Put a Box on It: Driving the Bottom Line in Packaging Production

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.
Packaging Production in Manufacturing

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Today’s manufacturers are focused on driving the bottom line by improving the performance of production line operators. With new access to real-time data, line operators don’t have to be a data scientist to make decisions that increase uptime and customer satisfaction.

Recently, Sight Machine has been working with a major packaging company to reduce defects and increase quality in corrugated boxes, as well as improving energy efficiency. The Sight Machine solution offers new levels of visibility into the plant’s production and energy data.

Accurate Track and Trace

One of the most critical use cases for manufacturers is the Holy Grail of quality management: precise track and trace. Sight Machine enables machine data to track down defects by tracing conditions on the line – correlating historical data from weeks or months earlier with real-time data from the manufacturing or distribution value chain.

The Sight Machine platform builds a data pipeline that integrates information from claims, machines, orders, and quality assurance to create a digital twin of each order or batch. Insights are incorporated into role-specific dashboards, providing a “control tower” view of key production variables. Custom workflows drill down and analyze the data using built-in correlation and variance analysis tools, as well as a machine learning algorithm that evaluates production variables on their ability to predict defects. These analytics are integrated into Sight Machine dashboards.

The conditions of a claimed defect – i.e., blistering due to too little glue, or warped board caused by excess moisture– are then tied to the SKU and order number to identify the actual batch of boxes affected and the system at fault. In this way, Sight Machine literally ties the machine to the customer incident to implement a fix.

Improving Customer Satisfaction

Line managers and supervisors have many responsibilities, and while they of course want the customer to be happy, they realistically have specific priorities, such as meeting OEE targets, monitoring busy shifts, keeping costs down, etc.

In this company, the Sight Machine solution has used machine learning to help support customer satisfaction to demonstrate, for example: “This specific interaction is causing a four-times increase in defects.” By showing process engineers the specific problem, they are able to create prescriptive actions that allow operators to fix problems more quickly to keep the customer happy while maintaining daily production and uptime targets.

Managing Energy Use

As well, Sight Machine helps the company reduce energy costs by better managing its peak load under new sustainability mandates.

Here’s how it works: Like many others, the company is charged for power based on use over a 24-hour period. This fee is based on regular sampling from the substation – say, every 15 minutes. But there’s a catch: If the power spikes during one of those 15-minute periods, the company must pay that peak amount as if it was the whole day.

Sight Machine allows the company to examine energy use throughout the plant, including machine parameters, characteristics, and so on, to assess the impact of each power spike. This allows managers to move specific orders to a time when energy is less expensive, reprice some orders, or assess the use of a machine and its settings at peak times. In other words, output can now be more effectively planned around energy costs.

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

Packaging Production in Manufacturing


Picture of Joe Larsen

Joe Larsen

Joe is a Lead Solutions Engineer at Sight Machine. His focus is helping manufacturers utilize IoT, machine learning, and other AI technologies to unlock value and increase productivity in their production operations.

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