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Designing Engagement Models to Keep Good Workers

Designing Engagement Models to Keep Good Workers

   Jason Franklin

Designing Engagement Models to Keep Good Workers

Next-generation labor management software (LMS) systems are making it possible to predict when any given worker is at risk for quitting, with accuracy rates around 95 percent. 

While some early adopters simply use this kind of data to automatically fire workers with the highest risk scores, this strategy overlooks the greater levels of value the data can provide. It’s also short-sighted in today’s tight labor environment — which shows every sign of getting worse long before it improves.

A smarter way to use smart data

A more enlightened approach is to use risk data from your warehouse LMS to inspire and engage the workers you’d like to keep. The powerful outputs of data science can be used to drive resource-focused engagement, creating measurable improvements for both workers and the warehouse. Even simple, low-cost strategies like badges, pizza parties or an extra day off can pay big dividends in employee satisfaction and retention. 

Many factors determine which employee engagement models will appeal most to a given worker. For example, baby boomers typically prefer monetary incentives, while many millennials value non-monetary perks such as extra time off. Other factors that often influence engagement models are an employee’s performance level, tenure, utilization, operation and current risk score.

Get the most from attrition prediction

Let’s say your LMS system identifies a group of employees who are likely to quit in the next six weeks. Based on each individual’s unique risk factors, you could divide them into three groups: 

  • Red: Employees with high turnover risk, low performance records, or both
  • Yellow: Medium-risk workers with moderate to high performance levels
  • Green: Similar to the yellow group, but with lower risk

In this scenario, attrition prediction will offer the most value with members of the yellow group. The efforts needed to hold onto them could include more intensive training, larger incentives, or other motivation such as making sure some type of intervention is tried to avert separation. The system can help determine which incentives are likely to work best, balancing their costs against the likely benefits. 

These investments are easily justified when you consider the costs of recruiting and retraining replacements. If just 15 percent of workers in the green and yellow groups can be persuaded to stay, a medium-sized DC could easily see six-figure savings each year.

Track measurable results

The system also monitors the results of all these initiatives. With properly engineered labor standards and employee engagement models, early tests suggest that for every 50 cents employers invest in engagement, they should expect to see about a dollar in throughput benefits. 

At Honeywell Intelligrated, labor management experts are currently conducting A/B testing to further refine the effectiveness of attrition prediction. Machine learning plays a key role in shaping these models, balancing them with the aid of historical information. Once trained, the system works to ensure a curve that drives performance, predicts the best incentives to offer, and helps determine the standards an organization should set as goals. Going forward, it also monitors these and many other factors, suggesting changes as needed to maintain peak efficiency.

To learn more about these and other profitable insights made possible by next-generation LMS systems, check out the article “The Science of Labor and Productivity” in our On The Move digital publication. You’ll discover how data is fueling the transition to predictability and profitability and learn how its impacts are already being felt in nearly every aspect of DC operations.

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