A new standard for lean manufacturing? How cutting optimization increases the entire manufacturing organization’s profitability

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Optimized cutting and kitting processes in many discrete manufacturing segments are crucial due to the high cost of raw materials, and therefore they have a direct influence on the company’s overall profitability. 

Traditionally, these processes were (and are) performed manually, involving only basic nesting software that uses pre-made libraries of cut plans, which are used repeatedly and in a wide range of use cases.

The approach of a traditional nesting software is problematic for several reasons: 

  • Generating the appropriate cut plan isn’t easy and, in some cases, takes a few days. Using the same program again and again from the nesting library when your plans and material in stock are always changing is simply not reasonable. This leads to compromises around both the production timeline and the efficiency of the result, and becomes even more of an issue considering the dynamic nature of discrete manufacturing businesses.
  • Relying on manual selection of geometry files may lead to human errors that adds to the cost. Companies must recut and scrap materials simply because an old design file was mistakenly used.  
  • Rush orders, changes in business needs, new cut plans and materials all need to find an answer within a limited number of plans included in the existing library. 

Along comes Artificial Intelligence and Automation

Computer monitor - Wallpaper

AI technology helps meet manufacturing needs by offering a dramatic advancement in cut planning and manufacturing optimization. 

Instead of ‘feeding’ the software with each and every detail of the plan and hoping for the best, advanced software is able to receive all relevant data and automatically create optimized cut plans for all machines at all sites.

Here are some of the prominent advantages of an AI-based  cutting and kitting solution: 

  • Advanced software solutions have full integration with other systems and IIoT devices, including the company’s ERP and CAD/PLM. New work orders are automatically processed, the CAD system’s DXF design files are taken into consideration and the cut plan is generated based on the most recent changes.  
  • The software is also able to run tests and simulations in parallel to the working cutting machines. Manufacturers can simulate multiple scenarios without any disruption to the current manufacturing process. 
  • Businesses operating multiple factories and production floors can align the work from one site to the other using the same software and having a birds’ eye-view, so that the workflow and use of materials are optimized, business priorities and KPIs are taken into consideration, and no raw material expires mid-process. 
  • Improved visibility is achieved thanks to detailed reports that can be viewed anytime, anywhere and via any device. Managers can compare different sites and plans, make smarter inventory purchasing decisions, while maximizing the overall performance of the entire business. Raw materials are no longer treated in a simplified FIFO manner. 
  • Some industries understand the difference between optimized manufacturing and traditional work all too well. The composites industry, for instance, uses extremely expensive materials such as Carbon Fiber Reinforced Plastics (CFRP). While this material provides excellent mechanical properties, it requires careful and customized planning, since any cutting errors or subpar planning results in preventable, painful costs.  
  • Automation helps cut cost, effort, and time which has a direct effect on the company’s ROI. 
  • Advanced discrete manufacturing is an intensive arena with complex cutting plans and related data loads. Cloud storage opens the door to almost unlimited amounts of data, which can help support AI algorithms and make them richer and smarter at a relatively low cost. 

An ultimate cutting strategy 

Advanced manufacturing businesses learned to lean on AI technology to save materials and reduce their operational costs, by synchronizing the entire business and supply chain.Data - Enterprise asset management

They embrace the mantra “cut only what you need” and implement it throughout the workflow in every decision made. The final step of cutting the material is based on countless considerations and a lot of data.

In addition to a smarter, more efficient process, these manufacturers reach a superior end result that is free from error and meticulously executed. The product is ready on time and no rework is required, which adds to the company’s overall throughput and capacity. 

Here’s a concrete example that I like using: 

The example below shows the traditional way of cutting a specific material into different parts. Each type is cut separately, in a sequential way. As you can see, the total material used stands on 160 in’. 

Under an optimized cutting plan, as you can see below, the same cutting is done based on much smarter calculations. All parts required in a given shift or ady are mixed and cut from the same sheet, resulting in almost 20% material saved (Total Material used for all 3 jobs combined in this example: 129.65”).  

In a discrete manufacturing industry, the flexibility offered by AI-based cutting and kitting solutions, provides a significant added value. Enterprises are able to make quick and smooth adjustments at scale and save significant costs.  

Summary 

In today’s world, being lean and efficient is key for resilience. Facing fierce competition, unpredictable environmental challenges (COVID-19 for instance) and ever-increasing quality standards, manufacturers are leaning on technology to optimize every step in the process. For advanced manufacturers, raw materials direct and indirect costs are so significant that they can impact the entire company’s profitability. 

Smart cutting planning leans on powerful algorithms and AI calculations that run optimized plans. The result is minimum material waste, much smarter inventory management, a quantity of ready material that can meets the production floor capacity and demand (not less and not more), a minimum level of human error originated issues and more. 

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