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With the arrival of Industry 4.0 and the Industrial Internet of Things (IIoT), a digital transformation is currently underway. Manufacturers have used data analysis to improve efficiency and advance their market share for many years. But the most significant change today is how data is collected. Many companies still use fragmented, traditional methods for data capture, with staff manually checking and recording factors, filling forms, and writing down operation and maintenance histories for the machines on the floor. Unfortunately, these methods are highly inaccurate due to human errors. They are also time-consuming, open to bias, and do not generate the quality of analysis required for accurate decision-making.
Equipment connected through sensors and edge devices feeds massive volumes of data to cloud-based analytics platforms that can analyze and understand data faster than human perception. This data can then be used to drive real-time decision-making and significant process improvement throughout the company. With accurate and real-time data, manufacturers can make better, faster decisions
Automated machine data collection is driving the next generation of manufacturing analytics, unlocking a myriad of advanced analytics use cases in manufacturing that range from simple monitoring and diagnosis to predictive maintenance and process automation.
In manufacturing analytics, data capture that records events can be leveraged to increase equipment utilization, reduce cost, drive process improvement, reduce human-based errors, and do so at a depth that reveals accurate machine conditions and trends in production.
By applying analytics, real-time data can be leveraged to do more than prevent breakdowns. It can predict with high accuracy the likelihood of a breakdown and the moment it will occur. This reduces costs by allowing technicians to perform repairs at the machine’s optimal time and stage parts. This reduces overall downtime and increases productivity.
Demand planning can be complex. With the addition of data science methods, end-to-end control of the supply chain can be used in conjunction with real-time shop floor data to better manage purchasing, inventory control, and transportation. Highly accurate demand plans can be generated that identify trends that would otherwise go unnoticed.
With a better understanding of how long it takes to make parts, how long job runs will take, and the expected costs and profit of a given job, manufacturers can better estimate their need for material to improve planning.
Cycle times play a major role in pricing. And knowing precise times for part creation and the associated costs allows for accurate cost models and optimized pricing strategies. Setting them too low reduces profitability while setting them too high may impact demand. An advanced analytics platform for manufacturing can bring this data forward to ensure prices are set appropriately. MachineMetrics can help manufacturers optimize their job standards to ensure accurate cycle times.
For many manufacturers, warranty support can be a drain. Often, warranties consist of a “one-size-fits-all” approach that’s more general. This allows uncertainty and unexpected problems into the equation.
By applying data science in manufacturing and capturing information from active warranties in the field, products can be improved or changed to reduce failure and therefore cost. It can also lead to more informed iterations for new lines of products to proactively avoid field complaints.
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