Supply Chain Planners: A Case from the CPG Industry Authors: Aura - - PowerPoint PPT Presentation

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Supply Chain Planners: A Case from the CPG Industry Authors: Aura - - PowerPoint PPT Presentation

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A Decision Support Model for Staffing Supply Chain Planners: A Case from the CPG Industry Authors: Aura C. Castillo & Ethem Ucev Advisor: Roberto Perez-Franco MIT SCM ResearchFest May 22-23, 2013 Agenda Research Question

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SLIDE 1

A Decision Support Model for Staffing Supply Chain Planners: A Case from the CPG Industry

Authors: Aura C. Castillo & Ethem Ucev Advisor: Roberto Perez-Franco MIT SCM ResearchFest May 22-23, 2013

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SLIDE 2

Agenda

  • Research Question
  • Motivation
  • Methodology
  • Analysis
  • Conclusions
  • Recommendations

May 22-23, 2013 MIT SCM ResearchFest 2

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SLIDE 3

Background

MIT SCM ResearchFest 3 May 22-23, 2013

Why Centralization?

  • Economies of scale
  • Control
  • Standardization
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SLIDE 4

Research Question

MIT SCM ResearchFest 4

What is the right number of planners?

May 22-23, 2013

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SLIDE 5

Motivation

  • Increased visibility
  • Adaptability to changes
  • Costs of under/over staffing
  • Employee morale and productivity

MIT SCM ResearchFest 5 May 22-23, 2013

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SLIDE 6

Methodology

MIT SCM ResearchFest 6

Qualitative

  • Site visit
  • Interviews
  • Identification of workload factors

Quantitative

  • Templates for data collection
  • Statistical method and software

selection

  • Data analysis and Model

May 22-23, 2013

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SLIDE 7

What factors influence planner’s workload?

Variables analyzed:

  • 1. Rescheduling Messages
  • 2. Number of Resources
  • 3. MD Complexity
  • 4. Scheduling Complexity
  • 5. Initiatives
  • 6. Manufacturing Sites
  • 7. Number of Codes
  • 8. Supplier Schedule Performance
  • 9. MAPE

10.Number of Contractors 11.Flexibility and Responsiveness 12.Forecasted tightness of supply chain capacity 13.Avg days IOH

May 22-23, 2013 MIT SCM ResearchFest 7

Scheduling Complexity Number of codes Master Data Complexity

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SLIDE 8

7 of the variables analyzed are relevant, while 2 show very low prediction power

MIT SCM ResearchFest 8 May 22-23, 2013

Results using bivariate correlation

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SLIDE 9

R2 = 99% - Running multiple regression with all variables as predictors

  • Hypothesis: The independent

variables used in this model have an effect on the number

  • f planners.
  • Null Hypothesis: All effects are

zero.

May 22-23, 2013 MIT SCM ResearchFest 9

Adjusted R2 = 99.5% F-ratio = 1,871 Probability > F = <0.0001*

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SLIDE 10

The initial model is not statistically valid

May 22-23, 2013 MIT SCM ResearchFest 10

Evidence of heteroscedasticity Normal distribution of residual plots Multicollinearity among independent variables - p-values>0.05

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SLIDE 11

The 4 most powerful predictors according to Stepwise Regression

May 22-23, 2013 MIT SCM ResearchFest 11

Adjusted R2 = 98% F-ratio = 1,001.8 Probability > F = <0.0001*

  • 1. Rescheduling messages
  • 2. Master Data complexity
  • 3. Number of contractors
  • 4. Flexibility and responsiveness
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SLIDE 12

The final model is statistically significant

May 22-23, 2013 MIT SCM ResearchFest 12

Parameter Prob>|t| VIF Intercept <.0001 1-Rescheduling Messages <.0001 5.94 3-MD Complexity[2-1] <.0001 8.20 3-MD Complexity[3-2] <.0001 3.14 3-MD Complexity[4-3] <.0001 4.84 3-MD Complexity[5-4] <.0001 3.08 10-Number of Contractors <.0001 6.03 11-Flexibility and Responsiveness[2-1] 0.006 2.41 11-Flexibility and Responsiveness[3-2] 0.001 2.86 11-Flexibility and Responsiveness[4-3] <.0001 7.33 11-Flexibility and Responsiveness[5-4] <.0001 8.99

Normal distribution of residual plots No evidence of heteroscedasticity No evidence of multicollinearity among independent variables - p-values < 0.05

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SLIDE 13

Final mathematical model

May 22-23, 2013 MIT SCM ResearchFest 13

𝐹𝑑𝑒𝑗𝑛𝑏𝑒𝑓𝑒 π‘œπ‘£π‘›π‘π‘“π‘  𝑝𝑔 π‘žπ‘šπ‘π‘œπ‘œπ‘“π‘ π‘‘ = 21.5 + 0.0008 Γ— π‘†π‘“π‘‘π‘‘β„Žπ‘“π‘’π‘£π‘šπ‘—π‘œπ‘• 𝑁𝑓𝑑𝑑𝑏𝑕𝑓𝑑 +𝑁𝐸 π·π‘π‘›π‘žπ‘šπ‘“π‘¦π‘—π‘’π‘§ Γ— 1 => 2 => 3 => 4 => 5 => π‘“π‘šπ‘‘π‘“ βˆ’9.38 βˆ’13.7 βˆ’11.05 βˆ’14.18 π‘œπ‘π‘’β„Žπ‘—π‘œπ‘• + 0.27 Γ— 𝑂𝑣𝑛𝑐𝑓𝑠 𝑝𝑔 π·π‘π‘œπ‘’π‘ π‘π‘‘π‘’π‘π‘ π‘‘ +πΊπ‘šπ‘“π‘¦π‘—π‘π‘—π‘šπ‘—π‘’π‘§ π‘π‘œπ‘’ π‘†π‘“π‘‘π‘žπ‘π‘œπ‘‘π‘—π‘€π‘“π‘œπ‘“π‘‘π‘‘ Γ— 1 => 2 => 3 => 4 => 5 => π‘“π‘šπ‘‘π‘“ 1.22 2.49 17.61 2.65 π‘œπ‘π‘’β„Žπ‘—π‘œπ‘•

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SLIDE 14

Conclusions

  • This reduced model could serve as a tool to estimate the number
  • f production and material planners
  • Results contrary to what common sense would suggest (number of

SKUs or complexity of the supply chain).The most critical predictors are:

  • The number of rescheduling messages
  • Master data complexity
  • Number of contractors
  • Flexibility and responsiveness of the supply chain

These factors explained more than 98% of the variance in the required number of planners.

May 22-23, 2013 MIT SCM ResearchFest 14

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SLIDE 15

Recommendations

  • Tracking of data is necessary in order to avoid estimation of

data points.

  • Elimination of variables - We recommend finding an alternative

way to measure Forecasted tightness of supply chain capacity for future analyses.

May 22-23, 2013 MIT SCM ResearchFest 15