service parts logistics

CastilloCastillo

ChuaChua

IbuyanIbuyan

Interesting Figures…Interesting Figures…

$400 Billion

$178 Billion per year

$40 Billion

80%$6 - $8 Billion per year

Challenges of Spare Challenges of Spare PartsParts

• High Demand Uncertainty

• Increase in Prices for Individual Parts

• Higher Service Requirements

• Financially Remarkable Stock-out Effects

• Large Variety

• Slow Usage

• High Criticality

• Obsolete Factor

Challenges of Short Challenges of Short Lifecycle ProductsLifecycle Products

• Inventory

• Procurement

• Capacity

• Forecast

• Disruptions

• Competition

• Long lead-times of components and spare parts

• Uncertainty in the prices of components and spare parts

• Unpredictable market response

IBM Global Chief Supply Chain Officer Study 2010

What has been done What has been done so far? so far?

Review of 200 Articles Revealed Main Themes of Researches

1.Risk minimization models that do not incorporate multi-echelon inventory equations.

2. Studies on spare parts multi-echelon models that do not attempt to understand system behavior when subjected to uncertainty.

Main Contributions to Main Contributions to Spare Parts Studies Spare Parts Studies

1. Easily Extensible Spare Parts Multi-echelon Multi-Indenture Model that Incorporate Risks

2. Alternative Solution Method: Excel Based Genetic Algorithm

3. Results of Analysis of Spare Parts Systems when Subjected to Uncertainty and Risks

Sherbrooke (1968; 2004); Kutanoglu et al (2005, 2007)

Sherbrooke (1968; 2004); Kutanoglu et al (2005, 2007)

Excel-based Genetic Algorithm (VBA)

Excel-based Genetic Algorithm (VBA)

3 RSM Experiments (2 Robust Design Experiments)• Demand Variability• Cost Variability

3 RSM Experiments (2 Robust Design Experiments)• Demand Variability• Cost Variability

200 Articles Reviewed200 Articles Reviewed

Risks Scenario Analysis Risks Scenario Analysis

• General Guidelines for Spare Parts Supply Chain Design

• Optimal Risk Mitigation Strategies

• General Guidelines for Spare Parts Supply Chain Design

• Optimal Risk Mitigation Strategies

Model Overview

System Considered

Inventory Control Point for Service Parts

Flow of Usable Spare Parts

Flow of Unrepaired Spare Parts

Regular Transport

Emergency Transport

Plant n

Retailer 1

Retailer 1

Retailer 2

Retailer 2

Retailer m

Retailer m

DC 1

DC 2

Plant 1

Retailer 3

Retailer 3

DC i

::

:

System DiagramSystem Diagram

Level 0 Part

Level 1 Part

Level 2 Part

Multi – Indenture Multi – Indenture Products SystemProducts System

Retailer DC Plant

Level 0 Part

Level 1 Part

Level 2 Part

Level 0 Part

Level 1 Part

Level 2 Part

Level 0 Part

Level 1 Part

Level 2 Part

Multi – Echelon Inventory Multi – Echelon Inventory Equation DevelopmentEquation Development

Solution Methodology

Excel-based Genetic Algorithm

Excel Template

Coding the Mathematical Model

VBA for Genetic Algorithm

Genetic Algorithm Parents Selection

Crossover Technique

Mutation Mechanism

Analysing Genetic Algorithm Behavior Over Time

Results

Outputs of Research

1. General Guidelines for Incorporating Risk to Spare Parts Supply Chain Design

2. Table of Risk Mitigation Strategies to Hedge against Different Types of Risks

General Guidelines for Robust General Guidelines for Robust Service Parts Supply ChainsService Parts Supply Chains

1. Always choose to place inventory at downstream elements first.

2. Lowering delays anywhere inside the system lead to more desirable supply chains in terms of cost.

3. While considering Rule 1, it is also always better to locate most of the inventory at facilities where majority of the repair operations are done.

4. Stock low criticality spare parts in the most upstream element of the supply chain.

5. Optimize the allocation of high criticality parts across all elements of the supply chain.

When Facing Demand Variability – Preventing “Bullwhip Effect” in Spare Parts

Systems

1. Lower delay times (ex. repair times) become more crucial in the face of demand variability.

2. Reduce delays at ALL facilities of the Spare Parts Supply Chain, even the facilities that receive little demand.

3. Repair should be done as much as possible at downstream elements to minimize total cost and its variability.

General Guidelines for Robust General Guidelines for Robust Service Parts Supply ChainsService Parts Supply Chains

When Facing Cost Variability

1. General Rules in Optimizing Service Parts Supply Chain Apply.

2. Unlike in the situation of demand variability, being robust to cost variability only requires the supply chain to minimize delays at FACILITIES WITH HIGH DEMAND.

General Guidelines for Robust General Guidelines for Robust Service Parts Supply ChainsService Parts Supply Chains

Risk Scenario 1st best Strategy 2nd best Strategy 3rd best strategy

High Demand Variability

Increased Responsiveness

Increased Inventory in all Sites

Increased Inventory at Plant and Retailer

Inventory Limit Increased Responsiveness

Increased Inventory at the DC

Increased Inventory at the Plant or at both Plant and DC

Extreme Demand Values

Increased Responsiveness

Increased Inventory in all Sites

Increased Inventory at DC and Plant

Inventory Cost Increased Responsiveness

Increased Inventory in all Sites

Increased Inventory at Plant and Retailer

Facility Cost Increased Responsiveness

Increased Inventory in all Sites

Increased Inventory at Plant and Retailer

Emergency Shipment Cost

Increased Responsiveness

Increased Inventory in all Sites

Increased Inventory at Plant and Retailer

Results of Risk RunsResults of Risk Runs

Total Average Cost Total Average Cost Across ScenariosAcross Scenarios

• Most effective Risk Mitigation strategy.

• Short lifecycle products are best served by a responsive supply chain (Cohen et al, 2006).

• Quick response to demand lessens penalty costs and fulfills a high service level requirement.

Increased Increased ResponsivenessResponsiveness

RecommendationsRecommendations

• Design of a C++ program that can execute the genetic algorithm faster.

• Adding more scenarios and conducting a full stochastic programming analysis.

• Design of alternative solution methodology to solve the mathematical model.

• Designing mathematical functions that can better approximate the pipeline inventory.