When is Automated Shift Optimization Worthwhile?

Shift planning seems simple at first glance, but behind every shift plan lies an enormous mathematical challenge with millions of possible combinations.

The Invisible challenge of workforce scheduling

Anyone who has ever created a staff schedule knows that a whole range of different factors must be taken into account for a good result, including:

• Legal and contractual regulations (e.g., labor law, collective agreements, company agreements)
• Employee qualifications and restrictions (e.g., specialist knowledge, availability, working time accounts)
• Operational framework conditions (e.g., car pools, budget restrictions, targets)

However, shift scheduling must not only comply with regulations but also align as closely as possible with actual demand. After all, overstaffing raises operational costs, while understaffing can lead to unattended customers, low morale, and even burnout.

Even a slight mismatch between demand and actual staffing levels can result in significant long-term financial impact. The goal of workforce scheduling has always been to achieve the ideal: perfect balance between staffing levels and demand.

Balancing all of these factors is a monumental task. It’s no surprise that historically, employee preferences were often an afterthought in shift planning, if they were considered at all. Even today, many companies continue to struggle with creating optimal schedules despite utilizing advanced workforce management (WFM) tools.

Modern algorithms are capable of processing massive data sets in real-time and learning from historical trends. And yet, multiple WFM providers continue to promise the “best” algorithm to solve the same scheduling challenges. Why?

Manual shift planning: time waster, cost trap, error magnet

Despite advances in technology, many organizations still rely on manual methods, like spreadsheets, to manage shift planning. And in many cases, what WFM planners can accomplish under these conditions is nothing short of impressive. With enough experience, creativity, and Excel wizardry, they often build surprisingly sophisticated systems.

While this might work in small businesses or simple operational setups, it doesn’t scale. As workforce complexity increases, manual planning becomes a time-consuming, inefficient, and error-prone process. And when shift models get more flexible or rules start to overlap, even the most well-crafted spreadsheets hit their breaking point.

>> Read: 8 Signs That You've Outgrown Excel for Workforce Planning

The number of possible scheduling combinations can explode rapidly. Without an algorithmic approach, the risk of inefficient staffing, higher costs, and demotivated employees increases exponentially.

But why does this happen?

Workforce Scheduling: A Combinatorial problem with huge computational demand

Even seemingly simple schedules with only a few requirements can generate millions of possible combinations. Each additional constraint, whether it’s labor laws, employee preferences, or shift overlaps, adds another layer of complexity. Eventually, the problem becomes so complex that even the most advanced tools struggle to find the truly optimal solution.

This is because workforce scheduling is a combinatorial optimization problem. It’s about finding the best possible combination of employees, working hours, and tasks, while satisfying every rule and still meeting demand.

Sounds complicated, but doable - until you look at some examples:

1. A simple planning scenario with billions of variants

Let’s consider a scheduling scenario involving 25 employees, each of whom can start their shift at one of three times: 8:00, 9:00, or 10:00. This results in 3²⁵ = 847,288,609,443 - over 847 billion possible schedule combinations. 

While this figure, over 847 billion, highlights the exponential complexity introduced by even modest scheduling flexibility, it does not imply a correspondingly high optimization potential. 
In reality, many of these permutations are functionally equivalent, offering only minor variations in coverage or rule compliance. 

Once business rules, constraints, and objectives are factored in, the effective search space shrinks. Still, this example sets the stage: complexity scales fast, and for most organizations, real-life planning is far more intricate.

For most organizations, however, realistic deployment planning is much more complex

2. Flexibility expands the number of possible schedules

Now let’s introduce a layer of realism: flexibility. Suppose each of the 25 employees can start their shift between 08:00 and 10:00, in 15-minute intervals, and work between 4 and 8 hours per day.

This is a common scenario because more flexibility in shift planning not only means happier employees but also greater planning efficiency.

This seemingly modest increase in flexibility causes the planning space to grow exponentially. Across just 5 days, the number of possible schedule combinations expands to 45¹²⁵- an astronomically large figure.

Even evaluating the simpler case of 3²⁵ (~847 billion) variants is computationally intensive. Assessing 45¹²⁵ without advanced automation is virtually impossible.

Yet this is not an edge case. It reflects how most modern organizations plan: balancing flexibility, fairness, and demand coverage in dynamic environments.

This illustrates a key insight: whether automated shift optimization is “worthwhile” doesn’t primarily depend on the number of employees. It depends on the complexity of the underlying planning logic. The more flexibility, constraints, and variables in play, the more essential algorithmic optimization becomes.

3. The more variables, the greater the planning challenge

In most cases, however, the day-to-day reality of workforce planning is significantly more complex.

Many of our customers manage well over 100 employees, each with different qualifications, work locations, contractual agreements, availability, and preferences. These variables aren’t static; they can change at any time.

Now, imagine the task of scheduling those 100 employees across ten different activities over the course of a month. Their working hours can start at any 15-minute interval between 08:00 and 16:00, and each shift can last between 4 and 8 hours, also in 15-minute increments. To add to the complexity, each employee might perform up to five different activities in succession per day.

The result: A search space of 33,126,489 to the power of 3,000, a number with 22,560 digits. And that’s before accounting for any flexible breaks, last-minute absences, or regulatory changes.

Automated optimization in shift planning

Even with modern computing power, evaluating every possible variant is impossible. Shift planning is a classic NP-complete problem, a category of mathematical complexity class for which no known algorithm can reliably find the optimal solution within a finite time.

That’s why only advanced workforce management systems are capable of generating effective, compliant, and demand-driven schedules under such complex conditions.

For many users, it’s not clear how WFM tools actually achieve this. This is what is important to know:

There is no single “best” shift plan. Instead, there are countless valid solutions, each representing a different trade-off between competing priorities, such as operational efficiency, employee satisfaction, compliance, and customer service quality.

Not all WFM systems produce truly cost-effective schedules. Behind the scenes, many systems are merely automated versions of Excel. They simply check whether a plan complies with regulations, i.e., whether legal requirements, collective bargaining agreements, or shift limits are met. This ensures valid, but not necessarily efficient, plans. Because:

Automation is not the same as optimization. Automated systems follow predefined parameters and replace manual planning with faster, less error-prone execution. Optimization, on the other hand, delivers sustainable improvements across the entire planning process and uncovers solutions that are often invisible to manual planners.

True shift optimization is rooted in Operations Research (OR), a mathematical discipline that applies algorithms to improve efficiency under complex constraints. Strictly speaking, OR is a subset of artificial intelligence, though it doesn’t match the popular understanding of AI today. Some vendors blur this distinction, marketing well-established mathematical techniques as "AI innovations," without offering any actual methodological advancements.

>> Read also: AI in Workforce Management: Separating fact from fiction

As a user, it’s difficult, if not impossible, to assess whether a “fully optimized” schedule is genuinely efficient, or whether another tool might have produced better results using the same constraints. Relying solely on marketing claims can result in high hidden costs due to suboptimal planning, not just automation. Consider these three questions:

1. Does the system offer optimization beyond basic rule-checking?
   Features such as break optimization or activity sequencing are essential for generating flexible, cost-efficient schedules, not just valid ones. A tool that only checks compliance won't improve performance where it matters most.
2. Is “AI-driven” or “intelligent planning” backed by substance?
   While algorithms may be proprietary, a professional provider should clearly explain what is being optimized why it matters, and how their methods improve outcomes, not just hide behind buzzwords.
3. Is the optimization logic actively maintained and improved?
   Optimization is not a one-time feature. If algorithms are outdated or static, planning accuracy and efficiency will degrade over time. Vendors with in-house mathematical and operational expertise can continuously adapt their models to new challenges, ensuring consistent, high-quality results.

Complex planning logic requires automated shift optimization

As a rule of thumb, the simpler the setup (such as stable workloads, a small number of employees, and minimal rules), the more likely it is that an experienced planner can create effective schedules manually.

But today’s workforce planning challenges are rarely simple. Employee preferences must be considered. Qualifications are more diverse. And customer demand doesn’t fluctuate seasonally, it shifts rapidly in response to trends, events, and market dynamics.

Instead of seeing this as an obstacle, this is where the greatest optimization advantage lies: the more flexibility you’re able to build into your planning, the greater the efficiency gains a capable WFM system can deliver, as long as it’s genuinely optimizing, not just automating.

In other words, don’t simplify your operations to fit limited tools. Use the right software to turn planning complexity into measurable efficiency.