Why Isn’t Factory Productivity Improving? Which Processes Should Smart Manufacturing Target First?

Manufacturers today face growing pressure from shorter lead times, high-mix low-volume production, labor shortages, and rising production costs. As a result, improving productivity has become a major operational priority. When production capacity cannot keep up with demand, the most direct response is often to add equipment, expand production lines, hire more workers, or extend working hours in the hope that greater resource input will lead to higher output. However, adding equipment does not always result in a proportional increase in capacity. Employees may become busier while delivery delays and production bottlenecks continue to persist.

In many cases, the real constraints on productivity are not insufficient equipment or labor, but equipment downtime, waiting time, changeovers, information gaps, quality issues, and process bottlenecks hidden within day-to-day operations. When manufacturers lack timely and complete production information, it becomes difficult to determine where capacity is actually being lost. As a result, “process inefficiency” may easily be mistaken for “insufficient production resources.”

This is one of the reasons Smart Manufacturing has become increasingly important to manufacturers. At its core, Smart Manufacturing is not simply about introducing more automated equipment. It is about creating stronger connections among production equipment, systems, people, and data. Through technologies such as sensors, connected equipment, the Industrial Internet of Things (IIoT), Manufacturing Execution Systems (MES), artificial intelligence, and data analytics, manufacturers can improve the timeliness and transparency of production information and gain a clearer understanding of actual equipment and process conditions.

Therefore, when factory productivity has remained stagnant for a long period of time, the first question should not be, “How much more equipment do we need?” but rather, “Which processes are currently limiting production output?”

Why Can a Factory Look Busy Without Becoming More Productive?

A busy shop floor does not necessarily mean that all available time is being converted into productive output. A factory may have machines running, employees moving around, and work orders being processed throughout the day, yet many activities may still fail to directly create value. These may include waiting for materials, waiting for equipment, waiting for quality approval, resetting machines, handling abnormalities, transporting work-in-process, or performing rework.

Individually, these issues may cause only short delays. However, when they occur repeatedly across different machines, shifts, and processes every day, the accumulated impact can result in significant capacity losses. More importantly, if production conditions are primarily tracked through paper reports, manual records, or end-of-shift summaries, managers may only see final output figures without clearly understanding where production time was actually lost.

In its Smart Manufacturing research, the National Institute of Standards and Technology (NIST) emphasizes that the value of manufacturing data lies not only in collecting it, but also in creating an information loop through sensing, transmission, analysis, communication, and action to support manufacturing decisions. In other words, even if a manufacturer already has large amounts of data, it will still be difficult to improve shop-floor operations if that data cannot be analyzed in time and translated into action.

For this reason, the first step toward improving productivity through Smart Manufacturing does not necessarily have to be full-scale automation. A more practical starting point is improving process visibility. Manufacturers first need to understand how long equipment is actually operating, why downtime occurs, where production bottlenecks form, how long changeovers actually take, and how much rework and scrap are caused by quality problems. Once these previously hidden issues become measurable through data, manufacturers can begin to identify which processes deserve improvement first.

Start With Equipment Downtime to Identify Where Capacity Is Really Being Lost

Equipment is one of the most important production assets in manufacturing, but owning equipment does not mean that it is continuously generating productive output. Downtime can occur because of equipment failures, material shortages, changeovers, machine adjustments, waiting for operators, quality inspections, or scheduling issues. If a manufacturer records only daily production output without distinguishing between running time, idle time, and downtime, it becomes difficult to determine where capacity is actually being lost.

Smart Manufacturing technologies such as connected equipment, programmable logic controllers (PLCs), sensors, and IIoT systems can automatically collect equipment status, production speed, downtime, abnormal signals, and process parameters. Compared with relying solely on manually completed production records, continuously collecting equipment data gives manufacturers a more detailed view of how machines are actually being used throughout the day.

For example, a company may initially believe that a particular machine lacks sufficient capacity and therefore consider purchasing additional equipment. However, further analysis of shop-floor information may reveal that the existing machine spends a significant amount of time waiting for materials, undergoing changeovers, or dealing with short interruptions. In that case, the real improvement opportunity may not be adding another machine, but improving material delivery, changeover methods, production scheduling, or shop-floor workflows.

This is also an important difference between Smart Manufacturing and traditional equipment investment thinking. Instead of asking only, “How fast can this machine produce?” manufacturers can ask, “How much of the available time is this machine actually producing effectively, and what factors are consuming its usable capacity?” Once equipment conditions become more transparent, companies can make better-informed decisions about whether they should improve processes, adjust production schedules, or add new resources.

The Production Bottleneck May Not Be the Busiest Machine—The Entire Process Must Be Considered

Increasing the speed of one machine does not necessarily increase the output of an entire production line. Consider a production line that includes machining, cleaning, inspection, and packaging. If the machining equipment has significantly greater processing capacity than the downstream inspection station, increasing machining speed will not necessarily increase final output. Production will still be constrained by the inspection process, and additional work-in-process inventory may accumulate instead.

This is why productivity improvement should not focus only on individual machines. Manufacturers need to evaluate the entire production flow to identify the true bottleneck. In the past, bottlenecks may have been identified largely through the experience of shop-floor supervisors. However, as high-mix low-volume production becomes more common and products, orders, and schedules change more frequently, the process limiting overall output may also change under different production conditions.

Through MES, equipment data, and other production management systems, manufacturers can gradually integrate information such as cycle time, actual output, equipment status, waiting time, and work in process (WIP) across different workstations. When managers can view production conditions across the entire process, it becomes easier to identify where semi-finished goods repeatedly accumulate, which workstations consistently take longer than expected, and which processes frequently cause downstream operations to wait.

In this context, Smart Manufacturing does not directly determine how a company should improve its operations. Instead, it provides more complete and timely information so that limited improvement resources can be focused on the processes that are truly constraining overall output.

As High-Mix Low-Volume Production Increases, Changeover Time Can Become a Hidden Productivity Loss

Market demand is increasingly shifting toward greater product variety, shorter lead times, and higher production flexibility. As a result, many manufacturers need to perform line changeovers, die changes, equipment parameter adjustments, and material preparation more frequently. When production batches become smaller, the proportion of time spent on actual production may decrease, while changeover and preparation activities take up a larger share of total production time.

If different operators or shifts use different changeover methods, the actual time required may also vary. Without continuously recording changeover time, it can be difficult for manufacturers to determine whether delays are caused by equipment setup, die replacement, material preparation, or waiting for confirmation from other personnel.

Digital production records allow manufacturers to compare actual changeover times across products, machines, and shifts and identify repeated waiting periods or unnecessary steps. These data can also be combined with standardized work and other process improvement methods to determine which activities can be prepared in advance, which settings can be standardized, and which steps still rely heavily on individual experience.

This demonstrates that Smart Manufacturing and traditional process improvement are not substitutes for one another. Digital technologies can make previously difficult-to-observe problems more transparent, while process improvement still requires manufacturers to redesign work based on actual operating conditions. Only when data and process improvement work together can Smart Manufacturing be translated into real productivity gains.

Delayed Production Information Can Also Slow Down Problem Resolution

Many manufacturers already possess large amounts of production data. The real issue is often that the information arrives too late. For example, operators may first complete paper production records, supervisors may review them after the shift ends, and managers may not learn until the following day that a machine experienced a long period of downtime or that actual output for a work order fell below plan.

The biggest limitation of this management approach is that even when a problem is eventually identified, the best opportunity to respond may already have passed. One of the most important changes introduced by Smart Manufacturing is the ability to reduce the time gap between “a problem occurring on the shop floor” and “management receiving the information.”

In its research on Smart Manufacturing systems integration, NIST also identifies technologies such as the Industrial Internet of Things, artificial intelligence, Big Data Analytics, and Model-Based Engineering as important areas in the development of Smart Manufacturing integration. As information from equipment, the shop floor, and management systems becomes increasingly connected, manufacturers can gain more timely visibility into production conditions. Data can then be used not only for historical reporting, but also to support operational decisions as events occur.

For example, if actual output on a production line begins to fall behind schedule, managers can immediately investigate whether the cause is equipment downtime, material shortages, or longer process times rather than waiting until the end of the shift to discover the shortfall. In this context, the value of Smart Manufacturing is not merely making a factory “more digital,” but helping manufacturers gradually shift from retrospective reviews toward more real-time management.

Quality Issues and Rework Also Consume Effective Production Capacity

Increasing production speed does not necessarily mean improving productivity. If a production line completes a large number of products quickly but some require rework or ultimately fail to meet quality requirements, the manufacturer still needs to reinvest equipment capacity, labor, and time to process them again. From an operational perspective, what matters is not how many processing steps a machine completes, but how many products ultimately move to the next process or reach the customer in a condition that meets requirements.

Quality, therefore, is an integral part of production efficiency.

Smart Manufacturing allows manufacturers to gradually connect equipment conditions, process parameters, and quality results. For example, companies can observe whether changes in specific machine parameters correspond with changes in product quality, or whether meaningful quality differences exist across different machines, shifts, or production conditions.

When quality information and process data can be integrated, manufacturers have the opportunity to move from discovering defects only after production is complete toward identifying process deviations earlier during production. This can support quality management while also reducing the capacity consumed by rework, scrap, and repeated processing.

For this reason, the goal of Smart Manufacturing should not simply be to “make machines run faster.” It should be to generate more effective output from limited equipment, labor, materials, and production time.

Moving From Reactive Repairs Toward Equipment Condition Management

In addition to process delays and quality issues, equipment failures are another major factor affecting productivity. When a critical machine unexpectedly stops operating, the impact may extend beyond that piece of equipment. Upstream and downstream processes may have to wait, work-in-process inventory may accumulate, and production schedules and delivery performance may be affected.

With the development of connected equipment and sensing technologies, manufacturers can continuously collect data such as vibration, temperature, pressure, current, and other equipment signals. Combined with historical equipment and maintenance data, this information can help manufacturers observe changes in machine condition. Predictive Maintenance is one common Smart Manufacturing application, with the core concept of using equipment and operational data to improve visibility into equipment health.

This does not mean that manufacturers can predict every equipment failure. Rather, maintenance teams can gain access to more information, identify abnormal trends earlier when appropriate, and schedule inspections or maintenance based on actual equipment conditions. As equipment management evolves beyond purely reactive repairs and incorporates more real-time condition information, manufacturers may be able to reduce the impact of certain unplanned stoppages on production.

This represents an important evolution in Smart Manufacturing—from “understanding what is happening now” toward “using data to identify potential problems earlier.”

How Global Lighthouse Factories Use Smart Manufacturing to Address Real Operational Needs

Whether Smart Manufacturing truly creates value ultimately depends on business and operational outcomes. The World Economic Forum’s (WEF) Global Lighthouse Network continues to document cases of manufacturers using artificial intelligence, advanced analytics, automation, and other Fourth Industrial Revolution technologies to improve operations. The focus of these cases is not simply on how many new technologies companies have adopted, but on whether digital transformation creates measurable effects in areas such as productivity, quality, cost, lead time, and sustainability.

Schneider Electric’s Shanghai factory is one such example. According to the World Economic Forum’s Global Lighthouse Network case study, the factory faced an operating environment characterized by increasing order volumes and growing SKU complexity. It responded by applying digital solutions including automation, machine-learning-based prototyping, intelligent production planning, and generative AI maintenance across different operational processes.

According to WEF, the Schneider Electric Shanghai factory achieved a 63% improvement in speed-to-market, a 67% reduction in make-to-order lead time, and an 82% increase in labor productivity. These results reflect the factory’s specific transformation context and implementation conditions and do not imply that every manufacturer adopting the same technologies will achieve identical improvements.

What makes the case more relevant to other manufacturers is the way technologies were connected to specific operational problems. Machine learning, intelligent planning, and generative AI were not implemented as isolated technology projects. Instead, they were applied to concrete areas such as product development, production planning, and equipment maintenance.

Therefore, when evaluating Smart Manufacturing, manufacturers should not begin by asking, “What is the most popular technology right now?” A more important question is, “Which operational problem needs to be improved first?”

Smart Manufacturing Does Not Have to Begin With the Largest Investment

When manufacturers begin exploring Smart Manufacturing, it is easy to focus on technologies such as AI, IoT, Digital Twins, MES, Cloud platforms, Machine Vision, and robotics. However, if a manufacturer has not yet clarified which operational problem needs to be improved, introducing a large number of technologies may not directly translate into higher productivity.

A more practical approach is to begin with the problems that have the greatest operational impact. If equipment frequently stops, the first step may be to establish records of equipment status and downtime causes. If production regularly fails to meet targets, the company can first analyze equipment utilization, process time, and bottlenecks. If changeovers take too long, it can begin by recording changeover processes across different products and shifts. If quality abnormalities occur frequently, quality results can gradually be connected with process parameters.

Manufacturers can also establish clear and measurable improvement objectives before implementation, such as reducing unplanned downtime, shortening changeover time, increasing good output, or reducing the time between an abnormal event and management receiving the relevant information. Compared with technology-centered objectives such as “implement IoT” or “build a smart factory,” operationally focused goals make it easier to determine whether Smart Manufacturing investments are creating real value.

This problem-driven approach also supports a gradual transformation strategy. A manufacturer can begin with one critical machine, one bottleneck production line, or one recurring operational issue. Once the company confirms that data and technology are creating measurable value, the approach can then be expanded to other equipment and production processes.

The Core of Smart Manufacturing Is Not More Technology, but Continuous Operational Improvement

Smart Manufacturing is often associated with emerging technologies such as artificial intelligence, robotics, Digital Twins, IoT, and automation. For manufacturers, however, what matters most is not how many advanced technologies a factory has adopted, but whether those technologies solve real operational problems.

When productivity remains stagnant over a long period, manufacturers first need to understand where capacity is being lost. The cause may be equipment downtime, production bottlenecks, changeover time, information delays, quality abnormalities, or information gaps between equipment and production processes. One of the most important capabilities of Smart Manufacturing is making these previously hidden issues measurable, visible, and analyzable through data, allowing manufacturers to identify problems more quickly and make better-informed decisions about improvement priorities.

For this reason, Smart Manufacturing does not necessarily need to begin with large-scale automation. For many manufacturers, a more practical path may be to start with one clearly defined operational problem, establish the necessary data, improve the process, and then gradually expand the approach to other production areas.

Ultimately, the goal of Smart Manufacturing is not to build a factory that simply “looks smarter.” It is to establish a production system capable of continuously identifying problems, responding quickly to change, and improving operational performance. Once manufacturers have improved the transparency of equipment and production processes, the next issue that cannot be overlooked is quality. Even if production lines operate faster, effective output will remain limited if process variation, rework, and quality problems continue to occur. Therefore, using Smart Manufacturing to reduce process variation and quality abnormalities will become another important step in strengthening manufacturing competitiveness.