Manufacturers are under tremendous pressure to do more with less — between workforce challenges, supply chain issues, rising shipping costs and bottlenecks, it is almost impossible to keep pace with demands and expectations. With all the hype around Industry 4.0, the Industrial Internet of Things (IIoT) and artificial intelligence (AI), we have seen a huge investment in digital manufacturing technology with continued market growth expected to top 16% as manufacturers scramble to deploy digital manufacturing and automation technologies in the hope that these will solve some of their resource challenges.
But, the reality is that rushing headlong into a complete transformation can be an ineffective and costly mistake when it is done with no regard for real measurable value, ROI or alignment with business goals. And, most importantly, you do not have to boil the ocean to make an impact. Instead, the biggest ROI comes from implementing practical solutions that deliver quick, quantifiable improvements based on a pragmatic and intentional roadmap.
Digital manufacturing can certainly drive business benefits, including up to 50% reduction in downtime and 30% improvement in labor productivity and throughput, according to McKinsey & Company. But it is most effective when implemented with a realistic, measured approach and a clear understanding of operational objectives and business goals. Here’s how:
1. Identify and meet the KPIs that matter most. Adopting digital manufacturing is not about embracing technology for technology’s sake. Organizations must start out with a clear understanding of what business problems need to be solved across their processes, labor, suppliers, materials, etc., and then decide where digital technology can deliver the greatest and most immediate impact. For example, if you are measuring how long it takes to retool a machine, but that only happens once per quarter, is it really that critical to your operations?
Deploying KPI management tools allows you to first establish a baseline of meaningful intelligence and use that to pinpoint focus areas for improvement. Then, as you implement changes and automate processes, you can track progress against KPIs, solving one problem before moving onto another. This “prove and move” approach is much more effective and sustainable than broadscale implementation all at once.
2. Use integrated data to get a clearer picture. In most organizations, the intelligence needed to inform process improvement is scattered across multiple silos: Manufacturing Execution Systems (MES), financial systems, Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), etc. Digital manufacturing solutions that bring this data together into one view provides complete visibility into how various business functions interact, and how changes in one area impact performance in others.
This insight can help companies determine where new implementations can pack the greatest punch by setting targets and goals that move the right needles in the right direction. For example, to improve quality control and reduce defects, video technologies can detect and alert staff to out-of-compliance conditions, so that they can address issues before they become problems. Or, if the aim is to improve on-time delivery, examining process data can help identify a small change to a material handling procedure that might dramatically accelerate productivity on down the line.
3. Leverage prescriptive analytics to zero in on the resolution. While some process changes may be small — simply moving the staging location of raw materials to streamline machine loading, for example — others can be more risky, difficult or expensive, like purchasing new equipment or hiring more staff.
Prescriptive analytics technology can help leadership make decisions based on a better understanding of the projected result. By using the software to identify probable solutions and run various what-if scenarios, companies can make much clearer, data-driven decisions to lower the risk and improve the result. Now, you can see the anticipated outcome before investing in new equipment, hiring more staff or upending processes that could significantly disrupt productivity. It can also enable continuous improvement by providing the management team with recommended guidance in how to correct missed or nearly missed targets.
4. Automate where it makes sense. Too often, companies hear about the benefits of automation and they set about trying to automate everything. But not every process is a good candidate for automation, so apply it only where it makes sense. In one instance, a company paid more than $70,000 to install a robotic material movement system to move parts about 10 yards. While it may eventually pay for itself, the company could have achieved far greater ROI with a more strategic investment.
For example, by measuring productivity and throughput on equipment, you can understand how various factors impact performance and automate their optimization. Say you discover the optimal operating conditions for a particular machine are within specific temperature, speed and vibration specs. Rather than having an operator stand by to make minor adjustments, you could install machine learning sensors and program them to make these control decisions automatically and in real-time. Or, you might discover that specific conditions precipitate machine failure or downtime. You could implement a maintenance alert system that triggers an alert and work order when equipment falls out of acceptable range, so that staff can respond proactively instead of waiting for a failure. This can substantially reduce downtime and improve throughput and productivity to drive growth.
5. Accelerate onboarding and training. Whether it is a new hire or cross-training, bringing employees up to speed on new job roles or processes takes time, and in many cases it’s a double investment in labor — both the trainer and the trainee are on the clock, but only one is producing. But it is also essential for optimal quality assurance and future productivity.
Implementing technology like a digital standard of work allows you to document optimal task completion and use that standard in two ways: 1) to deliver on-demand training to employees, which accelerates time to peak productivity while lowering training costs; and 2) to monitor their performance of tasks to spot anomalies and deliver recommendations and corrective actions in real time, which reduces errors and improves quality assurance. Both of these applications can have a direct impact on business growth.
Certainly, manufacturers must evolve in order to stay competitive. But with so many pressures coming to a head all at once — workforce shortages, supply chain disruptions, etc. — it can be very tempting to go all-in with digital implementation. And with so many options, it is easy to get caught up in the process of purchasing technology and overlook doing a thorough evaluation for whether it actually meets your needs. Not to mention, even with the best technologies on board, companies must also be willing to take action, to make the changes needed to drive incremental improvements and impact, which can be a hurdle in many organizations.
Approaching digitization with an ROI-focused mentality can help organizations focus on value creation, rather than just implementing technology for the sake of doing so. Remember that digitization is not the end goal, it is a journey — a means to an end, which should be improving business growth. That means staying focused on operation objectives with clearly defined goals and investing wisely.