Packaging materials manufacturers are faced with increasing challenges. Rising costs of raw material is impacting margin and hurting profit growth, while a lack of talent means hiring, training and retaining staff is becoming increasingly expensive.
At the same time, customer demands means that packaging needs to increasingly not only protect, but also appeal to consumer demands. All while being sustainable.
Using ThingTrax’s Smart Manufacturing Platform, packaging materials manufacturers can use plug-and-play sensors and advanced AI analytics to tackle some of these issues
Connecting machines to an AI platform means manufacturers can start to see exactly what is happening, which machines are working optimally, which need maintaining, where workforces are being deployed and what needs attention. This intelligence can be used to improve both worker and machine productivity and increase efficiency across the entire factory.
When packaging manufacturers use ThingTrax, they get built-in energy usage monitors, allowing them to see where energy is being consumed, what levels they should expect and therefore identify any machinery not working at an optimum level. In doing so, they can improve the environmental impact of their production lies.
By connecting machines and workers, packaging manufacturers are laying the foundations to create smart production facilities. Through the data they collect, they can implement machine learning to establish the environment required to unlock the potential of Real-Time Quality Management, Autonomous Shift Management and Autonomous Guided Vehicles.
A US-headquarter packaging manufacturer needed to improve productivity to offset the rising costs of raw materials. It also wanted to improve the efficiency of its workforce, with talent becoming more expensive and simply hiring more staff a challenge.
Deploying ThingTrax’s Smart Manufacturing Platform with its integrated Smart Vision System, the manufacturer was able to determine which of its factories was most productive, down to individual machines. It also built a matrix of operator skills, identifying gaps and assigning additional training, which meant it could rapidly assign specialist teams when machinery broke down and redeploy other staff to minimise the impact of downtime.
By connecting its machines and workers, the manufacturer has unlocked savings of 60% across its day-to-day running costs, helping to improve its overall margins by combatting the impact of raw material expenditure.