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Learning of Metric-Based Supply-Chain analysis

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Jing Li

on 5 February 2013

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Transcript of Learning of Metric-Based Supply-Chain analysis

Nodes' Features:
Location (echelon & index)
Supportability
Unit Price per item of the Product
Reliability
Failure Rate(demand/year)
Maintainability
Time to Repair(MTTP days/repair)

Network's Features:
Inventory -> Availability degradation New Algorithm Example (Resilience Experiments) Best Solutions:
<Individual: candidate = [0, 201, 53, 85, 176, 74, 70, 41, 56, 38, 58], fitness = [0.9507329877928855, 77125.0], birthdate =
1358796826.91> 1. Same Investment -> Difference in Availability?





2. Same Availability -> Difference in Investment? degradation New Algorithm Example (Resilience Experiments) Best Solutions:
<Individual: candidate = [0, 265, 45, 72, 148, 74, 0, 56, 36, 56], fitness = [0.632453096818729, 75875.0], birthdate = 1357333923.98>

re-allocation New Algorithm Example (Resilience Experiments) Best Solutions:
<Individual: candidate = [0, 286, 45, 72, 148, 74, 0, 56, 73, 117], fitness = [0.9704325310649923, 88175.0], birthdate = 1357333926.14> Supply-Chain Resilience Analysis Supply-Chain Vendor Customers Metric-Based? Metric-Based Supply-Chain Vendor Intermediate Central Intermediate Field Field Field Field Field Field Customers = average time to procure item i from a vendor to the depot (location 1 in echelon 0). echelon
index (UC($/item), N(t=1yr)(demand/yr), MTTP(days/repair)) Example of
Network of SC assumptions Improving the Computational Efficiency of Metric-Based Spares Algorithms David Nowicki, Ph.D. and Jose Emmanuel Ramirez-Marquez, Ph.D. Demand at the most forward stocking locations follows a stationary, Poisson process. SC Characteristics (UC($/item), N(t=1yr)(demand/yr), MTTP(days/repair)) Operational Investment

Operational Availability Network of SC Supply Chain Resilience Economic Considerations and Metrics Ivan Hernandez, David Nowicki* and Jose Emmanuel Ramirez-Marquez Need to allocate Inventory / Add Stock into Networks' nodes SC Degradation Event 11 Decision Variables:
Inventory / Stock What if... Total degradation
? Stock = 0 Degradation: SC Reallocation
Event 11 Reallocation Inputs Defined variables:
Unit Cost, Item Demand Rate, Process Time, Replenishment Time, Repair Time, Starting Ratio, etc. Process of Software (Python) Outputs degradation New Algorithm Example (Resilience Experiments) Best Solutions:
archive size 1
<Individual: candidate = [0, 265, 45, 72, 148, 74, 95, 56, 36, 56], fitness = [0.9504760815251334, 75875.0], birthdate = 1357333283.3> [Vender, central-1,field-102, field-101, intermediate-11, field-112, intermediate-12, field-111, field-122, field-121] [Availability, Investment] Definition on algorithm Type & Object Function Value: Definition on Supply Chain: Object Function Value: Availability = 95% Step 1: Allocate Stock Step 2: Reallocate the Stock Inputs Outputs [Restored Availability, Increased Investment] Intermediate-12 failed:
interdictionIntermediate = [1,1,1,1,1,1,0,1,1,1] #12 [Vender, central-1,field-102, field-101, intermediate-11, field-112, intermediate-12, field-111, field-122, field-121] [Degraded Availability, Current Investment] (UC($/item), N(t=1yr)(demand/yr), MTTP(days/repair)) Operational Investment

Operational Availability Network of SC Decision Variables:
Inventory / Stock What if... Partial Degradation
? Stock =
Stock * x% Partial
Degradation: Reallocation? Step 2: Reallocate the Stock Inputs Outputs Intermediate-12 failed:
interdictionIntermediate = [1,1,1,1,1,1,0.5,1,1,1] #12 SC Degradation Event 11 1. How to re-allocate the path?

2. How to calculate availability when it is partially degraded? Add later ... What kind of partial degradation? Choice 1:
The maximum stock is limited
Choice 2:
The maximum float(shipment) of link (Which link?) is limited
Choice 3:
Temporarily stock lose
etc. (UC($/item), N(t=1yr)(demand/yr), MTTP(days/repair)) Network of SC Decision Variables:
Inventory / Stock What if... Redefine the network
? Inter.2 ->
Inter.2&Inter.3 Comparison
of Alternative Network Design: Comparison: Step 1: Allocate Stock Inputs Outputs 1. Equal Investment -> Difference in Availability?








2. Equal Availability -> Difference in Investment? They depend on... Nodes' Features:
Location (echelon & index)
Supportability
Unit Price per item of the Product
Reliability
Failure Rate(demand/year)
Maintainability
Time to Repair(MTTP days/repair) Future: 1. More futures added to SC:
Shipment ability of link; Maxim & minim stock; other costs
2. Analysis in Availability:
Availability analysis on given SC & Availability comparison
etc. Current: Stock allocation for defined Availability Results' Summary Resilience = (Ar - Ad) / (Ao - Ad) [Lead Time Demand] + [Safety Stock] = Re-Order Point Availability:
In what percentage of customers' demand could be fulfill 1.Demand at the most forward stocking locations (also known as field stocking locations or the operational stocking locations) follows a stationary, Poisson process.
2.Repair times are independent and Palms theorem is invoked (Palm, 1938) for calculating expected backorders and is valid for any repair time distribution.
3.There is a first-come, first-serve repair priority within the repair facilities and a one-for-one replenishment policy between facilities.
4.No waiting or batching of items before a repair begins on an item; also referred to as the infinite channel queuing assumption or infinite repair capacity assumption or uncapacitated production-distribution system assumption.
5.There is an infinite (uncapacitated) source of inventory. Assumptions Summary in results Disruption Availability Restoration Availability Investment Stock Allocation (UC($/item), N(t=1yr)(demand/yr), MTTP(days/repair)) Operational Investment

Operational Availability (UC($/item), N(t=1yr)(demand/yr), MTTP(days/repair)) Operational Investment

Operational Availability (UC($/item), N(t=1yr)(demand/yr), MTTP(days/repair)) Operational Investment

Operational Availability (UC($/item), N(t=1yr)(demand/yr), MTTP(days/repair)) Operational Investment

Operational Availability Target:
<data key="BudgetConstraint">75875</data> Target:
<data key="TargetA0">0.95</data> 1. Same Investment -> Difference in Availability?





2. Same Availability -> Difference in Investment? degradation New Algorithm Example (Resilience Experiments) Best Solutions:
<Individual: candidate = [0, 201, 52, 83, 176, 72, 70, 41, 54, 36, 56], fitness = [0.9059682579554748, 75900.0], birthdate =
1358796917.6> [Vender, central-1,field-102, field-101, intermediate-11, field-112, intermediate-12, intermediate-13, field-111, field-131, field-121] [Degraded Availability, Current Investment] [Vender, central-1,field-102, field-101, intermediate-11, field-112, intermediate-12, intermediate-13, field-111, field-131, field-121] [Degraded Availability, Current Investment] Process of Software
(Python) Summary in results Disruption Availability Restoration Availability Investment Stock Allocation Echelon SC Degradation Event 11 Comparison in Total Degradation: SC Degradation Event 11 Original Network: Restored network: Resilience by SC Location and Investment Ar: Restored Availability; Ao: Original Availability; Ad: Degraded Availability Summary in Resilience by SC Location and Investment Original Network: Restored network:
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