GM501 Unit 4 Warehouse Overtime Cap Systems Paper Example

Five Hours of Overtime and Everything It Touches: A Unit 4 Systems Effects Paper on a Beverage Warehouse

[Student Name]

Purdue University Global

GM501: Management Theories and Practices II

Unit 4 Systems Effects Paper

[Instructor Name]

[Date]

The distributor, its warehouse and every figure are composite and illustrative; the frameworks and studies cited are real.

The Proposal and Its Intended Effect

Ridgeline Beverage distributes beer, soft drinks and bottled water to about 1,400 grocery stores, convenience stores and restaurants from one warehouse. The warehouse employs 86 full-time order selectors, who build mixed pallets for each route, and 22 loaders. In the peak months from May through September, selectors average about eleven hours of overtime per two-week pay period, and some exceed twenty.

Leadership proposes capping overtime at five hours per pay period from next May. The proposal names two goals. The first is cost: overtime carries a 50 percent premium over base pay, and last summer's overtime bill came to about $410,000. The second is fatigue: selectors lift cases weighing up to fifty pounds for ten-hour shifts, and injury claims rise every summer. Research on long working hours supports the concern; workers on overtime schedules have higher rates of injury than those on standard schedules, even after adjusting for the type of job (Dembe et al., 2005). The proposal fills the remaining hours with temporary workers from a staffing agency.

Why a Systems View

A spreadsheet treats the cap as a single cause with a single effect: fewer overtime hours, lower cost. A systems view asks what else depends on those hours, and whether any of those effects feed back on the original problem. Peter Senge argued that many management problems are created by yesterday's solutions, because decisions are made as if cause and effect were close in time and space when they are not (Senge, 1990). John Sterman's work on system dynamics provides the tools: causal loop diagrams that map how variables influence one another, and the distinction between balancing loops, which push a system toward a goal, and reinforcing loops, which amplify change (Sterman, 2000).

Figure 1: The Causal Loop Diagram

Figure 1 places the overtime cap at the left. From it, arrows run to six variables: selector overtime hours, selector fatigue, temporary labor hours, picking error rate, retail stockouts and experienced selector turnover. Two loops carry most of the dynamics.

Loop B1, the intended balancing loop. The cap reduces overtime hours. Fewer hours reduce fatigue. Lower fatigue reduces injuries and the errors fatigue causes. This loop works as leadership expects.

Loop R1, the unintended reinforcing loop. The cap increases temporary labor hours. Temporary workers, who do not know the warehouse layout or the product codes, make more picking errors. At Ridgeline last summer, temporary selectors averaged about 2.4 errors per thousand cases, against 0.6 for experienced selectors. Errors must be corrected before trucks leave, and correction falls to experienced selectors, who are the only ones who can find the right product quickly. Correcting errors takes time from their own picking and adds frustration. Frustration raises turnover among experienced selectors. As experienced selectors leave, the warehouse needs still more temporary labor, which raises errors further.

The cap is meant to protect experienced selectors from overwork, but it routes the extra hours to temporary workers whose mistakes land back on the experienced selectors' desks.

The Effects Beyond the Warehouse

Picking errors that are not caught reach stores as wrong or missing cases. Grocery chains measure their suppliers on order accuracy, and two of Ridgeline's largest customers charge penalties when accuracy falls below 99.5 percent. A short product means an empty shelf, which during summer costs the store sales and costs Ridgeline its standing with the store's buyers. Each of these effects arrives weeks after the cap begins, which is why a spreadsheet built around one pay period would miss them.

Estimating the Size of the Effects

A rough estimate, using last summer's figures, illustrates the stakes:

1. Overtime removed. Capping at five hours removes about six hours per selector per period across ten peak periods, roughly 5,200 hours. At an average overtime rate near $36 an hour, those hours cost about $187,000 last summer.

2. Hours replaced. The same 5,200 hours still have to be worked. At the agency's rate of about $31 an hour, temporary labor costs about $162,000, so the direct saving is only about $25,000.

3. Errors and penalties. A selector picks roughly 180 cases an hour, so moving 5,200 hours to temporary workers shifts about 936,000 cases. At the difference in error rates, that adds about 1,700 errors over the summer. If even a fifth reach stores, accuracy penalties and credits could cost $15,000 to $25,000.

4. Turnover. Replacing one experienced selector costs about $9,000 for recruiting, training and the months of slower picking. If the cap adds six departures, that is $54,000, and the loss of their knowledge raises the next summer's error rate.

The $410,000 headline is the whole summer overtime bill, not what the cap changes. Measured properly, the direct saving is about $25,000, and errors and turnover could turn it into a loss in the first summer.

Delays and Why They Matter

The loops run at different speeds. The overtime saving appears in the first paycheck. Errors rise within the first week. Penalties arrive with the first monthly accuracy report. Turnover shows up over the summer and the following spring. Because the benefits appear first and the costs later, early reports will make the cap look successful, and by the time the costs are visible the experienced selectors who could have fixed them may be gone. Sterman calls this the mismatch between the timing of a policy's effects and the timing of the evaluation, and it is a common reason good intentions produce poor results (Sterman, 2000).

Where a Small Change Has a Large Effect

The diagram suggests three ways to keep Loop B1 and weaken Loop R1:

1. Train temporary workers before peak season. A two-week paid training period in April, using the same agency workers each summer, would lower their error rate before the cap begins.

2. Assign temporary workers to simpler orders. Full-pallet orders of a single product are much harder to get wrong than mixed pallets. Routing them to temporary selectors would protect accuracy.

3. Keep a separate error team. Two experienced selectors assigned to checking and correction, with pay for the role, would stop correction work from spreading to everyone.

Each of these addresses the reinforcing loop at a point where a small change has a large effect.

Conclusion

The overtime cap would achieve its intended effect on fatigue and on overtime pay, but a systems view shows a reinforcing loop through temporary labor, errors and experienced selector turnover that could undercut both its savings and its purpose. Because the benefits arrive before the costs, the problem would be easy to miss. The cap can work if Ridgeline trains temporary workers early, assigns them simpler work and protects experienced selectors from correction work, and if leadership evaluates the policy over a full year rather than a single pay period.

References

Dembe, A. E., Erickson, J. B., Delbos, R. G., & Banks, S. M. (2005). The impact of overtime and long work hours on occupational injuries and illnesses: New evidence from the United States. Occupational and Environmental Medicine, 62(9), 588-597. https://doi.org/10.1136/oem.2004.016667

Senge, P. M. (1990). The fifth discipline: The art and practice of the learning organization. Doubleday.

Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. Irwin/McGraw-Hill.