Prescriptive Analytics for Logistics Management

The First NIDA Business Analytics and Data Sciences Contest/Conferenceวันที่ 1-2 กันยายน 2559 ณ อาคารนวมินทราธิราช สถาบันบัณฑิตพัฒนบริหารศาสตร์

•What is Prescriptive Analytics? •Prescriptive Analytics & Competitive advantage •Case study: Making “Better” Decisions in Logistics Management using Prescriptive Analytics

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รศ.ดร.กาญจ์นภา อมรัชกุลสาขาวิชา Logistic Managementคณะสถิติประยุกต์ NIDA

นวมินทราธิราช 3003วันที่ 2 กันยายน 2559 10.45-11.15 น.

PRESCRIPTIVE ANALYTICS FOR LOGISTICS MANAGEMENT

Kannapha Amaruchkul

Logistics Management Program,

School of Applied Statistics, NIDA

2016-09-02 10:45-11:15 Rm3003

ANALYTIC VALUE ESCALATOR

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PREDICTIVE VS PRESCRIPTIVE ANALYTICS

Predictive Analytics

o Show trends for the KPIs

o Create spot opportunities

1. Revise Estimated Time of Arrival (ETA) based on specific driver history and real-time traffic

2. What will happen to revenue if 30% discount is given to customer in segment A123?

3. What will happen if we keep 2-week of inventory?

Prescriptive Analytics

o Determine where your KPIs should be

o “Prescribe” how to get there

1. Optimize route to minimize average travel time

2. What is the best discount plan that would maximize the total revenue?

3. What is the optimal inventory policy that minimizes the expected total cost subject to pre-specified customer service level?

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http://blogs-images.forbes.com/louiscolumbus/files/2015/07/Figure-1-SCM-Data-Volume-Velocity-Variety.jpg5

LOGISTICS NETWORK (SUPPLY CHAIN)

6Source: Simchi-Levi, D., & Kaminsky, P., & Simchi-Levi, E. (2007). Designing and Managing the Supply Chain: Concepts, Strategies and Case Studies. Boston: McGraw-Hill.

WHAT MAKES SUPPLY CHAIN MANAGEMENT DIFFICULT?

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Global optimization:

It is challenging to operate supply chain so that total systemwide costs are minimized, while systemwide service levels are maintained.

Conflicting objective among players

The process of finding the best systemwide strategy is referred to as global optimization.

Managing uncertainties:

In every supply chain, “the only thing certain is uncertainty.”

Customer demand will not be known in advanced, and machines will break down at some point in time.

Supply chain management needs to deal effectively with uncertainties.

KEY ISSUES IN SUPPLY CHAIN MANAGEMENT

Global optimization Managing risk and

uncertainty

Distribution network configuration

Inventory control

Production sourcing

Distribution strategies

Supply contracts

Strategic partnering

Outsourcing and offshoring

Product design

Information technology

Smart pricing8

COMPETING ON ANALYTICS

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Com

petitive A

dvanta

ge Stochastic Optimization

How can we achieve the best outcome taking account of

uncertaintyPrescriptive

Optimization How can we achieve the best outcome?

Predictive modeling What will happen next if…?

PredictiveForecasting What if these trends continue?

Simulation What could happen…?

Alerts What actions are needed?

Query What exactly is the problem?

DescriptiveAd hoc reporting How many, how often, where?

Standard reporting What happened?

Degree of complexity Davenport & Harris (2007)

EXAMPLES OF OPTIMIZATION MODELS

Model context Decision variables Objective function Typical constraints

Product mix Quantities of product to produce Maximize profit Resource limitations (e.g.,

production time, labor,

material); maximum sales

potential

Blending Quantity of materials to mix to

produce one unit of output

Minimize cost Demand requirements; resource

limitation

Transportation Amount to ship between sources of

supply and destination

Minimize total transportation

cost

Supply constraints; require

demands met at destinations

Multi-period production

planning

Quantities of product to produce in

each several tie periods; amount of

inventory to hold between periods

Minimize total production and

inventory costs

Limited production rates;

material balance equations

Marketing Allocation of advertising

expenditures; production quantities

Maximize profit Budget limitation; production

limitations; demand

requirements

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STOCHASTIC MODELS

Markov chain

Customer relationship management

Inventory control

Accounts receivable

Warranty policy

Queueing models

Transportation management

Warehouse management

Markov decision process (MDP)

Equipment replacement model

Lot sizing model (Wagner-Whitin algorithm)

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