FIGHT AGAINST THE VIRUS Managing Global Supply Chain Risk at GIDGET - - PowerPoint PPT Presentation

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FIGHT AGAINST THE VIRUS Managing Global Supply Chain Risk at GIDGET - - PowerPoint PPT Presentation

FIGHT AGAINST THE VIRUS Managing Global Supply Chain Risk at GIDGET The Coronavirus Impact Who We Are? VIKRAM RASHI YISHAN RUIHAN GOLCHHA BAGADIA CHEN DING -MS in Global Supply -MS in Global Supply -MS in Global Supply -MS in Global


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SLIDE 1

FIGHT AGAINST THE VIRUS

Managing Global Supply Chain Risk at GIDGET – The Coronavirus Impact

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SLIDE 2

VIKRAM GOLCHHA RUIHAN DING

Who We Are?

RASHI BAGADIA

  • MS in Global Supply

Chain Management

  • Interned at Teradata,

Honeywell

YISHAN CHEN

  • MS in Global Supply

Chain Management

  • Worked at Unilever
  • MS in Global Supply

Chain Management

  • Worked at Ford Motor
  • MS in Global Supply

Chain Management

  • Interned at Accenture,

PwC

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SLIDE 3
  • 1. Executive Summary
  • 2. Base Model
  • 3. Assumptions
  • 4. Analysis
  • 5. Recommendations
  • 6. Risk Mitigation of Recommendations

TABLE OF CONTENT

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SLIDE 4
  • The disruption of supply chain caused by the spread of the

coronavirus

  • The inability of chinese factory to meet the demand globally
  • Resources allocation based on different scenarios
  • Whether to close factory in China permanently or temporarily?
  • Optimizing the profit
  • Mitigation of the global supply risk due to Coronavirus

EXECUTIVE SUMMARY

Challenges Key Focus Goals Recommendations

  • GIDGET should discontinue its policy of meeting all demands.
  • GIDGET should keep the facility in China despite production &

demand halt in China due to the virus

  • GIDGET should sell off 25% of Chinese plant capacities

Company

  • Global widget manufacturer GIDGET
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SLIDE 5

BASE MODEL

Background Case

  • Optimizing operation plan to maximize profits

considering the uncertainties in 3-demand and 2- currency exchange.

  • Multiple variables such as Capacity, Production

cost, Exchange rate, Transportation cost

Model

  • Two stage linear programming

○ Stage-One: Long-term decision

  • f opening/closing plants

○ Stage-Two: 6 possible scenarios

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SLIDE 6

ASSUMPTIONS

  • C. Every variables is uniformly distributed through the years.
  • D. Selling the capacity would not generate extra cost.

B.

After the virus, the demand and productivity would recover back to normal level.

  • A. The variables in the case phase 1 remains the same in conditions
  • f with/without virus in China.
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SLIDE 7

Satisfying or Not Satisfying Demand?

Roadmap of Thinking

Close or Keeping the Plant in China? Keep the Plant in China Fully or Partially?

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SLIDE 8
  • Without China being affected by Coronavirus

not satisfying all demand satisfying all demand

  • After China is affected by Coronavirus

From Location Y/N Indianapolis, USA 1 Cholula, Mexico Ankara, Turkey Ningbo, China 1

PROFIT $24,293,210

From Location Y/N Indianapolis, USA 1 Cholula, Mexico 1 Ankara, Turkey 1 Ningbo, China 1

PROFIT $13,080,710 PROFIT $20,152,377

From Location Y/N Indianapolis, USA 1 Cholula, Mexico Ankara, Turkey Ningbo, China From Location Y/N Indianapolis, USA 1 Cholula, Mexico 1 Ankara, Turkey 1 Ningbo, China

PROFIT $(10,378,567)

not satisfying all demand satisfying all demand

Quote from the case: “Bill Bowden continued to preach the benefits to not satisfying all demand”

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SLIDE 9

Satisfying or Not Satisfying Demand?

Roadmap of Thinking

Close or Keep the Plant in China?

Not Satisfying

Keep the Plant in China Fully or Partially?

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SLIDE 10

Expected profit of closing the plant in China

From Location Y/N Indianapolis, USA 1 Cholula, Mexico Ankara, Turkey Ningbo, China

Annual Profit= $ 20,152,377 End of Virus Time Decision point Production in China=0 Demand in China=0 Production in China= 0 Demand in China >= 0

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SLIDE 11

Expected profit of keeping the plant in China

Annual Profit= $17,152,377

From Location Y/N Indianapolis, USA 1 Cholula, Mexico Ankara, Turkey Ningbo, China 1 From Location Y/N Indianapolis, USA 1 Cholula, Mexico Ankara, Turkey Ningbo, China 1

Annual Profit= $24,293,210 End of Virus Time Decision point Production in China=0 Demand in China =0 Production in China > 0 Demand in China > 0 $20,152,377 < profit for closing plant

<

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SLIDE 12

Profit Breakeven Analysis of close/open plants

  • t= time taken for virus to

end

  • t’= time taken to reach the

profit break even point

t’ = 1.75 * t

t=6 months, t’=6*1.75 = 10.5 months t=12 months, t’=12*1.75 = 21 months

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SLIDE 13

Satisfying or Not Satisfying Demand?

Roadmap of Thinking

Close or Keep the Plant in China?

Not meeting

Keep the Plant in China Fully or Partially?

Open

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SLIDE 14

Expected profit of keeping China’s plant fully/partially

From Location Y/N Indianapolis, USA 1 Cholula, Mexico Ankara, Turkey Ningbo, China 1

Annual Profit= $24,293,210

From Location Y/N Indianapolis, USA 1 Cholula, Mexico Ankara, Turkey Ningbo, China 0.75

Annual Profit= $24,749,877

<

Selling 25% of Chinese plant will bring us higher annual profit

Keep Ningbo plant fully Keep Ningbo plant partially

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SLIDE 15

Shall we consider Carly’s suggestion, letting Ningbo

  • nly meet Chinese demand?

From Location Y/N Indianapolis, USA 1 Cholula, Mexico Ankara, Turkey Ningbo, China 0.75

Annual Profit= $24,749,877 Annual Profit= $23,922,377

<

From Location Y/N Indianapolis, USA 1 Cholula, Mexico Ankara, Turkey Ningbo, China 0.75

Open partially with export Carly’s suggestion: Open partially without export

We shouldn’t consider Carly’s suggestions!

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SLIDE 16

Sell off 25% of Chinese plant capacities

RECOMMENDATIONS

Keep the facility in China despite production & demand halt in China due to the virus Discontinue to meeting all the demand

1. 2. 3.

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SLIDE 17

RISK MITIGATION

Potential Risks: 1. A cut to capacity cannot easily be reversed if demand fluctuate. 2. Low working efficiency of the employees since the long time of unemployment . 3. Reluctance of employees to return to work since the psychological fear of coronavirus. Mitigation Actions: 1. Enhancing communications to eliminate the bullwhip effect and facilitate the lead time 2. Implementing Lean and Six Sigma in production to

  • ptimize the productivity

3. Launching short training to re-educate the employees 4. Providing incentives to employees for motivation

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SLIDE 18

Thank you!

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SLIDE 19

Appendix

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SLIDE 20

Without Coronavirus – not satisfying all demands

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SLIDE 21

Without Coronavirus – satisfying all demands

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SLIDE 22

With Coronavirus – not satisfying all demands

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SLIDE 23

With Coronavirus – satisfying all demands

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SLIDE 24

With Coronavirus – Keeping facility in China

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SLIDE 25

Without Coronavirus – Partially keeping facility in China with export

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SLIDE 26

Without Coronavirus – Partially keeping facility in China without export

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SLIDE 27

TWO-STAGE LINEAR PROGRAMMING FORMULATION CONSIDERING FUTURE SCENARIOS WHEN MEETING ALL THE DEMAND

Constraints:

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SLIDE 28

TWO-STAGE LINEAR PROGRAMMING FORMULATION CONSIDERING FUTURE SCENARIOS WHEN MEETING ALL THE DEMAND

DECISION OF SOURCING LOCATIONS

From Location

YES/NO

Indianapolis, USA 1 Cholula, Mexico 1 Ankara, Turkey 1 Ningbo, China 1

STATE IN FUTURE From / To Indianapolis, USA Cholula, Mexico Ankara, Turkey Ningbo, China STATE 1 Indianapolis, USA 300,000 Cholula, Mexico 100,000 50,000 Ankara, Turkey 50,000 Ningbo, China 200,000 STATE 2 Indianapolis, USA 300,000 Cholula, Mexico 125,000 25,000 Ankara, Turkey 25,000 Ningbo, China 75,000 100,000 STATE 3 Indianapolis, USA 300,000 Cholula, Mexico 50,000 Ankara, Turkey 50,000 Ningbo, China 300,000 STATE IN FUTURE From / To Indianapolis, USA Cholula, Mexico Ankara, Turkey Ningbo, China STATE 4 Indianapolis, USA 300,000 Cholula, Mexico Ankara, Turkey Ningbo, China 100,000 50,000 50,000 200,000 STATE 5 Indianapolis, USA 300,000 Cholula, Mexico Ankara, Turkey Ningbo, China 200,000 25,000 25,000 100,000 STATE 6 Indianapolis, USA 300,000 Cholula, Mexico Ankara, Turkey Ningbo, China 50,000 50,000 300000

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SLIDE 29

Constraints:

TWO-STAGE LINEAR PROGRAMMING FORMULATION CONSIDERING FUTURE SCENARIOS WITHOUT THE POLICY OF MEETING ALL THE DEMAND

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SLIDE 30

RESULTS OF 2-STAGE LINEAR PROGRAM CONSIDERING FUTURE SCENARIOS WITHOUT THE POLICY OF MEETING ALL THE DEMAND

DECISION OF SOURCING LOCATIONS

From Location

YES/NO

Indianapolis, USA 1 Cholula, Mexico Ankara, Turkey Ningbo, China 1

STATE IN FUTURE From / To Indianapolis, USA Cholula, Mexico Ankara, Turkey Ningbo, China STATE 1 Indianapolis, USA 300,000 Cholula, Mexico Ankara, Turkey Ningbo, China 50,000 200,000 STATE 2 Indianapolis, USA 300,000 Cholula, Mexico Ankara, Turkey Ningbo, China 25,000 100,000 STATE 3 Indianapolis, USA 300,000 Cholula, Mexico Ankara, Turkey Ningbo, China 50,000 300,000 STATE IN FUTURE From / To Indianapolis, USA Cholula, Mexico Ankara, Turkey Ningbo, China STATE 4 Indianapolis, USA 250,000 50,000 Cholula, Mexico Ankara, Turkey Ningbo, China 50,000 200,000 STATE 5 Indianapolis, USA 275,000 25,000 Cholula, Mexico Ankara, Turkey Ningbo, China 25,000 100,000 STATE 6 Indianapolis, USA 250,000 50,000 Cholula, Mexico Ankara, Turkey Ningbo, China 50,000 300,000

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SLIDE 31

To maximize the profit, GIDGET should open plants in USA and

  • China. We have 6 scenarios because of two possibilities on

exchange rate and three possibilities on demand. After deciding shun down of unnecessary plants, we adjust our unit flow plan according to which of the 6 scenarios we actually face in the real world. The unit flow plan for each of the 6 scenarios is shown in the orange/yellow cells in the previous slide.

Optimal Profit = (Sales) - (Production Cost) - (Duties) - (Transportation cost) - (Fixed Cost) = $24,293,210

>

$13,080,710

Close Mexico and Turkey plants

RESULTS OF 2-STAGE LINEAR PROGRAM CONSIDERING FUTURE SCENARIOS WITHOUT THE POLICY OF MEETING ALL THE DEMAND (cont.)

85.72% Increase in Optimal Profit