Offset Labor Challenges and Overtime Costs With Capacity Planning

Offset Labor Challenges and Overtime Costs With Capacity Planning

Production deficits from unplanned labor shortages are inevitable parts of running a manufacturing or distribution and fulfillment (D&F) operation. Since the start of the pandemic, many site managers have been forced to deal with situations where employee illnesses have caused multiple resources to be absent for extended periods of a time. These unexpected absences can put production behind schedule and force operations to increase overtime hours just to keep pace.

Too often, what was intended to be a temporary overtime strategy becomes a standard operating procedure (SOP), leading to a never-ending cycle of increased labor costs that cut into profit margins. Faced with a challenging labor market and ever-increasing competitive pressures, many operations are turning to capacity planning models to break the cycle.

Capacity planning refers to the practice of evaluating expected production targets against current and future labor resource availability. For decades, site managers have relied on short-term plans to help them hit daily or weekly production targets. Historically, the forecasting range and accuracy of these rudimentary planning tools have been limited to one to two weeks.

Typically created in spreadsheet software, these plans focus primarily on two key variables: production volume and available resources. In the hands of more skilled practitioners, these variables can be expanded to include other important components of the planning equation:

  • Attrition and absentee rates
  • Individual performance and proficiency levels
  • Scheduling per production of specific items and/or attribution to cost centers

But even if an operation has a spreadsheet specialist capable of making more advanced planning models, their processes are often considered “one-offs” and unable to be utilized across a larger network of facilities.

The future of LMS capacity planning

Modern warehouse labor management software (LMS) is beginning to leverage advanced data science techniques to enable more accurate and prescriptive capacity planning models. Let’s look at an example operation that was able to produce only 9,000 of its planned 10,000 units due to an illness outbreak. Now, the site manager must figure out a way to make up for a 1,000-unit shortfall. In this scenario, the following approaches demonstrate the journey from a typical resourcing strategy to a data-driven capacity planning model.

1.       No capacity planning (increase overtime) — The most common solution to this problem is to hire an excess amount of overtime resources (e.g., 15% additional overtime hours at 1.5 times — or time-and-a-half — pay rate). This temporary resolution significantly increases the cost-per-item produced and erodes profit margins.

2.       Rudimentary planning (expand your options) — The next step on this journey is to make rudimentary site calculations — based on absenteeism, equipment downtime rates and projected production targets — and then select an approach to recover from the production deficit. Options may include:

  • Increasing overtime
  • Hiring more workers
  • Adding production shifts

While this slightly more informed estimate gives site managers more options, it cannot compare the consequences and outcomes of each course of action.

3.       Data-driven capacity planning (choose optimal strategy) — A data-driven capacity planning tool provides the insights needed to effectively break the costly, inefficient cycle of the above scenarios. Historic data models are combined with the calculations presented in option 2 and processed with artificial intelligence (AI) techniques to create multiple planning scenarios.

Then, site managers can evaluate their options and choose the most optimal approach (i.e., using overtime, hiring workers, adding shifts, or any combination therein).

To discover the future of capacity planning and equip your operation with the ability to adapt to unexpected resource challenges, view our white paper.

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