“I Need a Nap!” — It’s time to sleep on shift.

Recently I was meeting with a team of union leaders and managers to discuss their shift schedules and our process for evaluating shift schedules and finding better alternatives. One of the things I often do during the introduction part of the meeting is to ask people to tell me what they want to discuss over the next couple of hours. In this meeting, one of the union leaders semi-jokingly said he wanted to know “when is the best time to sleep on shift?”

Well, that turns out to be a good question. Most of us have probably experienced the alertness boost resulting from a short duration nap. Studies have shown that both alertness levels and performance can improve when shift workers are allowed to sleep on night shifts.

Unfortunately, most organizations have no provision for sleeping on shift. The concerns raised are often around the manageability of the naps. Questions like:

  • How do we ensure people come back to work?
  • How do we keep people safe while they are napping?
  • How do we ensure the nap rooms are only used for naps?
  • What about sanitation?
  • You mean you want me to pay someone to sleep!?
  • If someone doesn’t need a nap, do we have to give them an extra break?
  • If one person takes a nap, and another doesn’t, is that fair?

I have some ideas for addressing some of these concerns, though not all of them. To the question about paying someone to nap, my answer is: If a person needs a nap, you can’t afford NOT to pay them to take a nap. A single mistake can cost much more than a 20-30 minute break for a nap. Especially if the 20-30 minute nap time is created by combining a break period and a nap period.

Let’s ignore the “mistake avoided” benefit for a moment and do some quick math:

  • Assume that a person working a 12-hour night shift is given 15 minutes of nap time that can be taken in conjunction with either a normal break or a lunch break. The extra time can only be used in the nap room.
  • If a person uses their nap break in conjunction with their 30-minute mid-shift lunch, they will come back to their workstation with 5.75 hours of work to complete before their shift is over. Since that 5.75 hours includes another paid break, assume that they actually only have 5.5 hours of actual work time remaining. 5.5 hours x 60 minutes = 330 minutes.
  • A 15-minute investment for the nap will require a 15 minutes/330 minutes or 4.5% improvement in productivity to break even.

Is a 4.5% productivity improvement feasible? That probably depends on the situation. If the work is self-paced, tedious, or intellectually challenging, the answer is almost always going to be “yes”. In many cases, the improvement will be significantly more than 4.5%, and the shift worker will be happier and safer.

Call or text us today at (415) 858-8585.

Is Your Shift Schedule Lean?

There are many aspects to the concept of Lean Manufacturing and Lean Thinking.  One of the fundamental goals of applying lean concepts is to eliminate waste in the process. 

What can we do to minimize waste in shift schedules?  In no specific order, here are some places to look:

  1. Match the coverage to the workload
    • A headcount mismatch creates idle time, overtime, and lost capacity (if you are unable to run)
    • Avoid over-staffing to cover absences.
       
  2. Create time for preventative maintenance
    • Make your product right the first time – avoid defects and extra processing resulting from machines that are out of adjustment.
    • Avoid waiting due to breakdowns.
    • Reduce operating costs due to improved equipment efficiency.
  3.  Allow shift workers to get rest (days off, hours/day)
    • Reduce defects due to human error.
    • Feel better, better performance, clearer thinking, and more interest in engagement.
    • Less pacing due to fatigue.
       
  4. Smooth production and create flow using a continuous schedule (24×7)
    • Reduce finished goods and work-in-process inventory.
    • Match production to demand.
    • Find defects when they occur and correct the cause immediately.
    • Maximize asset utilization.
    • One potential risk is the increase in overhead staff because of an increase in the number of supervisors and indirect support personnel.
       
  5. Operate through breaks and lunches
    • Avoid line instability that results in defects and line startup/shutdown costs.
    • Maximize capacity and asset utilization.
       
  6. Insufficient cross-training
    • Waiting to get the right person for the job
    • Not utilizing the potential for on-shift personnel to fill more roles

I’m sure you can come up with more opportunities to add to my list.   

Improving your schedule by addressing sources of waste requires making changes.  Changing schedules is not easy, but it can be done.  Our change process engages the workforce in the schedule evaluation and incorporates their feedback into the best solutions that result in a leaner, more efficient, and productive operation.

Call or text us today at (415) 858-8585  to discuss your operations and how we can help you make your schedule leaner, more efficient and more productive. You can also complete our contact form and we will call you.

What is the Worst Shift to Work? Night Shift? Afternoon Shift?

The night shift is difficult physically, but the afternoon shift can be hard on your family and social life.

In my last post, I talked about shift workers’ preferred shift, which is the day shift, and the implications of that preference on worker satisfaction levels.  An obvious follow-on question to the preferred shift assignment is to understand shift workers’ least-preferred shift.
Over the last 23 years working with shift work operations, I have observed that there is often one least preferred shift at a site, and it is either the night shift (also known as 3rd, graveyard, or sometimes the hoot-owl or hoot shift) or the afternoon shift (2nd or swing shift).  Which shift is least preferred at a particular site is typically driven by the demographics of the workgroup and the work environment.

Here are the overall results from our database of survey responses to the question “What is your least-preferred 8-hour shift?” : Least preferred 8-hour shift.

 

From a sleep management perspective, most shift workers have more trouble getting enough good-quality sleep on the night shift.  This makes it less desirable for facilitating high alertness.  On the other hand, it allows the people on night shift to meet other obligations in their lives like managing childcare, going to school, working a second job, and spending time with their families.

Afternoon shift allows many shift workers to manage their sleep patterns better (second shift workers get more sleep than either day shift or afternoon shift) so they often feel better on this schedule than on a night shift schedule.  The main downside to the second shift is that it requires work during the “prime-time” evening hours when family and friends are available.  For parents, this can be a deal-breaker since it may mean that they almost never see their families during the workweek.

This difference of opinion on the least desired shift is an opportunity when it comes to staffing your shift schedule.  On an 8-hour schedule, it is often possible to give an overwhelming majority of folks either their first or second choice of shift assignments and avoid the least desirable shift.  All it takes is some flexibility in the shift-bid system and sufficient cross-training of the workforce to meet the skill requirements on all shifts.

Call Us and We Can Help

Call or text us today at (415) 858-8585 to discuss your operations and how we can help you solve your shift work problems. You can also complete our contact form and we will call you.

Everyone Wants to Work Day Shift, Right? Think again!

We all know that shift work, and especially night shift, is difficult. For some folks, it can lead to disrupted sleep patterns and non-traditional social interactions with friends and family members. As a result, it is a common perception that, when it comes to shift work, everyone would prefer to work the day shift.

Over the last 20+ years, we have asked thousands of shift workers what their preferred shift assignment is. In an 8-hour, three-shift situation, you can see in the graphic that it is true that most people want to work day shift (71%), but there are also 15% that want to work the afternoon shift and 14% that want to work the night shift. In other words, in typical three-shift operation, you have a majority of people (33% on days + 15% on afternoons + 14% on nights =62%) satisfied with their shift assignment instead of dissatisfied with their shift assignment.
8-hour shift preference

A similar situation exists with 12-hour shift schedules.  When we asked shift workers whether they prefer a 12-hour day shift or a 12-hour night shift, 80% prefer the day shift and 20% prefer the night shift.  So like 8-hour shift schedules, you have a majority of people (50% on days + 20% on nights = 70%) satisfied with their shift schedule assignment.
12-hour shift preference

With these results, we can see that the majority of people do prefer day shift, but far from all of them. And, given that the typical 24-hour shiftwork operation is usually equally staffed on all shifts, or staffed more heavily on day shift than other shifts, we see that the majority of people get their preferred shift assignments even though the majority prefer day shift.

Call Us and We Can Help

Call or text us today at (415) 858-8585 to discuss your operations and how we can help you solve your shift work problems. You can also complete our contact form and we will call you.

Use Your Data — Know Your Business

Over the last 30 years, I have evaluated the operations of hundreds of manufacturing, processing, mining, and service organizations. Three things I have learned from that work:

  1. Companies know their business, but their knowledge is incomplete. They shoot targets, monitor KPIs, and compare their performance to their plans and budgets. But they don’t have time to actually think about their operations and performance.
  2. Companies have lots of data. It is not unusual to ask for real data and have a management team laugh heartily and say: “Oh, we have tons of data. So much data that you will be drowned in data.”
  3. Companies do not look at their data very much. Unfortunately, when asked what their mountain of data tells them about their operations, they go back to their targets and KPIs. They have data, but they use very little of that data because they believe there is too much data to understand what it is telling them.
  4. This lack of data analysis represents lost opportunities to compete more effectively within their industry. Some quick examples:
  • A world-wide, leading mining company was experiencing lower productivity on weekdays than weekends, resulting in more than a 7% (overall) loss in production capacity. Approximately 2% of this lost production was due to necessary operational and maintenance downtime, and the remaining 5% was unexplained. Why was it unexplained? The majority of lost production mid-week was attributed to the planned downtime. Since the available operational data had not been analyzed, there was no way to know that there was a potential to increase output by 5% by understanding and correcting the weekday operating problems. Instead, the problems were seen as insignificant. Imagine the value of improving an operation’s performance by 5% just by implementing procedures that are already in use within the organization.
  • An electronics manufacturer was experiencing over 25% lost capacity, resulting from shutdowns at lunches and breaks, on a board assembly line. In addition and for unexplained reasons, the line productivity ramped up as a day progressed, peaking mid-day on day-shift, and then fell off again as the remainder of the day was completed. This ramp-up/down phenomenon resulted in a 40% additional loss in capacity. In other words, the line capacity could have increased by an additional 65% just by operating continuously at its demonstrated peak productivity level yet no one knew the opportunity existed. Once again, imagine the impact of increasing capacity by 65% without additional investment This may push new capital investment off by years, not to mention the improved labor cost per unit impacts.

In this age of computers, these types of opportunities seem surprising. Yet it is not unusual to run into a management team that does what I sometimes think of as “managing by cocktail napkin”. Each day they face a set of problems indicated by their performance tracking measures but not really understood. They discuss possible solutions over lunch, take some notes on their cocktail napkins, finish lunch, and throw the napkins away because they still don’t understand the cause of the problems. The next day, they go through the same process all over again, never once looking at the underlying data from their operations.

With some effort and the willingness to scale a mountain of data (hundreds of thousands of data points in the cases above), the opportunities are clear. The data is available, but time for the analysis is not invested. The magnitude of these opportunities suggests that investing in this analysis will often result in a very real and fast payoff.

The bottom line here is that companies that do this type of analysis gain a competitive advantage in their industries. Since we do some of this analysis as part of our shift schedule evaluation projects, our clients benefit when they evaluate schedule alternatives. But you don’t need to be changing your schedule to do the analysis. You do need to know how to do the analysis or hire someone that knows how to do the analysis. And you should do that soon because your success is at stake.

I will be writing more about this topic over the coming months, including:

  • Looking at some example data sets and sources.
  • What questions should be asked of the data?
  • Answering these questions using some of the analysis tools already on your computer.

Of course, we are happy to help you if you would like outside help. Call or text us today at (415) 858-8585.