In today’s fast-paced world, time is a precious commodity. While machines perform tasks at remarkable speeds, the human workforce often encounters periods of non-machine idle time, hindering overall productivity. Enter digital machine intelligence, a solution poised to transform the way we capture and analyze production performance to alleviate this issue and propelling the payment card issuance and personalization industry into a new era of efficiency and innovation.

What Is Idle Time?

In manufacturing, the widely accepted definition is that idle time is the duration of time we wait while a machine or person, though fully functional, is not productive.

Idle time = scheduled production time – actual productive time.

The key here is the machine is fully functional (no errors, no broken parts, no system down, no communication issues, etc.), but it is not producing output for one reason or another.

Why Is It Important?

The most recent data I have gathered as a business analytics consultant in the card issuance and personalization space suggests that the average availability time for a company without any analytics solution or continuous improvement program ranges from 16% to 38%. This means that anywhere from 62%-84% of the time machines are not running when they could be. Closing that gap is where we see the largest impact when implementing continuous improvement and data-driven productivity gains on the production floor.

What Are Sources of Idle Time?

There are many common sources of idle time shared across all industries, but here we focus on those related to card issuance and personalization centers:

  1. Machine or operator waiting for work or jobs – this issue stems from job planning or an issue with the preceding steps.
  2. Machine or operator not authorized to perform work and waiting on approval – this could be due to quality checks, security checks, supervisor checks, missing job instructions, and so on.
  3. Machine or operator is missing raw materials needed such as consumables and operator materials.
  4. Machine or operator not present – this can happen for a variety of reasons including scheduled or unscheduled break, reassignment to another activity, or absenteeism with no backup resource available.
  5. Machine or operator is on standby due to personal process limitations such as dual custody is required and only one operator is available, missing card needs to be found, a security incident related to the previous job run occurs and an investigation is underway, or machine calibration checks are needed.
  6. The machine is undergoing setup for a variety of reasons including job loading or material loading, machine is getting cleaned or calibrated, or the workstation is getting set up for the job.
  7. External dependencies are at play including a halt to production ordered by the customer, a quality or maintenance lead who initiates immediate work stoppage, a power or network outage, a supplies shortage, external software delays, an emergency evacuation, or any situation where wait times are instituted.

How to Reduce Idle Time

The first step to resolving any problem is to understand the root cause and magnitude. This is the basis of knowledge-based management or Lean Six Sigma manufacturing: Define, Measure, Analyze, Improve, Control.

  • Define: The definitions have been made clear on what idle time is; now the next step is to measure them.
  • Measure: To gauge machine productivity accurately, track scheduled work time and measure actual operational periods, focusing on the cumulative bursts of output like finished personalized products. If a software-hardware solution is not available, your best bet is manual monitoring with a stopwatch for the entirety of the day, ensuring unbiased and unhindered data collection. However, one caveat is that an on-site observer may not pinpoint process and employee-related issues with the utmost precision.
  • Analyze: After obtaining data for a single machine’s scheduled work versus actual work for a day, calculate the idle time. If the idle time exceeds 15% (idle time divided by scheduled work time), there is a problem requiring attention. Achieving the industry average of 15% idle time is possible with the implementation of software-hardware tracking systems along with fostering a continuous improvement culture. Thorough data collection enables the creation of a Pareto chart, identifying the easiest-to-solve issues, or “low-hanging fruits.” Targeting these areas strategically will maximize gains. In cases of insufficient data, a return to the measure phase is necessary to confidently identify root causes. Implementing tools like Ishikawa diagrams helps isolate overlapping causes, guiding resource allocation and energy to the right focus areas.
  • Improve: Identifying the problem is just the beginning; the real challenge is solving it. Now armed with insights into the issue’s source, you can brainstorm solutions – whether it involves process modifications, personnel retraining, or technological changes. Often, this is where an external consultant can offer valuable perspectives, leveraging industry experience and unbiased insights. Once a solution is pinpointed and evaluated, an implementation plan, coupled with a trial and measurement system, ensures fair assessment and feedback from all stakeholders. This initial trial is crucial for the success of the broader rollout. Lack of an experienced continuous improvement engineer often leads to challenges and failures at this stage, making external consultation advisable.
  • Control: After the successful trial and subsequent large-scale rollout, sustaining the solution becomes the most challenging phase. The absence of proper controls, feedback mechanisms, and the ability to steer the process, teams, and operation often leads to solutions unraveling after a few months. Management’s shift in focus and personnel reverting to familiar habits contribute to this breakdown. Control is about identifying any future pains that come up and developing and adapting the solution to overcome these bumps on the road so the gains of the solution can be earned for the long run. The key to process control is to know when it is going out of control, which requires continuous monitoring and data collection every minute of every hour to identify long-term trends. This is where having software running in the background collecting data analytics is key. Without control, processes fall apart.

The Power of Production Analytics

Idle time is by far the largest contributor to operational inefficiencies in card issuance and personalization environments. Many factories are using logbook-based manual measurements to understand what is happening, which can be marred by bias, human error, and lack of detail. Without a software-hardware based analytics solution, many key opportunities to improve go undiscovered. Once these problems have been clearly identified, there is a critical process to analyze the data to identify key root causes. After that, a rigorous and creative solution-generating process must take place with a carefully orchestrated trial preceding a large rollout of the solution. Additionally, any gains from these depend entirely on the factories’ abilities to control and sustain those gains in the long run, which requires continuous data monitoring and solution steering.

Learn more about the Entrust Adaptive Issuance™ Production Analytics Solution and read our white paper for insights into how the Overall Equipment Effectiveness (OEE) framework can be used to aid in operational efficiency.