Driving Higher Workplace Performance: Using Analytics, Dashboard Metrics, and Soft Skills to Improve Results

Situation Analysis

When I was assigned the task of improving warehouse performance for a Western Canadian industrial distribution center, I rediscovered what many business leaders subconsciously know, but often forget: Special care must be taken when tending to human-dependent processes.

The improvement project focused on the performance of the warehouse “picking” process. In plain English, a “picker” is an employee that is paid to pick, and sometimes pack, customer orders.

In my first week of mapping the process, I experienced several “aha” discoveries:

• Metrics—Our performance measurements were weak and not defensible.
• Pay structure—Everyone was paid essentially the same. Tenure, not performance, was the barometer for pay differentiation.
• Job pride—There appeared to be little dignity in the picking role.
• Leadership—Active, hands-on leadership was missing.
• Human dependency—Although technical systems (software, conveyers) were definitely part of the process, the core of our warehouse picking function relied on people.

Quality Solutions

Results of a voice of the customer (VOC) survey showed that two things mattered with respect to being a good picker: accuracy and productivity. My challenge was to develop a measurement system that measured both the volume and the accuracy of the work completed in a day.

Research indicated that four critical changes would yield significant improvements to productivity and accuracy: transparency, utilizing dashboards, rewarding excellence, and restoring pride.

Our CEO approved a four-week pilot that provided carte-blanche authority to change the way we administered our picking process. After several meetings with our picking staff, the following changes were authorized:

• We would measure the accuracy and productivity of every picker, every day.
• We would post the results in visible locations.
• We would reward excellence. Superior performance was monetarily rewarded.
• I would enable worker involvement, meeting with the pickers every week to solicit feedback on how we could improve the process.
• Root causes of picking errors or lower productivity would be identified, and corrective action, usually additional training, would be prescribed. The data would not be used in a punitive manner.

Results

The picking process, developed jointly by warehouse employees and a Six Sigma Black Belt, was more rewarding and more fun, and performance had improved.

Highlights from project results include:

• Overtime was eliminated.
• Productivity increased by 25%.
• Absenteeism declined by 47%.

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