How Can Companies Achieve Maximum Efficiency With Label Converters?

If you’re bleeding time and money, now is the time to change!

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08/01/2022

Greg Cholmondeley

 

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It’s hard to go to a conference, tradeshow, or even read a print industry news article that does not discuss labor, substrate prices, and availability. These are critical areas of concern for label converters due to the often-complex label geometries of this type of application and the tradeoffs faced.

 

Labels are an essential way to differentiate and even personalize products to capture wider attention. However, it also leads to complexities for label and packaging converters. Filling label substrates to the maximum would not be too difficult if all labels were rectangular, the same size, and had the same order quantity. Finding the best way to print and gang label print jobs to optimize material provides less waste and increases efficiencies. Ganging jobs and impositioning labels to maximize media coverage can solve the problem. However, this process can take a significant amount of time for skilled prepress staff. This creates a tradeoff between how much time and money label converters spend on ganging and impositioning versus money lost on unused substrate.

 

 

Determining when substrate utilization is “good enough” can be difficult, and even good enough is rarely the best option. Every minute spent planning and impositioning print jobs reduces profitability; every square inch of the unused substrate also costs money. Label converters who manually gang and nest jobs, however, must constantly balance the tradeoff between labor and waste.

 

Of course, there’s a reason why so many label converters still manually perform these functions. Traditional computer-driven ganging and nesting programs are typically expensive and slow, particularly with irregularly shaped images. Most of these programs start with predefined templates, try every possible combination, and give up after a predetermined number of attempts or once coverage is attained. In this case, human experience, perception, and intuition can often be more effective at solving problems than computers.

 

Tilia Labs applies leading-edge artificial intelligence (AI) programming to resolve these profit-killing waste and labor problems. The system uses experientially learned pattern recognition to identify efficient potential nesting approaches across images and jobs. Thus, the solution combines the power of computers to inspect millions of combinations with a human-like ability by starting with the most likely candidates. While Tilia Labs’ software starts with learned solution approaches, it continues to recognize and learn new patterns for any mix of jobs and job elements.

 

This intelligent planning and impositioning software helps label converters optimize production by better managing the influx of smaller jobs while maximizing print efficiency. The range of planning and intelligent imposition solutions from Tilia Labs enables label converters to get the most out of their equipment, substrates, and labor costs.

 

Keypoint Intelligence Opinion

Intelligent planning, ganging, and impositioning are critical for reducing waste and labor profit killers, as well as reaching maximum velocity in your label printing operations. Tilia Labs’ Imposition AI understands the mix of jobs, looks across all available printing methods, and calculates thousands of potential production layouts in seconds rather than hours. You can select the best plan based on costs, waste, service level agreements, and your overall production schedule. Tilia Labs claims that customers realize an average of 15% less material use when using its solutions versus traditional approaches. That can result in significant savings across jobs and over time.

 

Log in to the InfoCenter to view additional research on AI, impositioning solutions, and label printing through our Color Digital Label & Packaging and Production Software Advisory Services. If you’re not a subscriber, just send us an email at sales@keypointintelligence.com for more info.

 

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