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How Fixed Shift Patterns are costing your organization money

By 15 September 2020 No Comments

What are fixed shift patterns?

Chances are, you are or have followed a fixed shift pattern at one point in your life. In fact, if you are working a regular 9-5 job and are reading this while at work, you are currently following a fixed shift pattern of five individual shifts from 9AM to 5PM, Monday through Friday.

We often don’t think about fixed shift patterns as we simply take our daily work routines for granted. More often, we associate shift workers with industries that need to operate around the clock, 24/7 365 days a year. Examples of such industries are the healthcare sector, where nurses need to work around the clock to care for patients that could be admitted to the hospital at any given point in time. Other industries include manufacturing, public transportation and retail.

A fixed shift pattern, sometimes also referred to as a “repeated shift pattern”,  is simply a routine of working hours that you follow for a specific time interval. Nurses, factory workers, retail assistants and railway inspectors might follow a repeating pattern consisting of blocks of morning, afternoon and night shifts:

Fixed Shift Pattern

Why do organizations use fixed shift patterns?

After reading the title of this article, you might be wondering why the use of fixed shift patterns is so widespread, even though they could cost your organization a hefty amount of money. After all, if fixed shift patterns are bad or inefficient, what could be reasonable explanations why organizations in various industries continue to adopt them? Simply put: Shift plans based on fixed shift patterns are easy to create and follow.

If you have ever planned the working hours for a group of people, it is likely that you have realized how complex it can be. Making changes to one person’s schedule could impact the schedule of another person. The workload might not be distributed evenly, resulting in a sense of unfairness. Ad-hoc changes might be needed, if people report sick for work at the last possible minute. And as the number of people in your schedule increases, so does the complexity of your scheduling problem at an exponential rate. Quite often, generating a shift plan that is operationally viable is a big challenge in itself, such that optimization remains an afterthought.

If you look at the above visual representation of a fixed shift pattern, you quickly realize that every team is working exactly the same shift pattern (Morning ⇒ Afternoon ⇒ Night), offset by one week. The beauty of this fixed shift pattern is that it automatically ensures that we will have one team working the morning shift, one team working the afternoon shift and one team working the night shift. This is helpful, because it will help to ensure that every team is treated equally and will work an equal amount of working hours. However, there might be instances where locking our workforce into a particular pattern might actually be detrimental to the business – something we will discuss in the next section.

Finally, another key benefit of a fixed shift pattern is that they are usually easy to remember and easy to communicate. Once your workforce has seen and understood the fixed shift pattern, you no longer need to update them on a daily basis about changes to their working hours. Fixed shift patterns usually also repeat indefinitely, meaning that you only need to configure them once and they will then take care of the rest, forever. Not having to communicate changes to your workforce on a daily basis not only takes effort off your shoulders, but also decreases confusion and increases your staff’s adherence to their work schedule. So what are some of the gaps that fixed shift patterns have and why do industries with unpredictable demands often choose to go for alternative ways of scheduling their workforce?

The pitfalls of fixed shift patterns

To understand the disadvantages of fixed shift patterns, we need to take a closer look at the term itself: “fixed shift pattern” highlights a crucial characteristic of this scheduling approach: It is fixed – static, rigid and inflexible and often fails to take into account the changing business needs and daily fluctuations in the amount of work that needs to be carried out (i.e. demand).

Once we fix a work schedule to be a certain way, we sacrifice flexibility for certainty. As my finance professor always used to say: –  “Having options is valuable.” – and having a workforce that is fixed and predetermined to follow a certain work schedule eliminates options and often causes inefficiencies in the day-to-day operations.

Let me give you the example from the manufacturing world:

In the automobile industry, factories often operate assembly lines that run seven days a week, 24 hours per day. One of the key reasons for that is that starting and stopping the assembly line can be a very costly undertaking. Surveys with automobile manufacturing executives have shown that stopped production costs anywhere from US$22,000 – US$50,000 per minute. To keep such an assembly line up and running through fixed shift patterns alone can be a challenging task as the number of cars produced and number of employees working on the assembly line can vary on a day-to-day basis. If you adopt a fixed shift pattern consisting of three 8-hour shifts per day, all it takes is that one employee reports sick for work or the need to produce twice as many cars in a day and the entire assembly line will come to a halt or operate sub-optimally. The reason? We have failed to incorporate the dynamic needs of the business into our scheduling exercise.

An even more serious example could be in the healthcare industry, where the number of patients is often seasonal and variable. Imagine you are blindly adopting a fixed shift pattern in a hospital setting and suddenly there is a huge influx of new patients being admitted. As a result, you will be unable to provide the right levels of care to your patients, which could even result in a life-or-death situation.

In summary, while fixed shift patterns are easy to create and follow (there are plenty of free templates available online), they often sacrifice flexibility for consistency, reduce the number of available scheduling choices and fail to take into consideration the actual, changing needs of the business on a day-to-day basis.

The alternative: Dynamic, AI-Enabled scheduling

In order to understand the potential of AI-enabled shift scheduling, let me borrow from a concept from the field of mathematical optimization. In mathematical optimization, there is a concept called “feasible solution” or “feasible region”, which describes all the solutions that fulfill the constraints of the optimization problem. To put this into a shift scheduling context: The feasible region contains all the possible permutations of shift timings for each employee that would not violate constraints such as labor laws (maximum overtime hours per day, minimum rest days per week, etc.).

Let’s assume the following example from the field of Linear Programming to add some life to this example:

We operate a hamburger shop and employ 2 employees, Billy and Bella. Our goal is to prepare as many hamburgers as possible while staying within our budget of $50. Billy makes $5 per hour and Bella makes $10 per hour. Bella is more experienced than Billy and is able to prepare 12 hamburgers per hour and Billy can only prepare 2 hamburgers per hour.

A simple chart can be used to visualize a feasible region (shaded in red) for this optimization problem:

Feasible Region without Constraint

At both extremes, we could either:

  • Engage Bella for 5 hours (5 hours x $10/hr = $50) and Billy for 0 hours
  • Engage Billy for 10 hours (10 hours x $5/hr = $50) and Bella for 0 hours

Alternatively, we could engage Billy for 6 hours and Bella for 2 hours or find any other possible combination below the blue line.The feasible region (all possible combinations that would not cause us to exceed our $50 budget) are all within the red shaded area.

If our goal is to prepare as many hamburgers as possible, we will choose to engage Bella for 5 hours, allowing us to prepare 12 hamburgers/hour x 5 hours = 60 hamburgers.

Now let’s assume that we put Billy on a fixed shift pattern which requires us to engage him for at least 2 hours per week. We can visualize that in the following manner:

Feasible Region with Constraint

As we can see, our feasible region has gotten smaller by the area shaded in yellow. What this means, is that we now have less options available to us when it comes to choosing how many hours of Billy’s and how many hours of Bella’s time we would like to engage.

The optimal strategy has changed: We will engage Billy for the minimum requirement of 2 hours and Bella for 4 hours with our remaining budget, allowing us to prepare (4 hours x 12 hamburgers/hour) + 2 hours x 2 hamburgers/hour = 52 hamburgers in total.

As shown above, putting Billy on a fixed shift pattern has reduced our feasible region and thus led to us being able to prepare less hamburgers (60 vs 52, that’s a loss of 8 juicy, delicious hamburgers!).

The above example is just a simple illustration from the world of Linear Programming where we are comparing the possible combinations when engaging 2 different resources (Billy’s and Bella’s time).

In the real world, problems like these are much more complex, workforces are much larger (often in the tens of thousands) and there are hundreds of considerations that need to be factored in (labor laws, employee shift preferences, supply vs. demand, etc.). With more resources and longer time frames, we will have a much larger number of possible permutations (shift assignments for individual employees) and introducing a fixed shift pattern will cause us to reduce our feasible region.

The best of both worlds? A hybrid approach

Are fixed shift patterns and AI-enabled shift scheduling mutually exclusive? Definitely not. In fact, many organizations that put a lot of thought into the scheduling of their workforce understand that a hybrid approach might be the best fit for them: Combining the simplicity of fixed shift patterns with the enormous potential for optimization that AI-enabled shift scheduling provides.

Examples of that could be putting your staff on a repeated pattern three days a week and filling the remaining two working days as demand emerges. Some organizations negotiate fixed working hours with each employee individually, resulting in a wide range of unique fixed shift patterns, which can come with its own set of challenges. As a general rule, the following table can highlight the pros and cons of fixed shift patterns, dynamic AI-enabled shift scheduling and a hybrid approach:

Fixed Shift Patterns vs. AI-Enabled Shift Scheduling vs Hybrid Approach


Are fixed shift patterns always bad and inferior to dynamic, AI-enabled shift scheduling? Certainly not. In some cases, especially in small organizations, it might make more sense to use a simple-to-follow and straightforward fixed shift pattern. However, as organizations grow and as their scheduling requirements become more complex, the potential for improvement (labor cost savings, better customer service, improved patient care) also grows and it quickly starts making sense to explore how AI-enabled shift scheduling can improve the allocation of manpower resources (supply) to demand (tasks). That being said, fixed shift patterns and AI-enabled shift scheduling need not be mutually exclusive and a balance can be struck by adopting a hybrid approach in order to try to combine the best of both worlds: The straightforwardness of fixed shift patterns combined with the potential for optimization through AI-enabled shift scheduling.


Curious to learn what the best shift scheduling approach for your organization might be? Contact us for a free workforce management consultation.





Florian Parzhuber is a Workforce Management & AI Specialist at Workforce Optimizer, a leading AI-Enabled workforce management solution. Having lived and worked in China, South Korea as well as Europe, he accumulated extensive knowledge on the challenges large enterprises face across the globe. Florian married his interests in technology and social impact at Workforce Optimizer where he hopes to capitalise on the power of machine learning and resource optimization to drive positive social change. He can be reached at

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