You probably already know that most supply chains still rely on Excel for forecasting and that the adoption of machine learning and advanced analytics is slowly and steadily increasing. But this slow increase is not at the pace of the overall transformation that the supply chain has gone under during and after COVID. There is a growing gap.
So, let’s recap the areas where AI and machine learning can improve a manufacturer’s ability to become more proactive to improve business performance. It may finally be time to act.
Accurate Demand Forecasting
AI and machine learning algorithms can analyze vast amounts of historical data, external factors (e.g., weather, holidays, events), and customer behavior to predict future demand with greater accuracy. This enables manufacturers to plan their inventory levels, production schedules, and distribution strategies more effectively – reducing the risk of stockouts and overstocking. The ability to probabilistically forecast and make the right trade-offs can dramatically impact managing intermittent demand and freeing up working capital.
Real-time Inventory Optimization
And a better demand forecast allows for real-time inventory optimization, where inventory levels, sales patterns, and other variables are used to identify opportunities that optimize inventory levels and get inventory as close to the customer as possible.
Improved Supply Chain Visibility
Machine learning algorithms can analyze data from multiple sources, including supplier performance, logistics, and production processes, to identify bottlenecks and optimize supply chain efficiency. This improves supply chain visibility. By identifying and resolving issues proactively, manufacturers can reduce lead times, improve order accuracy, and enhance customer satisfaction.
Dynamic Pricing Optimization
Getting demand, inventory, and visibility optimized are just “table stakes.” Not using AI in these areas can put you at a significant disadvantage out of the gate. Where you gain significant leverage is by going even further to use AI for dynamic pricing optimization. AI and machine learning can help manufacturers to optimize pricing strategies based on real-time market data and customer behavior. By analyzing factors such as demand, competition, and pricing elasticity, manufacturers can adjust their prices dynamically to maximize revenue and profitability.
Lastly, my daughter’s boyfriend works in the supply chain for a paper manufacturer on the shop floor. Recently a piece of equipment went out of service for 4 days, costing them hundreds of thousands of dollars through lost revenue/productivity, machining the new part (in China) and rush shipping to the US. Predictive maintenance is often overlooked as a benefit of AI, but algorithms can analyze sensor data from manufacturing equipment to predict when maintenance is needed. By identifying potential equipment failures before they occur, manufacturers can reduce downtime, improve equipment reliability, and reduce maintenance costs.
With all these known benefits it’s a wonder that the pace of adoption is so slow. Here are some common speedbumps on the road to transformation.
- IT resources are stretched too thin to take on a new project. With growing cyber security threats, P&L concerns, and just day-to-day deliverables, IT is often not as worried about systems that are working well. If the current way of doing things (from a technical perspective) is working, IT tends to shy away from making too many changes for fear of breaking what is working.
- The supply chain team is stretched too thin to conduct an RFI. Sourcing providers, drafting an RFI, talking to the salesperson after salesperson, and then agreeing as a group on the final solution is a daunting task for any team. But add the day-to-day pressures of keeping the business well-supplied in a volatile, and complex environment and you have a recipe for biting off more than the team can chew. Projects such as these require strong leadership and the ability to chunk the tasks into bite-sized pieces that are easy for the team to “digest”.
- Excel has been in place for years and not everyone on the team will warm to a new way of doing things. This feeds into the point above. If it is working, why change it? In a lot of ways, we’ve forgotten to ask “what-ifs” and accept that good enough is good enough.
- It’s too expensive and we do not know how to make the business case. Ultimately businesses need clear direction on the cost-to-benefit time frame for any initiative. IT, the CFO and the CSCO may all have a different view on the ROI of improving supply chain software. Alignment is the key. This will address the big question – whose budget is it anyway?
At Arkieva, we understand the reality of today’s supply chain, its challenges, and the obstacles that stand in the way of improvement. Our primary role in transformational change is to make it less complex for the companies embarking on the journey. Our roots are in supply chain consulting. We’ve had the jobs you are now in. We can help.
Speak with a member of our team to help map out a strategy to rise to the level of the changing landscape. We will guide you to the best decision for you and your team. All it takes is a call. Contact us to get started.