I’ve been designing and implementing shift patterns for decades, across manufacturing, food and drink, security, logistics and other multi-site operations. No matter the industry, I see the same issue repeatedly.
Most shift patterns are built around assumptions that no longer reflect how the business actually operates.
In some cases, those assumptions were never accurate in the first place.
The flat shift pattern demand assumption
One of the most common mistakes is designing a shift pattern around a flat demand profile.
On paper, it looks logical and controlled. In practice, demand is rarely flat. In manufacturing environments in particular, production levels vary across the year. Seasonality affects volumes. Internal supply chains shift. Maintenance programmes disrupt output. Absence fluctuates. Yet the shift pattern often remains static.
That mismatch creates inefficiency in two directions. Either there are not enough people at the times demand peaks, or there are too many people scheduled when workload drops.
Consistently having too many people on shift can be more expensive than occasionally using overtime to cover peaks. If you permanently overstaff to cover variability, you are effectively paying that premium every day, not just when demand requires it.
Many organisations design around a fear of overtime rather than a clear understanding of cost patterns across the year.
Designing shift patterns around history rather than evidence
Another issue is that shift patterns are often inherited rather than properly reviewed.
A pattern might have been introduced years ago under very different operating conditions. Over time, product mix changes, opening hours extend, customer expectations increase and technology evolves. The shift structure remains largely untouched.
When I go into a site, the first step is always understanding reality. We look at demand profiles over time, absence data, seasonality, internal transfers of production and known peaks. Only once you understand what you are trying to achieve can you sensibly evaluate whether the current pattern supports it.
If you do not measure where you are, you cannot know how far away you are from an optimal solution. Making change without that understanding often creates disruption without delivering meaningful improvement.
Matching shift pattern demand without creating chaos
Another trap is trying to match staffing exactly to a fluctuating demand profile at every point.
If you attempt to mirror every rise and fall in workload, you quickly end up with highly irregular and unpredictable shift patterns. That may look efficient in a spreadsheet, but it does not work well for employees who need stability and routine.
The objective is not to chase every short-term fluctuation. It is to design a pattern that broadly follows the demand curve while remaining workable and acceptable for staff. That balance requires judgement as well as data.
Minimum staffing is not the same as optimal staffing
Many organisations talk about minimum staffing levels, but minimum depends on what you are trying to protect.
Minimum to maintain service? Minimum to avoid penalties? Minimum to meet safety standards?
There is an important distinction between critical minimum staffing and optimal staffing. Some businesses overstaff to avoid perceived risk. Others operate permanently on the edge and rely heavily on voluntary overtime or agency staff to close gaps.
Neither approach is sustainable in the long term. Critical minimum staffing needs to be based on a proper understanding of demand variability, skills requirements and operational constraints. It should not be based on habit or fear.
Seasonality and the role of annualised hours
In industries such as food and drink manufacturing, seasonality is built into the operating model. Soft drinks manufacturers see higher demand in summer. Cheese production often increases before Christmas. In other cases, internal production shifts between countries create predictable surges in workload at specific sites.
If a shift pattern is designed as though demand is constant throughout the year, inefficiency becomes embedded in the structure.
Annualised hours can be a powerful mechanism for managing this kind of variability. When designed properly, they allow hours to flex across the year while providing employees with income stability. However, they must be structured carefully. Contractual arrangements, payroll implications and minimum wage compliance all need to be understood and built into the model.
Annualised hours are not a quick fix. They are a structural tool that must align with the organisation’s real demand profile.
Compliance and the reality behind the legislation
When Working Time Regulations were first introduced, there was significant concern about their impact. In practice, much of that fear was driven by misunderstanding.
If employees are averaging more than 48 hours over 17 weeks, that is usually an indication of a deeper structural issue in workload or staffing levels. The regulation itself is rarely the root cause of the problem.
We are now seeing similar uncertainty around the Employee Rights Bill, particularly around notice periods for cancelling shifts and the right to stable contracts. Depending on how these elements are finalised, they may have material implications for highly flexible or on-call models.
Shift design always involves trade-offs. I often describe it as a balloon. If you push one side to improve something, there will be an impact elsewhere. A later start time may mean a later finish. Reducing weekend working may require adjustments elsewhere in the schedule.
Understanding those consequences in advance is part of good design.
The role of technology and human judgement
Technology has an important role to play. Systems can measure current performance, highlight mismatches between staffing and demand and support day-to-day optimisation.
However, the initial design of a shift pattern still requires human understanding. It requires conversations with managers and staff to understand what is happening operationally and what constraints exist in practice. Individuals often have deep knowledge of their own area, but not always visibility of the wider picture.
Software can optimise within a framework. It cannot decide what that framework should be.
Challenging the underlying assumptions
The most significant assumption I see is that shift patterns are simply operational tools.
In reality, they are strategic structures. They shape the cost base, influence compliance exposure, affect employee satisfaction and determine how resilient the organisation is to demand shocks.
Before making small adjustments to rotas, leaders should ask themselves whether the assumptions underpinning the entire pattern are still valid. Has demand changed? Has the workforce changed? Have contractual or legislative conditions shifted?
If the foundation is outdated, no amount of minor tweaking will resolve the underlying issue.
In most cases, the problem is not the people. It is the pattern that was built around assumptions that no longer reflect reality.
