Prevention and Mitigation of Snowstorm in N.Y

Group 3 Jinbo Wang, Xulong Zhu, Mengqi Li, Guanlin Wang, Yu Guan MFE 634 Spring 2020

Topics to cover

• BACKGROUND • FLOWCHART • COPQ • AFFINITY DIAGRAM • FISHBONE CHART • QUALITY ASSESSMENT • PROCESS CAPABILITY

ANALYSIS

• DESIGN OF EXPERIMENT

• VALUE STREAM MAP

• ACCEPTANCE SAMPLING

PLAN

• SPC

• CONCLUSION

Background

A Snowstorm is a weather condition where snow is not falling but loose snow on the ground is lifted and blown by strong winds. Snowstorm characterized by strong sustained winds of at least 56 km/h (35 mph) and lasting for a prolonged period of time—typically three hours or more. Snowstorm can bring whiteout conditions, and can paralyze regions for days at a time.

Snow Depth Map

Snow Depth Map by 2016

When a snowstorm comes, there are the following problems: • Property damage • Personal safety • Traffic paralysis

Our task is to analyze the potential problems of Snowstorm in New York from an internal and external perspective, and explore solutions to prevent failures and solve problems through quality analysis methods.

Problem Statement

Process Chart

Snowstorm Forecast

Prepare Supplies

Snowstorm Coming

Clear Snow

Distribute Supplies

Snowstorm End

Flowchart Start

Monitoring Snowstorm

Detected? Keep improving the prevention planNO

Detection Building HumanEmergency

Plan

Wind Snowfall

MonitoringStation

Packed Isolator

V roof Training

Mitigation Plan

Police Straff Rescue TeamNew York State

Dep of Transportation

Sweeping Snow

Reassure stranded

passengers

Solution

Measure Sustainable

End

Yes

Cost of Poor Quality

Process Internal Failure External Failure Appraisal Prevention

Prepare(include both long and short term): Monitoring Snowstorm

Inspection Equipment Failure Test all equipment

Technical Support，Periodical Inspection

Emergency Notification False Notification Recheck

Building Reinforce Test Structural Strength

Power Backup Guarantee Equipment Failure

Business, Communication Failure

Make Available Alternative Power Resource

Build Shelters Shelter Collapsing People Getting Injured/Dying inspection of Shelters

Tents Support, Use Good Quality Material, Flood Proof Shelters

Stock Emergency Supplies

Storage Equipment Failure, Shortage Theft, Lost Periofical Inspection

Protect Property(temporary methods) Damage to Property Periofical Inspection

Cost Of Poor Quality

During:

Clear roads Sweeping Equipment Failure Traffic accident

Test sweeping equipment

Send Emergency Team Not enough training

Move Victims to Shelter

The Construction problem, Shelter Shortage

The Residents Were Relutanted to Evacuate Tents Support

Provide Necessity for Life

Water and Food Shortage

Complaints Were Raised by Residents

Standerization of Food and Water Supply

Provide Power Eletricity and Fule Shortage

Complaints Were Raised by Residents

Emergency Electrical Supply,Eletric Generater Backup

Rescue Inspection Equipment Failure Limited 911 Services

After:

Return of Victims Vehicle Damage and Shortage

The Residents Were Relutanted to Return Deport Colonias

Home, Schools, Hospitals Rebuilding

Materials and Equipment Shortage Insurance

Prepare for The Next Snowstorm

Periofical Inspection, Training

Affinity Diagram

Media Government Department of Transportation Residents Rescue Department

Late news update Insufficient places Low efficiency of snow clearner

Insufficient awareness of the disaster

Insufficient equipments

Low forecast effciency and accuracy Poor management Insufficient staff

Insufficient self recuse knowledge Low efficiency staff

Fishbone Chart

• Training the rescue worker and medical staff based on corresponding standards.

• Reduce clearing snow time through using new transportation methods.

• Verify the new monitor system.

Improvements to Current System (Suggestions)

Process Capability

• Random data with mean 21.7 and variance 6.8

• LSL= 10; USL=30 • x=̅ 21.71; σ=6.881 • Cp=0.53; Cpk=0.44

• Random data with mean 15.6 and variance 5.5

• LSL= 10; USL=25 • x=̅ 15.61; σ=5.312 • Cp=0.45; Cpk=0.34

-1

0

1

2

3

4

5

6

7

8

9

10

1 2 3 4 5 6 7

Pareto Chart of Factors

DOE DATA A B C Responeses

10 1 -1 7.96154454

10 1 -1 9.20314993

10 1 1 4.86028205

10 1 1 12.7564534

10 2 -1 14.8727997

10 2 -1 15.2598967

10 2 1 10.8465247

10 2 1 20.2900425

20 1 -1 1.82594053

20 1 -1 6.72531718

20 1 1 -1.24996489

20 1 1 11.8293398

20 2 -1 10.8597796

20 2 -1 20.0640941

20 2 1 9.19314748

20 2 1 21.6229455

Start

Specific affected area and

snowfall detected

Process

T = 1 day

Reliability: 50%

Accuracy: 70%

Media inform citizens to take

precautionary measures

Process

T = 1 day

Citizens Reserve Food,

Cancel long distance journey

Process

T = 1 day

Snowstorm is coming

Block high speeds in severe

areas

Process

T = 3 Hours

Transport department

shovel road and melt snow

Process

T = 1 Hours/3 Hours

Rescuing victims and

homeless people

Process

T = 8 Hours

Snowstorm end

VSM(CURRENT)

Start

Specific affected area and

snowfall detected

Process

T = 1 day

Reliability: 70%

Accuracy: 90%

Media inform citizens to take

precautionary measures

Process

T = 1 day

Citizens Reserve Food,

Cancel long distance journey

Process

T = 1 day

Snowstorm is coming

Block high speeds in severe areas,

Transport department shovel road and

melt snow

Process

T = 3 Hours

Rescuing victims and

homeless people

Process

T = 8 Hours

Snowstorm end

VSM(FUTURE)

Statistical Variables

● 𝝰: Producer’s Risk - short cleaning time design is rejected. ● 𝞫: Consumer’s Risk - long cleaning time design is accepted. ● AQL: Acceptable Quality Level - the level of defectiveness, set by

the producer as a minimum “goal”, citizens usually like zero defectiveness but there are always something uncertainty.

● Lot Size: Total number of items - estimated number of snow cleaner equipment total in the system.

● LTPD: Lot Tolerance Percent Defective - the maximum defectiveness that the producer will accept in the product as to not affect the citizens negatively.

Assignment of Variables

⍺ 0.05

β 0.2

AQL 0.1

LTPD 0.2

Lot Size 100

Nomograph Analysis

• n= 80 • c= 12

Operating Characteristic Curve (OC Curve)

● An OC Curve visualizes the probability for a sampling plan, showing the probability of accepting a lot given the percent defectiveness

● This probability is calculated using a Binomial Distribution

Excel OC Curve PD Bin_Pa

0 1.00000

0.01 1.00000

0.02 1.00000

0.03 0.99999

0.04 0.99987

0.05 0.99926

0.06 0.99713

0.07 0.99160

0.08 0.97999

0.09 0.95931

0.1 0.92693

0.11 0.88131

0.12 0.82238

0.13 0.75172

0.14 0.67224

0.15 0.58770

0.16 0.50212

0.17 0.41926

0.18 0.34220

0.19 0.27310

0.2 0.21321

0.00000

0.20000

0.40000

0.60000

0.80000

1.00000

1.20000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41

Effect n and c on OC curve

0.21 0.16291

0.22 0.12188

0.23 0.08932

0.24 0.06415

0.25 0.04517

0.26 0.03119

0.27 0.02113

0.28 0.01405

0.29 0.00917

0.3 0.00587

0.31 0.00369

0.32 0.00228

0.33 0.00138

0.34 0.00082

0.35 0.00048

0.36 0.00028

0.37 0.00016

0.38 0.00009

0.39 0.00005

0.4 0.00003

0

0.2

0.4

0.6

0.8

1

1.2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Fraction detective

OC Curve Minitab vs Excel

minitab excel

Comparisons Between Minitab & Excel

Control Chart • An X-bar and R (range) chart is a pair of control charts used with processes

that have a subgroup size of two or more. The standard chart for variables data, X-bar and R charts help determine if a process is stable and predictable.

• The p-chart is a type of control chart used to monitor the proportion of nonconforming units in a sample, where the sample proportion nonconforming is defined as the ratio of the number of nonconforming units to the sample size.

• The c-chart is a type of control chart used to monitor "count"-type data, typically total number of nonconformities per unit.

Data For R,P&C Charts

Data Generate:

• Random Integer in Minitab between 10-20 for Cleaning snow time (Two Trails with 100 data each)

X-bar R-chart(Cleaning Time)

P-chart(Cleaning Time)

C-chart(Cleaning Time)

Summary of Problems detected

The problems are detected by the citizens complaint:

• They can’t get supplies during the road blocked.

• The power off because of wire is broken.

• Disconnect Internet.

• Not enough Heat.

Sending more snow clearer can reduce cleaning time, but the cost is higher.

Inefficient usage of snow cleaner and rescue crew.

Solution proposed

Train the rescue worker to make the rescue much faster.

Improve the monitor function and improve snow removal efficiency, avoid wasting resources.

Storage more food and medicine in different area close to where people live and check the quality of these things at a regular interval.

Simplify the procedure of news report and material distribution.

Conclusion

By applying COPQ, flowchart and affinity diagram, we can find there are many extra cost in our project.

By acceptance sampling plan we find increase transportation equipment and improve the monitor system, which can effectively reduce snow cleaning time. So our system is optimized.

The SPC leads our project to the right rack and keep improving.

Thank you!