Target 48.50 Variance 12%

Actual 42.68

Unleash the power of your workforce dataHow analytics help manufacturers gain valuable business insights

You’re sitting on a goldmine: your data. As automation expands across your manufacturing operations, you’re collecting more information than ever before. But what good is all that data if you’re not harnessing it to make smarter business decisions and drive continuous improvement?

Analytics help you unleash the power of your data by transforming it into meaningful business intelligence. These actionable insights empower you to:

• Answer critical questions that impact your bottom line

• Identify potential issues and take corrective action

• Uncover opportunities for cost and productivity improvements

Working with leading manufacturing companies across the globe for more than 40 years, Kronos has seen the value data analytics can bring to organizations like yours. Keep reading to learn how manufacturers are using Workforce Analytics™ for Manufacturing to uncover, understand, and address common labor issues and problematic business practices. You’ll discover innovative ways to gain data-driven insights that can be used to squeeze out inefficiencies and optimize your operations.

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Pinpoint sources of high overtime Challenge: Regulate high overtime costsOvertime can be an extremely beneficial and cost-effective option to meet peaks in demand without hiring and training more labor. However, when not utilized correctly, overtime can become a big problem — especially when you don’t know where it’s being used. The ability to pinpoint the source of overtime helps you determine if it’s necessary or if you’re just spending extra labor dollars.

Workforce Analytics for Manufacturing can help manufacturers drill down into labor data and identify sources of high overtime — pinpointing specific locations, departments, cost centers, and even jobs and employees. This visibility allows manufacturers to better understand where labor dollars are being spent and make data-driven decisions to improve their bottom line.

20% 20%

35%

30%

25%

20%

15%

10%

5%

16%

12%

8%

Labor Level 2

Regular Hrs- 378,214<300,000< 200,000<100,000- 0

NorthernWestern

4%

All OT Hrs

10,000 20,000 30,000 40,000 50,000 60,000

1,000 2,000 3,000 4,000 5,000

Atlanta

Pittsburg

HoustonDenver

Chicago

Portland

RaleighSeattle

DallasBillings

Richmond

CharlotteScottsdale

Portsmouth

Detroit

Albuquerque

Springfield

Rochester

OT

Rate

OT

Rate

All OT Hours

Overtime Hours and Rates by Locations Overtime Hours and Rates by Department

4000

3000

2000

1000

Weekly Trend of Regular and OT HoursW

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Overtime by Location analytics show that the Portland and Atlanta locations are outliers. In this example, deeper data analysis would uncover which departments are accounting for most of the overtime for these facilities and driving up labor costs.

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Avoid unnecessary overtime Challenge: Utilize excess capacity

While overtime is a great way to quickly meet demand, it’s not always necessary when regular hours are available. Especially in larger plants, there’s often no way for management to know exactly how many hours each employee has worked. A deep dive into the data identifies possible replacements who won’t reach overtime if they fill an open shift.

With Workforce Analytics for Manufacturing, manufacturers can utilize untapped regular hours before they allow overtime, thereby reducing labor costs without reducing throughput. Additionally, if the data reveals consistent overtime being used and no excess capacity available, it can signal management that it may be time to hire more labor to avoid burning out the workforce.

Weekly Worked OT and Capacity

Unused Capacity

Ove

rtim

e H

ours

Use

d

800

700

600

500

400

300

200

100

0

200 400 600 800 1,000 1,200

This visual representation clearly identifies locations using overtime when untapped regular hours are available. The large grouping of dots in the lower left represents an ideal staffing scenario: taking advantage of unused capacity and using minimal overtime hours.

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Minimize injuries and loss of quality

Challenge: Avoid fatigue and burnoutWhen demand is high, the demand placed on the workforce can be high as well. Employees may be required to work more days a week, work longer shifts, or be given less time in between shifts. Operating like this for an extended period can not only lead to high overtime costs, but also low employee morale, quality concerns, and even safety incidents. Employees could be burning themselves out without even noticing it because they are enjoying larger paychecks.

Workforce Analytics for Manufacturing can provide visual dashboards that highlight where employees might be working excessive schedules, how bad the situation has become, as well as the financial impact it’s having. With visibility into this data, manufacturers can see when it’s time to create a more balanced working schedule and hire more talent, helping to maintain employee morale. The result can be significant cost savings through reduced overtime, fewer injuries, and improved quality.

Days Range

1: 7-10 Days2: 11-14 Days3: 2-3 Weeks4: 3-4 Weeks5: 4+ Weeks

Chicago

Portland

Plant

Richmond

Detroit

Albuquerque

Rochester

Where Are Employees Working 7+ Days in a Row?

Occ

urre

nces 100

60

20

Occ

urre

nces 100

60

20

Occ

urre

nces 100

60

20

Occ

urre

nces 100

60

20

Occ

urre

nces 100

60

20

Occ

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nces 100

60

20

By creating a dashboard view of where employees are working more than seven days in a row, managers can easily see which locations are at the highest risk for employee fatigue and burnout, and take steps to correct the situation before problems occur.

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Find hidden costs Challenge: Visibility into labor varianceIn manufacturing, labor variance helps you to understand how labor is being used, compared to what is budgeted or standard. While you may have visibility into labor cost variance at a high level, it’s typically very difficult to trace the source back to a specific root cause at the product, location, or shift level without the right tools.

Workforce Analytics for Manufacturing gives visibility into labor cost variance at a much more granular level, allowing them to better understand what factors might be driving up their labor. With the data mapped, it’s much easier to identify root causes. For example, by comparing the labor variance of multiple shifts, you could uncover processes that require more labor in one shift than another and make adjustments. Identifying these variances and correctly allocating them helps minimize product cost inflation and leads to more accurate cost-per-unit numbers.

The Variance by Shift chart shows that the shift represented in blue has a higher cost variance percentage than the other two shifts. With visibility into this variance, managers can dig deeper to determine whether the variance was expected or represents an area to be investigated further.

By Day

Variance by Shift Variance by Day of Week

Metrics

Actual Labor CostStandard Labor CostCost Variance

Shift

EFG

12/2

2/2

018

12/2

4/2

018

12/2

6/2

018

12/2

8/2

018

12/3

0/2

018

1/2

/2019

1/4

/2019

1/6

/2019

1/8

/2019

1/1

0/2

019

1/1

2/2

019

1/1

4/2

019

1/1

6/2

019

1/1

8/2

019

1/2

0/2

019

1/2

2/2

019

1/2

4/2

019

1/2

6/2

019

1/2

8/2

019

1/3

0/2

019

2/1

/2019

2/3

/2019

2/5

/2019

2/7

/2019

2/9

/2019

2/1

1/2

019

2/1

3/2

019

2/1

5/2

019

2/1

7/2

019

2/1

9/2

019

2/2

1/2

019

2/2

3/2

019

2/2

5/2

019

2/2

7/2

019

3/1

/2019

3/3

/2019

3/5

/2019

2,400

2,000

1,600

1,200

800

400

0

(400)

120

80

40

0

-40

-80

3K

2K

1K

0

-1K

-2K

-3K

Cos

t Va

rian

ce %

Labo

r H

ours

Cos

t Va

rian

ce

Standard Labor Cost

0 500 1,000 1,500 2,000 2,500 3,000 Sunday Monday Tuesday Wednesday Thursday Friday Saturday

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Zero in on compliance risk

Post-Shift Punch Edits

Amount of Individual Edits (Minutes)-15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Post-Shift Punch Edits

Amount of Individual Edits (Minutes)-15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Num

ber

of P

unch

Edi

ts

1,600

1,400

1,200

1,000

800

600

400

200

0

Num

ber

of P

unch

Edi

ts

1,600

1,400

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1,000

800

600

400

200

0

This dashboard shows timecard edit volume broken down by changes made to the number of minutes worked. The top chart clearly skews to the left side of zero, with the manager taking many more minutes away from timecards than they’re adding. Ideally, the chart would show a bell curve with a more equal distribution on either side of zero to minimize compliance risk. The bottom chart shows how adding too much time can increase labor costs.

Challenge: Noncompliant punch edits With hundreds of employees, some are bound to come in late or leave a few minutes early over the course of a pay period. It’s up to managers to make timecard edits to ensure employees are being paid appropriately. But an unfair distribution of punch edits can put a manufacturer at significant legal risk. Too many punch edits taking paid time away from employees exposes companies to government investigation and potential fines.

Supervisors need visibility into the data so they can take corrective action before it’s too late. Workforce Analytics for Manufacturing provides visual dashboards that highlight unusual patterns of punch edits. Managers can see at a glance whether the company is at legal risk or if labor costs are being driven upward by punch edits that are adding paid time.

Too many punch edits taking paid time away from employees exposes companies to government investigation and potential fines.

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Ensure accurate, timely paychecks

See how long managers are taking to approve timecards. You can view this metric by average approval time, number of employees, and number of weeks.

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How Long to Approve Timecards?

Average Approval Time (Days) Number of Employees Number of Weeks

-10 -6 -2 2 6 200 400 600 800 1,000 2 4 6 8 10

Challenge: Timecard approvalsAt the end of each pay period, managers must approve timecards to ensure that employees are paid correctly and on time. If managers start missing errors or are late on approvals, employees’ pay can be delayed or inaccurate, affecting both their morale and their daily lives.

With Workforce Analytics for Manufacturing, leadership has visibility into which managers are having timecard issues so they can pinpoint the source and take action to address the problem. Don’t risk losing a top performer because of paycheck problems. Getting paychecks right every time is possible with an analytics solution in place.

Don’t risk losing a top performer because of paycheck problems. Getting paychecks right every time is possible with an analytics solution in place.

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Dig deep into absenteeismChallenge: Unplanned employee absencesIn a manufacturing environment, the business impact of employee absenteeism can be felt at all levels — from unmanned stations to a loss in production to a negative hit to the bottom line. When employees are undependable, it forces manufacturers to scramble to find replacements. But efforts to minimize downtime could require putting another employee into overtime, driving up labor costs. And dedicated employees who show up for their shifts feel pressured to pick up the slack, creating resentment and lower engagement. While occasional unplanned absences are unavoidable, repeated occurrences could signal that employees are taking advantage of the system.

How can data help? With Workforce Analytics for Manufacturing, manufacturers have visibility into absence data so they can identify trends and solve them. Supervisors can view the data in any format, by day of the week, location, department, or job role. With visibility into unplanned absences, managers can address potential problems before they affect labor costs, employee engagement, or customer commitments.

Absence by Month

Act

ual U

pabs

180.00

160.00

140.00

120.00

100.00

80.00

60.00

40.00

20.00

Janu

ary

Febr

uary

Mar

chApr

ilM

ayJu

neJu

lyAug

ust

Sep

tem

ber

Oct

ober

Nov

embe

rD

ecem

ber

Absence by Day of Week

Act

ual U

pabs

280.00

160.00

120.00

80.00

40.00

Sun

day

Mon

day

Tues

day

Wed

nesd

ay

Thur

sday

Frid

ay

Sat

urda

y

This Absence by Month chart shows a common seasonal absence pattern in manufacturing with a higher rate of absenteeism around the holidays. The Absence by Day of Week chart reveals that most absences occur on Fridays — insight that allows managers to better prepare both weekly and seasonally.

With visibility into unplanned absences, managers can address potential problems before they affect labor costs, employee engagement, or customer commitments.

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Put your data to work for your businessLooking to gain actionable insights into your manufacturing operations? Schedule a consultation today to learn more about unleashing the power of your workforce data with analytics.

Kronos is a leading provider of workforce management and human capital management cloud solutions. Kronos for Manufacturing provides solutions that help manufacturers attract, retain, and develop fully engaged employees who deliver better business outcomes. More than 2,000 manufacturing organizations of all types and sizes use Kronos to drive productivity improvements and enable on-time delivery of high-quality goods and services. Learn more about Kronos industry-specific time and attendance, scheduling, absence management, activities, HR and payroll, and labor analytics applications at kronos.com/manufacturing. Kronos: Workforce Innovation That Works™.

kronos.com

© 2019 Kronos Incorporated. Kronos and the Kronos logo are registered trademarks and Workforce Innovation That Works is a trademark of Kronos Incorporated or a related company. Kronos Workforce Ready and Kronos Payroll Services are provided by and contracted with Kronos SaaShr, Inc., a wholly owned subsidiary of Kronos Incorporated. For a full list of Kronos trademarks, please visit the “trademarks” page at www.kronos.com. All other trademarks, if any, are property of their respective owners. All specifications are subject to change. All rights reserved. MF0298-USv1

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