Production Efficiency, Automation, and IoT

Every manufacturing company pursues optimized production efficiency as its primary goal.  It is one of the oldest and most common terms used. But what does production efficiency mean in the era of Industry 4.0 and the Internet of Things (IoT)?  The answer lies in the various iterations of the industrial revolutions of the past and they all have one thing in common. From the introduction of steam power to the advent of mass production and the assembly line to the dawn of the computer age in the 70s, they all were aimed at directly impacting the mechanical processes of manufacturing and the capability of the worker in a way that increased throughput and productivity.  Even the introduction of computers and the automation of the last thirty years was done largely to automate back office functionality and perform administrative and planning tasks to allow resources to be more focused on the production floor itself.

Industry 4.0 and the Internet of Things is a departure from those revolutions based on two important distinctions.  For one, as processes have been improved through methodologies such as lean and Six Sigma, less and less opportunity exists to deliver large improvements and deep cost savings because of those methodologies.  Their success has seen the improvement of manual processes reach the point where little can be gained by human influence at the shop floor level. And secondly, as automation has increased through the rise of robotics, robots and machine automation to various degrees, the data generated that could deliver deeper value and improved process is out of the reach of human understanding to analyze and organize into actionable strategies.

Automation and IoT Impact on the Shop Floor

Along with the possibility of full automation at the shop floor level, IoT technology and software have emerged that allow the management of the data generated by this automation and allows it to be turned into actionable strategies that can again trigger new leaps in production efficiency.  Using sensors attached or embedded in production equipment, this software can even make autonomous decisions within defined parameters to improve production efficiency even further. And the technology has advanced to the point where it can take advantage of new capital equipment with IoT connectivity built in while also using data from legacy equipment with IoT devices retrofitted.  The result is extended lifespan of older equipment and a full picture of the factory floor.

Here are some of the ways automation and the deployment of IoT technology can improve production efficiency on the shop floor:

• Visibility – Manufacturing equipment with embedded or retrofitted devices give operators and managers complete visibility over the production floor.  Tasks do not need to be visually verified to know whether they are complete, and if the system shows a drop-off, resources can be deployed automatically to bring the equipment back up to speed.  This visibility allows resource deployment and staffing to be managed at a more precise level than before.

• Labor – Automated equipment completes numerous tasks quickly.  This reduces labor and improves the quality of the product.  It also means less operators and technicians can handle larger work assignments as well.  When tied into IoT software and deep analytics offered by service providers, this division of labor can be adjusted based on the complexity of the run and the amount of “hands on” functions an operator must do.  Previously, lot complexity and adjustment of job area responsibility was a manually scheduled function usually done by management.

• Lot Accuracy – With automation tied to IoT technology, lot accuracy is improved.  This may be based on more precise unit counts, narrower acceptable variances, more accurate measurements for goods produced by linear foot or pound or any number of other metrics.  With devices measuring production and monitoring conditions, overages and short runs are eliminated and raw materials purchasing, and raw material inventory is more precise.

• Automated Decision Making – With the deep analytic capabilities of IoT software, decision making for many functions previously left to operators, technicians or managers can be done automatically by the system itself.  

  For example, in a factory environment where four machines are running lots of 5,000 lbs., each requiring a two-hour changeover upon completion, IoT devices and analytics can work together to determine that several machines may finish their lots at the same time and that by doing so, labor resources will be stretched, and changeover time will increase.  With automated decision making, the system could determine the optimal changes that will result in the least down time and increase lot size on some equipment, reduce it on others and leave others unchanged to spread the changeover times to match staffing and labor resources required to complete them.

• Objective Decision Making – In traditional manufacturing, shop floor scheduling is done by a planning group with a degree of input from manufacturing managers.  As a result, tribal habits, culture and manager preference can enter in. Today’s IoT software and analytics can often be tied into planning, MRP and ERP systems to optimize scheduling.

  For example, a factory may have traditionally scheduled heavier lots near transit areas or doors to make movement from department to department in the downstream easier for staff.  However, with machine learning and AI applications, data may be analyzed to determine that the convenient placement of the lots may not be the most efficient based on data obtained from all equipment to place the lots on the optimal equipment for the best production.

Automation and IoT Impact in Production Support

In traditional manufacturing environments, areas in support of the shop floor are generally considered overhead.  Departments such as quality control, maintenance, supply chain and others operated with backward looking data based on what had happened.  With IoT technology and the deep analytics of its supportive software, these areas are now able to use real-time data and predictive analysis to proactively adjust or respond as the run is in process.  This has resulted in the ability to intervene and act in a way that moves these support areas from simply overhead to value generating entities. Aided by these tools, these support areas can now develop strategies and protocols that improve the overall production efficiency on the shop floor.

Here are some of the ways support functions can contribute to improved production efficiency:

• Maintenance – With automation and IoT analytics, many maintenance regimes are moving away from preventive to predictive maintenance.  Studies show that companies who deploy predictive maintenance can realize up to 25% reduction in maintenance costs.

  However, the impact to production efficiency is just as drastic and companies that use predictive maintenance can improve as productivity by as much as 25%.  Using the same data available for optimizing the production floor, predictive maintenance strategies can be developed to identify potential failure and schedule repair and service during optimum times such as during changeovers or scheduled downtime.  IoT devices monitoring equipment functions can also pinpoint the part or component in question eliminating the “trial and error” style search for failed parts. They can also send alerts to technicians and part orders to supply departments to pull and stage the repair part.  These features cumulatively contribute to improved production efficiency by reducing downtime and overall repair time.

• Quality – Another area that can change its protocols and operating strategy with IoT analytics and data is quality control.  Instead of counting failures based on past performance and recommending improvements or manual monitoring, quality functions can also be built into an IoT framework to use real time data to reduce quality fallout.  

  Some of these actions can be automated so that zones or lines out of spec can be automatically adjusted, or, if the adjustment isn’t effective or possible, to halt the zone without halting the entire line until repair can be made at a more optimum time.  The balance of the line is then automatically tasked with producing the balance so that the run is done with the least amount of quality fallout with no shortages. The net impact is improved production efficiency at the shop floor level.

• Supply Chain – IoT is also aiding in radical changes in supply chain management that improve production efficiency.  This particularly impacts inventory where connected devices within the warehouse can communicate with equipment to allow better JIT production.  And when tied into a completely IoT enabled supply chain system, inventory on hand can be reduced while still having the right materials on hand for required production.  And in factories where the product is complex, work in process tracking using RFID can help balance upstream and downstream production and assembly to eliminate shortages.

The impact of IoT and software that offers advanced data analytics is profound.  It is even estimated that by adopting the technology, companies can not only improve their production, they can gain a competitive edge over those who don’t.  It has enabled additional productivity gains that could not be managed by human intervention because of the volume of data available and has given manufacturing yet another “revolution” to build upon.