver the most value.
Predictive maintenance is an example of a key business process that can be optimized using AI in manufacturing organizations. By capturing and analyzing sensor data from factory machine equipment, AI can identify anomalies and predict equipment failures before they occur. This AI-enabled process can help minimize unplanned downtime and reduce maintenance costs.
This article will explore how to develop an AI deployment strategy by starting with organizational process constraints. This takes a lean six sigma approach by understanding process challenges and measuring the opportunity based on value and complexity ensuring meaningful business opportunities are realized.
Understanding AI Trajectory
AI has shown fast industry adoption paired with fast technological advancements. AI initially focused on conversational capabilities enabling users to interact and prompt questions generating responses based on language model learning. A shift towards AI-augmented development and process automation has followed providing capabilities beyond traditional robotic process automation (RPA). The current trend shows generative-based capabilities that support automation of new designs and complex end-to-end processes.
Start with Process
To leverage AI effectively in improving internal business processes, organizations must first assess their existing operations. This includes engaging teams to identify areas for enhancement across all functions. This allows teams to systematically pinpoint bottlenecks, creating pathways to solutions that align with organizational strategy and regulatory requirements. Starting with simpler, high-probability improvements that yield measurable benefits in safety, quality, cost, and efficiency will instill confidence and build capabilities for tackling more complex processes. This also fosters a culture of continuous improvement empowering stakeholders to contribute knowledge and further enable integration of AI tools for ongoing value realization.
Set Goals
Organizations can set clear goals around utilizing AI to improve business processes and achieve significant outcomes in efficiency and productivity. By defining objectives that leverage AI to streamline workflows, eliminate waste, and automate repetitive tasks, such as data entry and customer support, companies can empower their teams to prioritize value-added activities while maintaining compliance with regulatory and quality standards. Establishing specific targets for AI-driven improvements, like optimizing inventory management and enhancing decision-making through data analytics, allows organizations to measure cost savings and increase profitability, ultimately fostering a sustainable business model. Additionally, it is vital to cultivate a culture of innovation and accountability by encouraging employee participation in AI initiatives. By setting goals that promote data-driven decision-making and effective change management, organizations position themselves to uncover hidden opportunities, make informed strategic choices, and thrive in a competitive landscape, driving continuous improvement and long-term value realization.
Brainstorming Process Improvements
Engineering: In the engineering function, workshops can be held to analyze current workflows and pinpoint inefficiencies in product design, testing, and production processes. Common engineering processes that may present opportunities for leveraging AI include design simulation and analysis, where AI algorithms can automate the testing phases to quickly assess various design parameters and constraints, leading to optimized structures and components. Predictive maintenance for equipment is another area where AI can anticipate failures by analyzing sensor data, allowing for proactive maintenance and minimizing unplanned downtime. Additionally, AI-driven automated quality control inspections utilize computer vision to detect defects in products or components with greater accuracy, significantly enhancing quality assurance processes. Lastly, optimizing manufacturing workflows through AI can streamline operations, reducing cycle times and resource consumption. Engineering can achieve substantial improvements in product quality, efficiency, and overall project outcomes.
Supply Chain: For the supply chain function, a brainstorming approach can involve key stakeholders across procurement, logistics, inventory management, and demand planning. Utilizing techniques like affinity diagrams or process mapping during brainstorming sessions can help uncover bottlenecks, redundancies, or gaps within the supply chain. Common processes ripe for AI-driven improvement include demand forecasting, where AI algorithms analyze historical sales data and market trends to provide accurate predictions; inventory management, which utilizes AI to monitor real-time inventory levels and optimize reorder points; and supplier selection, where AI evaluates supplier performance and associated risks. Additionally, organizations can leverage AI for route optimization in logistics to enhance transportation efficiency, warehouse management to automate order picking, and production planning to align manufacturing schedules with demand forecasts. Supply Chain can enhance forecasting accuracy, optimize inventory levels, reduce excess costs, minimize stock-outs, and improve overall service levels, ultimately driving greater operational efficiency across the supply chain.
Human Resources (HR): In the HR function, teams can encourage idea generation that focus on various HR processes, such as recruitment, onboarding, performance management, and employee engagement. Brainstorming sessions should include diverse perspectives, allowing participants to identify inefficiencies or areas where AI can be employed to automate repetitive tasks or enhance decision-making. Common processes that may yield AI opportunities include candidate screening using natural language processing to analyze resumes and identify suitable candidates efficiently, automating onboarding paperwork and training scheduling through AI-driven platforms, and utilizing sentiment analysis of employee feedback to improve engagement strategies. Additionally, AI can optimize performance management by providing objective insights into employee performance trends, recommend personalized learning paths for development, and automate HR data analytics to identify turnover patterns. HR can elevate workforce satisfaction and streamline HR operations ultimately creating a more engaged and productive workforce.
Finance: Within the finance function, process improvements can be found in budgeting, forecasting, accounts payable/receivable, and financial reporting. Collaborative sessions can leverage financial modeling and analytics to visualize pain points and discuss potential AI applications. Common processes amenable to AI enhancements include automated invoice processing, where AI can extract and validate data from invoices, predictive analytics for cash flow management to forecast future liquidity needs, and anomaly detection in financial transactions to identify potential fraud or errors. Additionally, AI can streamline expense management by analyzing expense reports in real time, enhance portfolio management through data-driven investment recommendations, and automate regulatory compliance monitoring. Finance can achieve quicker turnarounds on financial reporting, enhanced accuracy in forecasting, and reduced manual errors, facilitating more informed strategic decision-making.
Information Technology (IT): In the IT function, roles including systems analysts, service desk, and software developers can explore opportunities for process improvement. Utilizing retrospectives to assess existing workflows, teams can discuss challenges in areas such as incident management, change management, and system maintenance. Common processes that could benefit from AI integration include automated ticket routing and prioritization in incident management, where AI can triage and assign tickets based on issue severity; predictive maintenance based on system performance metrics to anticipate potential failures before they occur; and user behavior analytics for security to identify anomalies that may indicate threats. Additionally, AI can automate administrative tasks, streamline application performance monitoring, and optimize capacity planning within IT systems. IT can enhance service delivery, reduce response times for incidents, and ultimately improve the user experience across the organization.
Create a Value Matrix
To effectively evaluate and prioritize process improvement opportunities for leveraging AI, organizations can use a value matrix that plots initiatives along two axes: value and complexity. The value axis reflects the potential impact of AI implementation, such as cost savings and efficiency gains, while the complexity axis measures execution difficulty, considering resources, time, and change management. By mapping these opportunities, organizations can identify high-value, low-complexity initiatives as quick wins for immediate benefits, while also recognizing high-value, high-complexity projects that require careful planning and resource allocation. Opportunities in the low-value quadrants should be re-evaluated to ensure alignment with organizational goals. This structured approach fosters effective AI implementation, optimizes resource allocation, enhances focus on impactful initiatives, and accelerates the journey toward operational excellence.
Looking Forward!
By adopting a structured approach to identify and evaluate business processes that are constrained and exhibit process waste, organizations can enhance their operational efficiency through the strategic application of AI. Leveraging AI allows businesses to automate repetitive tasks, optimize workflows, and uncover hidden inefficiencies that may not be immediately apparent through traditional analysis. As organizations systematically assess these processes via brainstorming sessions and value matrices, they can pinpoint high-impact opportunities that deliver substantial value without compromising on complexity. This targeted approach not only empowers teams to focus on initiatives that drive measurable benefits, such as cost reductions, improved quality, and enhanced productivity, but also cultivates a culture of continuous improvement and innovation.
