1. Aravind Meenatchi Sundaram Saravanan Pitchaimani
ISyE 6203 Project Dynamic Allocation of Labor in a Distribution Center : Staples
Team
Aravind Meenatchi Sundaram
Anirudh Ramkumar
Ajay Balan
Saravanan Pitchaimani
Siddhesh Wavhal
Deepak Gahlawat

2. ISYE 6203 Project
Staples is a global company with delivery and retail businesses serving customers in 27 countries
With $25 billion in sales, it serves businesses of all sizes and consumers in 26 countries throughout North and South America, Europe, Asia and Australia.
The Three business segments are: North American Delivery, North American Retail and International operations.
Staples operates 1,872 retail stores throughout the United States and Canada, offering savings on more than 7,000 office products.

3. Project Background
Staples.com is an award winning e-commerce site that features 30,000 products ranging from basic office supplies to the latest business technology.
With 29 fulfillment centers and a fleet of about 820 trucks, Staples is able to offer fast, friendly delivery throughout North America.
Their DCs serves these fulfillment centers: Employs Associates as Full time and Contract basis.
These associates work across 12 Departments and some of the associates are cross trained.

4. Project Background Departments Wave Replenishment Lean Replenishment
Bulk- Full Case Reserve
Bulk – Pick to Belt
Bulk – Non Conveyables
Bulk – Bulk Wave
Bulk – Furniture Picking
Break pack/ KIVA Picking
QC/Finalizing
Shipping
Wrap & Label
Returns
Receiving
Put away

5. Project Background

6. Current Associate Information
Project Background
Current Associate Information
Full time
$17.50/hr
8 hours per day
Part Time
$12.50/hr
6 hours per day
Overtime
$24 / hr
Limited to 4 hr/person
Only Full time Associated can perform overtime

7. Objective
Achieve optimal staffing levels and labor allocation in their Distribution Centre.
Maintain flexibility to serve variable demand that the DC faces over a year.
Minimize Labor Cost

8. Methodology PART 1 ~ Over an year
Optimal Staffing Level for each department over a year
1. Balance number of associates on Full time and part time maintained on payroll to minimize staff levels
PART 2 ~ Over a day
Using the staffing level obtained in part1,
Determine the optimal labor allocation in a day
2. Divide the days work among various labor categories.
PART 3 ~ During a day
Using the optimal labor achieved over a day
1. Determine number of employees to be called during each period in a day.
Period spans for 30 minutes
Working hours vary by department
Labor categories
Full time Specialized
Full time cross trained
Full Time Over time
Part time Associates

9. Deciding the long term labor levels
Methodology:
Using a Newsvendor model to
Calculate the optimum number of labor hours to be employed per day, so as to
Balance the over-utilization and under-utilization of labor, while
Considering the variability of the demand for a period of one year so as to minimize the annual labor costs

10. Methodology
Split the total number of labor hours to be employed between full time and part time according to a practical ratio between the them
This ratio is based upon the no show probability of the part time workers based upon historical data

11. Problem Formulation For each department CU = Shortage cost
= rate($/hr) in overtime – rate($/hr) in regular time
CO= Overage cost
= rate ($/hr) in regular time
Critical ratio = CU /(CU + CO)
We calculate the required labor hours, based on this critical ratio and the CDF of the demand data

12. Cost Calculation for each day
If the number of hours is lesser then the critical value, then
Cost = Critical Value * Regular time rate
Else if the number of hours is greater than the critical value
Cost= ( Critical Value * Regular time rate) + ((Hours used-Critical value)*overtime rate)
Sensitivity
Cdf for

13. Long Term Labor Allocation Tool
Functionality
Determines the long term labor levels for each departments
Comparison of the existing labor plan with the optimized labor plan, considering similar parameters
Provision for determination of future staffing levels according to a pre-specified growth %

14. Inputs Daily Demand Data for each department for a year
Labor wage details
Working hours per day
No. of workers employed in the current plan
Ratio of full time to total laborers
Anticipated annual growth %

15. Output Optimized Labor levels Previous Labor Allocation
Department-wise Cost Calculations

16. Results
The overall savings that we achieved with this model is $0.1 million or 2% of total labor costs
The optimized labor levels vary only slightly from the current labor levels
This shows that the current long term labor allocation is pretty accurate and no major changes are recommended

17. Usability
This tool is recommended to be used yearly or quarterly, according to the manager’s requirements
The data required for this tool is readily available, hence there is an ease of use
Input data would be fed from DOR(Daily Operating Report) in the same format

18. Limitations
This tool is conservative. It does not account for changes in labor allocations done daily or weekly as per the demand forecasts which is considered in the subsequent model

19. Part 2 Objective: Using the staffing level obtained in part1,
Determine the optimal labor allocation in a day
Divide the days work among various labor categories.
Methodology:
Based on the demand forecasted for the day
Linear programming assignment model was built in excel to determine the optimal labor allocation
Assumptions:
Productivity was assumed to be the same for all the associates
Pay rate was assumed to be fixed for different labor categories.

20. Inputs CT -1: Cross Trained between specific departments
Forecasted Demand
FT Count
PT Count
Receiving
25000 13 10
Put-away 9 5
Wave Replenishment
3000
Lean Replenishment
11072 14
Bulk - Full Case Reserve
700 2
Bulk - Pick to Belt
Bulk - Non Conveyables
5873
Bulk - Bulk Wave
7477
Bulk - Furniture Picking
600
Breakpack/KIVA Picking
10000 31 23
QC/Finalizing 7
Shipping
27207 8
Wrap & Label
1845 1
Returns
2000
Inventory Control
8000
CT - 1
Cross Trained
CT Count
Receiving/Put Away 4
Across Replenishments 3
Across Bulk operations
Across BP/QC 10
Across Ship/Wrap/Returns Inventory
CT - 2
Across all departments
CT -1: Cross Trained between specific departments
CT-2: Cross Trained Across all Departments

21. Part 2: Model Constraints: Objective: Assignment Constraint:
Min Allocated Labor hours= Number of labors assigned * cost
Assignment Constraint:
For all department,
No. of Full time Specialized Employee assigned = No. of Full time employees available
Number of Cross trained Employees Assigned = Number of Cross Trained employees available
No. of part time Specialized Employee assigned ≤ No. of Part time employees available
Demand Constraint:
For any department i,
Number of Associates * Hours Employed * Productivity ≥ Demand Forecasted
All Full time associates would be assigned .
Trade off is established between the part time associates

22. Part 2: Results
Achieved Optimal allocation of labors in a day based on the demand forecasted.
Department
Total Labor Count
Receiving FT PT
CT-1
CT-2
OT mins
Put-away 13 1
Wave Replenishment 9 3
Lean Replenishment
Bulk - Full Case Reserve 14 4 27
Bulk - Pick to Belt 2
Bulk - Non Conveyables
168
Bulk - Bulk Wave 71
Bulk - Furniture Picking
Breakpack/KIVA Picking
QC/Finalizing 31 20
Shipping 7 5 6
Wrap & Label 8
275
Returns
187
Inventory Control
531
116 47 25
1259
192

23. Part 3 Objective: Using the optimal labor achieved over a day
Determine the number of employees to be called during each period in a day.
Period spans for 30 minutes
Working hours vary by department
Methodology:
Demand that occurs over the day is stochastic in nature , forecast is 95% accurate.
2. Each of the department is allocated various categories of employees based on its demand.
3. A full time and part time associate would work for 8 hours and 6 hrs continuously..
Input from Part-2
Demand incurred during the day is met.
How many of each type of associates to call during each time period.

24. Part 3: Model Constraints: Objective: Assignment Constraint:
To assign associates across different time periods
Assignment Constraint:
For all department,
No. of Full time Specialized Employee assigned = No. of Full time employees allocated from part 1
Number of Cross trained Employees Assigned = Number of Cross Trained employees allocated
No. of part time Specialized Employee assigned = No. of Part time employees allocated
Demand Constraint:
For any period i,
Number of Associates * Hours Employed * Productivity ≥ Demand Forecastedperiod
All Full time associates would be assigned .

25. Part 3: Result Input to the Model Output Legend Blue: Input
Demand
25000
Assigned Workers
Assigned Hours
Full Time Specialized 13 8
Full Time Cross 1
Part Time 6
OT hours
Output
Legend
Blue: Input
Green: Output

26. Part 3: Result
Receiving
Putaway
BreakPack
Quality Control

27. Summary/Conclusion
Optimal number of Full time and Part time associates to be employed over an year was determined.
Excel models were built to determine the optimal number of employees to be ‘called-in’ during a day based on the demand forecasted.
Also the assignment of the employees across various time periods during a day was arrived.
This model assures better distribution of cross trained employees across department.
This model can be used to dynamically allocate labors across days based on the demand forecasted

28. Future Steps/Recommendation
Including the benefits provided for full time employee in their pay rate
Accounting the reliability of the part time workers in terms of their productivity.
Accounting for the cost involved in cross training.
Explicitly displaying the benefits to each department that would encourage in cross trained worker sharing.
Seasonal demands at the end of the year can be met by hiring additional part time associates.
Determining the optimal number of cross trained employees would minimize cost while maintaining the required flexibility to meet demand.

29. Thank You
Questions ???