In terms of hype noise levels in technology today, nothing may be more cacophonous than the Internet of Things (IoT). As you are probably already well aware, technology vendors want to connect everything in your personal and professional lives to make things run better. The investment signal in this technology hype noise is in the use cases. For manufacturing, this revolves around the concept of smart manufacturing (or Industry 4.0 if you are based in Europe).

The concepts aren’t entirely new to the industry (e.g., machine-to-machine), but certainly recent technology advances allow us to bring together data capture, connectivity, remote control and, most importantly, analytics to deliver on the promise of cyber-physical manufacturing sooner than you might think. Certainly, our inquiry activity with research clients has been robust on this topic.

As a result of these ongoing industry conversations about smart manufacturing, we recently published a PlanScape report on the subject. This methodology frames the why, what, how and who of an investment initiative. The objective is to give our research clients a jump start on creating an investment plan. In this article, we look at the why of investing in smart manufacturing.

Executives accept the inevitability of a cyber-physical manufacturing future and understand intuitively the competitive value of having responsive operational capabilities, but building the business case from the ground up can be tricky. In order to assist clients in the process, IDC Manufacturing Insights encourages companies to begin with the construct of overall equipment effectiveness (OEE). Those familiar with the metric know it includes considerations for efficiency, utilization and yield.

Efficiency: Beating the Plan

Efficiency is a measure of throughput usually expressed as actual versus expected (or standard). So if the expected throughput of a machine is 10 units per hour and it produces 11, it is considered 110% efficient. Efficiency is obviously important because if a machine can produce more, there are more products to sell without adding new capacity. However, for the purpose of investing in smart manufacturing, efficiency should be thought of in several dimensions beyond just the machine efficiency:

  • Labor efficiency—factory personnel are able to manage more machines.
  • Material efficiency—optimizing set ups and production sequences so less material is sacrificed to scrap.
  • Energy efficiency—optimizing the consumption of energy in the process which may include running the equipment slower.

Collectively, there are opportunities to improve efficiencies across all of these dimensions through the application of smart manufacturing principles.

Utilization: Improving Availability

An efficient machine is useless if it is broken. Manufacturers have often calculated productivity as efficiency multiplied by utilization. Utilization is simply a measure of the cumulative time of operation divided by the total elapsed time. So if our example machine is available 99% of the time, then the productivity is 108.9% (110% multiplied by 99%).

Availability comes down to keeping the equipment running. Asset intelligence—understanding the operating condition, predicting failure, interdicting to minimize disruption—is the common categorization and is integrated to a company's enterprise asset management (EAM) system. There are varying levels of asset management (as shown in Table 1).

Asset management can extend beyond just the operating equipment to include tools, tooling, jigs, fixtures and inspection equipment—all of which need to be available and well maintained. This domain is also a common starting point for many companies to test how to deploy and use smart manufacturing-related technologies as it is less invasive to the production process and can produce quick paybacks.

Quality: Real Time Measurement

The last component of effectiveness is reliability. There has been a lot of progress in transitioning measurement tools and equipment—calipers for mechanical, environmental test chambers for electronics, etc.—to provide digital recording. And the information is getting more complex with visual inspection systems sending calibrated image files for example. Smart manufacturing initiatives will incorporate and integrate those digital readings and other outputs into a more disciplined root cause analysis and corrective action capability.

Improving reliability isn’t just about integrating the data from inspection equipment. One of the capabilities manufacturing companies describe is an ability to support design for experiments (DFE). The concept is that if plant management believes they may have a problem, they can turn on specific pieces of data acquisition and test the results against upper and lower limits, better identifying a set of conditions that may be causing a quality problem. DFE has always been part of the Six Sigma methodologies, but has been a very manual proposition to execute. Smart manufacturing approaches will assist in automating this critical quality function.

Of course, DFE can also extend beyond just testing for quality parameters. Experiments related to asset operation would be available, but perhaps the most interesting could be A/B testing to improve throughput (efficiency). As an example, consider a plant that has two production lines that are equipped in a similar fashion. Operations management wants to decide the best way to calibrate and operate those lines to produce product X. One line uses a set of parameters (e.g., line speed, staffing, material quality)—call that test A—and the other uses a different set (test B), by comparing the results for equipment, material, labor and energy efficiency; the company can choose the best set up.