According to Polaris Market Research, the market size of artificial intelligence (AI) in manufacturing is predicted to grow more than 41% during the next decade. Although the latest Sikich Industry Pulse found that less than 20% manufacturers have started to implement AI, the ones that want to thrive in the next decade are pursuing AI strategies that make sense to support their business objectives. AI can help manufacturers target where to focus and automate mundane tasks.

There are many uses for AI in manufacturing. A classic example is predictive maintenance. Instead of preventative maintenance, target where to maintain with predictive maintenance to maximize your efforts. Collaborative robots (cobots) work alongside people and often can perform tasks such as those requiring heavy lifting. Digital twins are virtual models of physical objects or layouts, and they can receive information about the object through sensors to get information about maintenance needs etc. On the software side, manufacturers are using robotic process automation (RPA) to handle high-volume, repetitive tasks that can be automated. 

For example, in working with a water tank manufacturer, we wanted to upgrade the use of ERP and start using MRP (material requirements planning) recommendations for purchasing. Before turning MRP on, we had to perform a cleanup of data and add routing data steps in the system. The team was lean, and it was estimated to take a few months to prepare for go-live. The executive team did not want to wait to improve service and increase efficiencies, and so a technical expert on the team used RPA to write a code to automate the setups. We completed the preparation in three days instead of three months and started gaining results.

Another application for AI in manufacturing is a lights-out factory which runs with robots. Although full lights-out factories are rare, this application is gaining momentum across the globe especially as workforce participation rates remain low, manufacturers realize they need to gain control of their success and are reshoring, and margins remain tight with high material and labor costs. Robots can operate around-the-clock without lunches, breaks, and workers compensation claims.

For example, an industrial manufacturing client struggled to find the talent to run its manufacturing operations. They couldn’t keep up with demand, employees were frustrated, and customers were unhappy. They purchased a robot to perform production in a key bottleneck area of the facility; however, the robots couldn’t produce around-the-clock because there was nowhere to store finished product on second and third shift without material handlers. Thus, the engineering team developed an automated way for the product to be moved from the point-of-production. Past due deliveries plummeted as the system came online.

AI is also prevalent in supply chain applications that support manufacturing success. For example, sales forecasting and demand planning is supported with AI algorithms to better predict demand. Even in the most industrial of manufacturers, demand patterns have been difficult to predict, creating a need for AI to get in front of demand. AI is also used in inventory management and to prevent bottlenecks and predict what’s needed.

AI requires proactive design so that you can limit expense and minimize the high-skilled resources required to go live. Start with a rapid assessment of your business requirements, process and technical infrastructure and resources. Select a pilot to test your plans and results will follow.

Originally published in Brushware Magazine, January / February 2024.

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Although production and materials planning can be overlooked in its importance in most companies if going smoothly, it is cornerstone to success. Unfortunately, when not going smoothly, it can bring a company to its knees. For example, production might not know what to run, changeovers can be out of control, customers become unhappy, materials shortages persist, resources are scrambling to catch up with changing conditions, and chaos ensues. Read more about this topic in our article, The Million Dollar Planner.

An Industrial Equipment Manufacturer Case Study

An industrial equipment manufacturer struggled to keep up with month end sales goals when receiving last minute notice from Engineering of final design of the engineer-to-order item (bills of materials) before the item was scheduled to ship to meet customer requested dates. There were multiple stages to the manufacturing process (fabrication, weld, paint, final assembly), and parts had to be shipped offsite for process steps and married up along the way at the “right” time to make the orders come together.

The bottleneck and pacing item was the machine shop, yet visibility was limited to seeing which parts had to complete production at the same time, and the production schedule was completely manual based upon paperwork on hand since there was a lack of visibility in the system. The production supervisor would go through the work order packets, pick out manufacturing differentiators (size, material type, etc.) and group the packets in piles by the optimal run sequencing. For example, you run different sizes on different machines, and you would sequence by material type to be most efficient with changeovers.

Although the machine shop pulled out the stops on a regular basis to meet sales goals, it required constant expediting and coordination of process steps, was sub-optimal based upon the work order packets available at the time, and the process was completely dependent on a person (who also turned into a single point-of-failure). Since there was a lack of visibility, sales order availability frequently moved from month-to-month, creating concerns with predictability. And the machine shop ran less efficiently than it would have if there was visibility to the full scope of work order packets.

As we provided consulting support to this client, we learned about the optimal sequencing triggers (size, material type, etc.) and looked for ways to identify these triggers sooner in the process. Of course, it is never as easy as it appears. Thus, we had to work upfront in the sales quoting process to get a better picture of the demand plan by adding configuration strings (high-level identification of the item) into the process and system early in the process. By adding this information into sales orders, the team had better visibility to what was coming down the pike prior to Engineering’s final design so that we could gain visibility to plan capacity and materials (master scheduling) instead of reacting to sales orders late in their life cycle. We integrated this visibility into a SIOP (Sales Inventory Operations Planning) process to build a monthly cadence and review of critical sales and operational forecasts.

To address the machine shop scheduling, additional triggers had to be identified and incorporated into the data. Sales order statuses were also key to the process as sales orders went through engineering, production engineering, customer approval, material availability, and work order creation before the items were available to be scheduled. We built these statuses into a planning report along with key triggers and dates (incorporated from a production status review process). Once this report was built, a dashboard was developed for improved visibility and ease of use. This powered the production scheduling process and replaced the packets process so that the system automated the 80/20 and focused attention on what was meaningful to optimize the production schedule and ensure the parts married up at the right time.

Master Scheduling & Production Planning Results

As the client gained visibility to required capacity and materials, they were able to start making directionally correct decisions early in the process with the master scheduling process. As capacity bottlenecks arose, they were able to address proactively before “running into a wall”. For example, we gained visibility that paint was a future bottleneck, and so the head of Operations was able to put together the appropriate capital requests, gain approval, and order an additional paint system to support sales growth goals. Additionally, offload capacity was needed to supplement the weld area, and so leadership was able to pursue additional options prior to negatively impacting customers.

From a materials standpoint, Purchasing was able to look into the future and secure materials ordered from the Russia-Ukraine region while they were still available. While every client struggled to maintain service levels during COVID, our client was able to keep one step ahead and sustain higher levels of service for customers.

As the production scheduling process was upgraded, our client gained visibility to the machine shop and could optimize efficiencies and gain capacity. The head of Operations said he was able to double capacity to support sales growth. The production schedule was no longer dependent on a person; it became part of a process. Thus, this key resource could focus attention on further optimizing machine shop performance.

The Bottom Line

Pay attention to your planning processes as they will drive bottom line business results. Changing from reactive to proactive sounds far easier than it is when you get down to the details, but rolling out the appropriate process, data, and ERP system upgrades will propel progress. If you are interested in talking about implementing a master planning and production scheduling process upgrade to improve visibility and results, contact us.

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Clients are struggling to keep up with customer’s changing requests. Order backlogs remain relatively high (depending on the industry), but customers are pushing orders out at the last minute, pulling orders in without notice, adding future potential orders, and changing requirements on the fly. Production is scrambling to keep up.

80%+ of manufacturers simply do not have enough skilled production and support resources to keep up with the volumes, let alone with the volatility of the order backlog and changing forecasts. Not surprisingly, executives do not want to hire more resources than absolutely necessary as they are concerned about rising input costs and the uncertainty of their order backlog. To add fuel to the fire, the supply chain has been volatile as well with global unrest, strikes, and other disruptions as well as supply chains on the move. Read our recent article on how supply chains are changing. The production resources cannot keep up with changing conditions, and triage must occur.

Our best consulting clients are engaging in proactive business processes to get ahead of changing customer conditions and sales forecasts and the impact on capacity, production and replenishment plans as well as the reallocation of critical resources. SIOP (Sales Inventory Operations Planning) is a key process and toolset for successfully navigating this volatility while maximizing output and production capacity to support revenue growth.

An Industrial Manufacturer Case Study

An industrial manufacturer struggled to meet customer requirements. Order deliveries were lagging, capacity wasn’t allocated evenly across its ten production facilities and production at a critical site had almost 1000 hours of change overs per month for nine months in a row to try to keep up with urgent customer requirements. Several large customer jobs pushed out and others pulled in, keeping Operations scrambling.

We rolled out a SIOP process, starting by getting a handle on the sales orders and potential sales orders. A weekly meeting with Sales and Project Management helped to solidify the priorities of the demand plan (sales forecast). Although customers continued to request push outs and pull-in’s, when the requests were proactively worked with the team and the ERP system was maintained, better clarity emerged.

The demand was run through a capacity model, showing available capacity vs. operational requirements by production facility. The operational requirements were bucketed in categories of firmed sales orders, sales orders waiting on Engineering release, sales quotes that were better defined, and sales quotes. By evaluating near-term capacity, priorities could be established with Engineering, short-term capacity actions could be taken (overtime, supplementing production at additional sites, etc.), and proactive customer communications could take place.

More importantly, by evaluating medium and long-term capacity, the appropriate strategic decisions came to light. For example, the critical site showed as overloaded months in advance so that Operations could reallocate customer orders among production facilities within the same region to mitigate impacts on freight cost. The model could be evaluated with multiple what if scenarios so that Sales and Operations could address the bottlenecks proactively. Guidelines were set to reprioritize and set pricing for key customers, capacity could be reallocated, additional capacity could be planned, and capacity offload options explored.

The key is the connection between Sales, Project Management, and Operations and Engineering. As customer requirements change, capacity scenarios need to be reevaluated and impacts reviewed. Proactive communication and collaboration is a critical piece of SIOP to keep demand and supply aligned and optimized.

SIOP Maximized Production Output & Capacity

By seeing the demand and capacity picture in advance with SIOP, the executive team could maximize production output and capacity. They could do this by proactively addressing bottlenecks to level load the plants so that the scheduling teams could optimize the production schedules to increase efficiencies and reduce waste. By running like items, sizes, and material types together, changeovers are minimized. And by seeing the final assembly schedule requirements, labor and resource plans could be optimized.

Also by reviewing the full capacity requirements across all North America sites, capacity could be reallocated to maximize output, thereby minimizing the need for offload capacity. Each plant’s strength could be maximized and planned in advance while minimizing transfers between plants, freight to customers, and material price differences.

By addressing these supply plans proactively, materials contracts could be addressed in advance ensuring material availability which positively impacts manufacturing planning and output. It also typically provides opportunities for more favorable contracts and pricing. In addition to maximizing production and capacity output, SIOP improved the customer delivery performance, resulting in happier customers and additional revenue possibilities.

SIOP: A Look Forward

In our book, “SIOP (Sales Inventory Operations Planning): Creating Predictable Revenue and EBITDA Growth“, we discuss how SIOP can support these types of improved results. As companies navigate the exaggerated volatility of the global environment and try to keep up with changing customer needs, SIOP becomes an essential tool in the toolkit to survive, let alone thrive. Our best clients are utilizing SIOP as a way to take control of their future and manage their options instead of letting their situation manage them. In fact, they are taking SIOP to the next level with advanced technologies and by connecting SIOP to their customers and suppliers to gain an end-to-end supply chain view.

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