How do Packaging Manufacturers Maximize Margins and Preserve Profits with Production Data?

Over the past decade, productivity improvement has stagnated as many manufacturers reach a limit on the benefit of traditional continuous improvement.Now, as the shop floor becomes digital, a whole new world of data and insights are unlocking CI to reignite manufacturing productivity.
Production Data in Manufacturing

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This is a time of changing dynamics in the packaging manufacturing industry. Margins are under pressure from increasing costs of materials and energy, and consumer preferences are evolving. People are looking for more convenience, and brands, for their part, want as much differentiation as possible. Both trends create momentum towards greater personalization of packaging, a wider variety of SKUs, and shorter runs. 

Another major trend is sustainability. Recyclable products are currently in high demand, to the point where 57% of packaging executives cite sustainability as today’s top trend.*  Geopolitics figure into the equation as well, with fallout still to come from Brexit and various international trade wars. 

While you can’t control these external factors, you can shape an effective response. Manufacturers who can maximize productivity, improve product quality, and reduce energy consumption will be well-positioned to exploit today’s challenges and transform them into opportunities. Here’s what we mean: 

When considering shorter-run artisanal-product clients like micro-brewers, you have to factor in a number of incremental costs. First is the expense of switching among different SKUs: more setup time, additional tooling, printing, and the like. Similarly, specialty packaging entails increased complexity: adjustments to line mechanics for customized elements like logos, labels, and embossing. Next, there are differences in product behavior — for example, aluminum cans with a matte finish tend to stick together more. This reduces the likelihood of faults at higher run rates, enabling you to push production speeds to boost output and become more competitive. 

These and other considerations affect pricing in different ways. If you can quickly tune machine settings to deliver high throughput with low scrap for each SKU – and can also spot and avert potential outages in advance – then you can price your runs to both undercut competitors and maximize profits.

This kind of production optimization is very achievable. But it requires a high degree of visibility into your process as a whole, far greater than what most packaging manufacturers possess right now. Data holds the key to changing the visibility equation. 

What production data can do

Most packaging manufacturers already have plenty of production data: terabytes of input streaming in continuously from hundreds to thousands of shop-floor sensors. Blend this insight-filled data together with information on quality, downtime, and energy, and voila, you have complete real-time visibility into production – the foundation for optimization.

There’s just one problem: production data is siloed in a jumble of different, incompatible formats and systems. And there is no scalable, automated way to integrate it all. Doing so manually would take an army of data scientists forever, even working around the clock. So, it just doesn’t get done. In point of fact, roughly 99% of manufacturing production data sits unused, in effect gathering virtual dust. 

This is where manufacturing has been stalled for years. But recently, the obstacles to full visibility have been swept aside by a combination of technology breakthroughs:  

System-wide modeling. The capability to consolidate and integrate the entire universe of production data, providing whole-system visibility into production lines – and beyond that, across your entire network of plants.

Real-time data. The ability to collect and evaluate streams of sensor data arriving on a continual basis – enabling operators to see problems as they arise and take corrective action to avert faults and downtime.

Digital manufacturing expertise. A blending of solid data science knowledge with the operational savvy to convert data-driven insights into concrete actions that optimize production. 

From Insight to Action: The Sight Machine Data Platform for Package Manufacturers

These pioneering capabilities have been engineered into the Sight Machine software platform, developed, refined, and field-proven over the last seven years. The solution includes analytics tools specifically designed to address package manufacturing challenges, and comes with Sight Machine’s expert advice and support in applying analytics results to deliver concrete operational improvements. 

Figure 1: How Sight Machine Delivers Production Optimization

Production Data in Manufacturing

A company wanted to trace the root cause of customer complaints about product quality. Specifically, they needed to match customer returns with product manufacture three months beforehand, to understand what had happened. Sight Machine was able to associate the defect claims data with the digital model of production at that specific time, to localize where the problems originated, what caused them, and determine machine settings to prevent future recurrence. The result was a savings of $650,000 at the single plant, with $10 million identified enterprise-wide. 

In another example, a manufacturer of cardboard boxes wanted to understand the machine operating conditions that drove energy spikes during production runs, thus increasing costs. Sight Machine integrated output from energy meters along the line with production data from specific machines, and determined the right settings to optimize both energy use and product output. The improvements generated $15 to $20 million in new value across 19 plants.

For a large manufacturer of aluminum cans, Sight Machine automated the process of integrating machine sensor, downtime, and quality data, and then created a system-wide model of the entire line. By analyzing the blended data, Sight Machine was able to understand the linkages between parameters on upstream assets and quality outcomes further down the line – – enabling the line to achieve 4% higher throughput without appreciably increasing scrap. The net results: $2 million greater annual revenue per facility, with a cost savings of 5% per can.

Figure 2: The Sight Machine Solution for Packaging Manufacturers

Production Data in Manufacturing

To learn how Sight Machine can deliver unprecedented optimization for your production lines and plants, visit and request a virtual live demonstration.

*  Packaging Strategies, “The Power of Packaging: 2018 State of Industry Report


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Picture of Jamie Ulrich

Jamie Ulrich

Jamie is a Senior Continuous Improvement Manager at Sight Machine. He helps our manufacturing customers drive improvement in production processes and systems through our manufacturing data analytics platform.

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