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Improving the Operation of Warehouse Cold-Storage Areas

Client: North Texas Food Bank

Senior Design 2010

Nafees AhmedPrajyot Bangera

Shahrzad RahimianPablo De Santiago

May 10, 2010“Passionately pursuing a hunger-free community”

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Client BackgroundNTFB was established in 1982 to deal with

the critical issue of hunger in the North Texas area

They provided over 400,000 meals during their first year of operations; Last year, over 37 million meals distributed

Currently, their goal is to reach 50 million meals distributed by 2011

Their vision is to see a hunger-free world

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Management SummaryMet with Director of Operations, Sean Gray, and toured the

facility

It was determined that the best area of focus for us would be determining bottlenecks as to why the freezer and cooler operations were not as efficient as they could possibly be

ObservationsObserved how cold food comes to NTFB, is stored, sorted,

ordered, and delivered to agenciesData collection – online inventories and ordering system

Determined main metric to measure improvement would be time

Used a simulation to model the system and determine recommendations

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Problem DescriptionConcerns with the freezer/cooler operations

Organization of items insideTime wasted looking for items or moving items

around to get to another item – this time could be better utilized

Optimizing usage of the limited spacePerhaps we could see a problem that was going

unnoticed in the operationsCooler/freezers items brought out once agency

physically shows up

Data CollectionOrganization – no tracking of cycle timePlenty of inventory data availableObservations and process timing

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Situation AnalysisSpeak with operations and facilities managers

Concerns with freezers/coolerNew ideas for system improvements

Spoke to some agencies that come to pick up food

LayoutEast dock

Primarily assigned to NTFB trucks to deliver and pick-up items

Used to receive items in the afternoonWest dock

Primarily for agency pick-ups

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Direct ObservationPictures and videos

taken, so we can track each process and time them as well

Interviews with truck drivers, forklift operators, front office management, and agencies provided different viewpoints to the sameproblems

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Situation AnalysisConclusion of analysis

NTFB runs a very efficient operation

Recommendations would most likely be on small changes that will add up to an overall increased efficiency

Based on the concept of “kaizen” - Japanese for "improvement" or "change for the better“…focuses on constant, little improvements to a system to makeit run more smoothly

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Creating the Simulation Model

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ProModelLocations are like

variablesSet constraints

through locationsSet times of

processes based on our direct observation data

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ProModel Used expected values from historical data

NTFB’s online ordering system and inventory database; sign in sheets

Performed an A-B-C pareto analysis on the C/F inventory to determine realistic probabilities and values

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Analysis of ResultsPossible to run thousands of simulations with different

scenarios

We chose to run 4 variations of our base line model with possible scenarios, and then a 5th variation with extremely ideal conditions and parameters, to be able to see how the results would vary

Since space within freezers and coolers is limited and is not something we can create, it is a constant in our model

Each model variation ran 10 times, over the course of 8 hours, using 15 minute time slots

Results were given as averages from the 10 simulation runs

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Analysis of ResultsName

Scheduled Time (HR) Capacity Total Entries

Avg Time Per Entry (MIN) Avg Contents % Utilization

docks 8 35 34.3 6.99 0.50 1.42freezer1 8 30 9.1 168.57 3.18 10.61freezer2 8 30 8.8 141.25 2.70 8.99cooler 8 30 15.5 163.97 5.40 17.98

weighting point 1 8 1 33.8 4.54 0.31 31.50

weighting point 2 8 1 9.9 46.84 0.94 93.72

Delivery queue 8 1 8.9 51.31 0.92 92.12gate2 8 1 7.9 6.49 0.11 10.56

•Base-Line Model: Base-Line Model [45-55 min agency wait, 1 agency pick up dock, 1 queue line]

NameScheduled Time (HR) Capacity Total Entries

Avg Time Per Entry (MIN) Avg Contents % Utilization

docks 8 35 32.9 7.20 0.50 1.42

freezer1 8 30 8 149.97 2.49 8.30

freezer2 8 30 9.2 134.48 2.68 8.95

cooler 8 30 14.9 155.39 4.82 16.06

weighting point 1 8 1 32.5 4.11 0.27 27.38

weighting point 2 8 1 10.3 44.40 0.93 92.56

Delivery queue 8 1 9.3 50.19 0.94 93.53

gate2 8 2 8.3 5.96 0.10 5.13

•Scenario 1 [45-55 min agency wait, 2 agency pick up docks, 1 queue line]

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Analysis of ResultsName

Scheduled Time (HR) Capacity Total Entries

Avg Time Per Entry (MIN) Avg Contents % Utilization

docks 8 35 34.9 7.38 0.54 1.56freezer1 8 30 10.2 100.13 2.18 7.26freezer2 8 30 7.6 94.99 1.76 5.86cooler 8 30 16.5 106.09 3.82 12.72

weighting point 1 8 1 34.6 3.86 0.28 27.99

weighting point 2 8 1 18.7 21.84 0.83 83.50

Delivery queue 8 2 17.7 47.97 1.73 86.67gate2 8 1 15.7 6.10 0.20 19.83

•Scenario 2 [45-55 min agency wait, 1 agency pick up docks, 2 queue lines]

NameScheduled Time (HR) Capacity Total Entries

Avg Time Per Entry (MIN) Avg Contents % Utilization

docks 8 35 33 7.25 0.50 1.42

freezer1 8 30 8.6 126.88 2.39 7.97

freezer2 8 30 8.4 120.45 2.21 7.35

cooler 8 30 15.3 124.70 4.18 13.93

weighting point1 8 1 32.5 4.00 0.27 27.24

weighting point 2 8 1 13.9 30.66 0.86 86.15

Delivery queue 8 1 12.9 34.91 0.91 91.17

gate2 8 1 11.9 6.19 0.15 15.50

•Scenario 4 [30-40 min agency wait, 1 agency pick up dock, 1 queue line]

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Recommendations & Conclusion Simulation results showed that the main

problem/bottleneck lies with the agency waiting line, and once that problem can be eliminated, the process for the cooler/freezers will become more steady as well (due to items only coming out once agency arrives)

Add an extra shelf in the back corner of the cooler to utilize vertical space; will minimize time moving things around to get to item in back

Label designated areas within cooler/freezers Increase organization/decrease cycle time

Group items together

Look into possibility of opening another dock for P&W customers

Record cycle times of items

Recommend looking into other O.R. methods, like waiting line queuing theory to further optimize operations

By implementing some of these recommendations, we feel that it will really help the NTFB make their operations more efficient, and help them reach their goal of 50 million meals by 2011

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Thank you!