Optimization Automation in Supply Chain

Bala. Padmakumar
Director & CEO,
OptiRisk India

© 2010-13 OptiRisk India (P) Ltd, All rights reserved

1. Business Challenges

2. Optimization

3. SCN Analysis & Redesign

4. Inventory Analysis

5. Transportation Scheduling

6. Solution Approach

7. Summary

© 2010-13 OptiRisk India (P) Ltd, All rights reserved 2

Uncertain Demand
Uncertain Demand Angry Investors
Angry Investors Unnecessary trips
Unnecessary trips

Regulatory Changes
Regulatory Changes Too far from Suppliers
Too far from Suppliers Uncertain NPV
Uncertain NPV

Lower ROI
Lower ROI Shrinking Margins
Shrinking Margins Competitive Pressure
Competitive Pressure

Supply-Demand Gap
Supply-Demand Gap LTL Fleets
LTL Fleets Too far from Customers
Too far from Customers

Stock Outage
Stock Outage Higher Investment
Higher Investment Delayed Deliveries
Delayed Deliveries

Higher Cost of Capital
Higher Cost of Capital Unhappy Customers
Unhappy Customers Escalating Cost
Escalating Cost

Subpar Revenue Growth
Subpar Revenue Growth Discounted Stock Price
Discounted Stock Price Bloated Inventory
Bloated Inventory

Production-line stoppage
Production-line stoppage Stressed Employees
Stressed Employees Quality Issues
Quality Issues

Product Obsolesce
Product Obsolesce

People

Policy

Culture
Process Plan

Quality

Integration Communication

- Winston Churchill

Achieve Business objective with minimal resources

Generate Plans in minutes rather than hours or days


Strategic Long Term 1+ years

Supply Chain Network Design
Resource Acquisition (Capital)

Tactical Medium Term Monthly / Quarterly

Production or Distribution Planning
Resource Allocation (Machine and labors)

Operational Short Term Weekly/ Daily

Transport Planning and Scheduling
Resource Scheduling


SC Network optimization Production planning
Locate facilities Optimal production schedule
Match Supply & Demand Meet plant floor constraints.
Managing seasonality, and Benefits :
Reduce carbon footprint. Improved throughput
Benefits: Reduced costs,
5-15% reduction in SC costs Reduced inventory
Better service level Leaner plants

Revenue / Yield Management
Sourcing Selection Choose best price
Select Suppliers Indentify Optimal Commission
Determine Order Quantities Benefits :
Benefits : Higher sales volume
Enhanced S&OP capability, Better Margins
2-5% reduction in mfg costs

Transportation Scheduling Operational Inventory Management
Fleet Selection Order quantity
Route Plan / Schedule Order time
Benefits : Supplier identification
10-30% reduction in Transport Benefits :
cost Increased on-time deliveries 10-30% reduction in inventory
Improved service level



2. Optimization





7. Summary


Optimization is a
decision making process
and a set of related tools that
employ mathematics,
Algorithms, and computer software not
only to sort and organize data, but to
use that data to make
Recommendations faster and better
than humans can.
- The Optimization Edge, Steve Sashihara


Example : Find the shortest path for delivery
A
Warehouse
60

115 80
105
Deliveries Possible Routes Time to process
manually

70
3 3! = 6 B
35
10 10! = 3,628,800 C
Route Possible Distance(KM)
13 13! = 6,227,020,800 3 centuries
# Routes
1 A–B–C 245
20 20! = 11 billion
centuries 2 A–C–B 315
3 B–A–C 370
10 seconds to calculate each route manually 4 B–C–A 315
5 C–A–B 370
Best Route 6 C–B–A 245

Wherever decisions are made !!
Wherever decisions are made

Where, Decisions require resources for implementation !!
Where, Decisions require resources for implementation !!

Capital
Capital Allocate
Allocate
People
People Acquire, schedule, assign, Train
Acquire, schedule, assign, Train
Equipment
Equipment Acquire, schedule, Locate
Acquire, schedule, Locate

Facilities
Facilities Locate, schedule
Locate, schedule

Vehicles
Vehicles Acquire, route, schedule
Acquire, route, schedule

Raw Material
Raw Material Acquire, assign
Acquire, assign

Time
Time Allocate, Schedule
Allocate, Schedule


Inventory
Optimization

Sch utin tion
(Ro orta

Op
Ne izat
ed g,
)
ng

t im
tw ion
uli
nsp

ork
Tra

OR Key
Areas
Al l o ,
Ma

Lay ility
cat
Job nes

Pla out
ng
chi

ion

c

nni
Fa
Production
Planning


What-If Analysis

Collaboration
Courtesy: IBM Limited


2 Chilean Forestry firms* Timber Harvesting $20M/yr + 30% fewer trucks

UPS* Air Network Design $40M/yr + 10% fewer planes
South African Defense* Force/Equip Planning $1.1B/yr
Motorola* Procurement Mgmt $100M-150M/yr
Samsung Electronics* Semiconductor Mfg 50% reduction in cycle times
SNCF (French RR)* Scheduling & Pricing $16M/yr rev + 2% lower op ex
Continental Airlines* Crew Re-scheduling $40M/yr
AT&T* Network Recovery 35% reduction spare capacity
Grant Mayo van Otterloo* Portfolio Optimization $4M/yr
Indeval* Securities Trade Settlement $150M/yr financing costs
Midwest ISO* Energy Grid Management $2.1B to $3B savings over 4 years

*Franz Edelman Competition Finalists, Science of Better, http://www.scienceofbetter.org , Published Case
Studies
© 2010-13 OptiRisk India (P) Ltd, (P) rights reserved
© 2010-13 OptiRisk India All Ltd, All rights reserved 15


2. Optimization





7. Summary


Warehouse

Production Facility
Consumer
Highway

10 – 30% reduction in overall Supply chain and Logistics Cost

Input Output

What-if Analysis


Software GUI *

MANU
MANU
MANU

MANU
MANU
MANU
MANU
MANU
MANU
MANU

MANU
MANU
MANU
MANU

MANU
MANU
MANU
MANU

MANU

MANU
MANU

*Courtesy : IBM ILOG LogicNet+ XE

What we Delivered
What we Delivered
OptiRisk developed SCN model
OptiRisk developed SCN model
suggested reconfiguring order delivery
suggested reconfiguring order delivery
to reduce out-bound transportation
to reduce out-bound transportation
cost, overall taxes, and inventory level.
cost, overall taxes, and inventory level.
In addition, it suggested potential
In addition, it suggested potential
warehouse locations, plants, and their
warehouse locations, plants, and their
optimal capacity for future growth. At
optimal capacity for future growth. At
the end, "what-if" analysis is done to
the end, "what-if" analysis is done to
study the impact of proposed Goods &
study the impact of proposed Goods &
Services Tax (GST) on the existing and
Services Tax (GST) on the existing and
proposed Supply Chain Network.
proposed Supply Chain Network.


Existing Supply Linkage Optimal Supply Linkage

© 2010-13 OptiRisk India (P) Ltd, (P) rights reserved
© 2010-13 OptiRisk India All Ltd, All rights reserved 22


2. Optimization





7. Summary


Manufacturing Manufacturing and Primary / RDC’s Secondary / Local Customer
Packaging DC’s
Process
FG
FG
WIP FG

FG
FG


Shorter order to delivery lead times

Increased penalties for product not being available

Expect similar service for all product categories

FG


Uncertainty in demand (seasonality, promotions, changing customer mix, etc).
creates a “Bullwhip” effect as we move up the supply chain

Demand variability is amplified up in the supply chain; upstream
Demand variability is amplified up in the supply chain; upstream
nodes face greater uncertainty, further increasing inventory costs
nodes face greater uncertainty, further increasing inventory costs

Global supply chains have increased lead time and variability from suppliers
Plant constraints (downtime, tight capacity) reduces reliability of completing
orders on-time, in-full

Orders are placed weekly

(2,0)
(2,0)
 Inventory Turns: 26.0
Domestic (2,0)
Plant (2,0)
 Working Capital: $5.2 Million
Customers
 Product Cost: $1 per unit

(36,7)  Inventory Turns: 5.4
International
(2,0)  Working Capital: $12.5 Million
(2,0)
Plant
(2,0) (Increase: 140%)
Orders are placed monthly  Product Cost: $0.50 per unit

(36 = Transportation time, 7 = Std dev of transportation time)


•• Only half of companies that believe they do multi-echelon optimization
Only half of companies that believe they do multi-echelon optimization
actually take into account both demand and supply variability.
actually take into account both demand and supply variability.
•• Most use home-grown systems.
Most use home-grown systems. Source: Aberdeen Group, 2004


Each echelon and each site is There is no standard
Inventory is not method for determining
optimizing their own inventory coordinated across the
without regard to the supply chain inventory policy at
facilities. different locations.

Supplier 1
Plant 1

Supplier 2

Plant 2
Supplier 3


Inventory Questions:
Inventory Questions: 1. Maximize Service Level
1. Maximize Service Level
1. How much per SKU?
1. How much per SKU? Objective
Objective 2. Minimize Inventory Cost
2. Minimize Inventory Cost
2. Where?
2. Where?

Procurement Manufacturing Packaging & Customer
Distribution Service

What impact does Which facilities How should How should I take
each supplier have should be make to shipments and advantage of
on the entire supply order or make to policies be centralization to
chain? stock? coordinated? reduce inventory?


The Efficient
Frontier – global
optimization

Global Optimization enables
Global Optimization enables
your supply chain to move
your supply chain to move
towards the “efficient
towards the “efficient
frontier” performance
frontier” performance

Current Inventory Position

The ability to evaluate new
The ability to evaluate new
supply chain structures and
supply chain structures and
processes allow
processes allow
organizations to shift the
organizations to shift the
efficient frontier
efficient frontier

70% 80% 90% 100%


Items FY11-12 -1 Day Peer Avg Best Competitor Units
Days in Inventory 71 70 61 53 days
Revenue 12,842 12,842 12,842 12,842 Cr
EBITDA 903 903 903 903 Cr
COGS + R&D expenditure 11,939 11,939 11,939 11,939 Cr
R&D expenditure (2%) 239 239 239 239 Cr
COGS 11,700 11,700 11,700 11,700 Cr

Capital tied up in Inventory 2,276 2,244 1,955 1,699 Cr
Inventory carrying cost 0.03% 0.03% 0.03% 0.03% per day
Inventory carrying cost per day 0.68 0.67 0.59 0.51 Cr
Inventory carrying cost per year 249 246 214 186 Cr
Capital freed-up 32 321 577 Cr
Reduction in Operating expense 3.5 35.1 63 Cr

Net Profit 607 610 642 670 Cr
Capital Employed 3656 3624 3335 3079 Cr
Net Profit Margin 4.73% 4.75% 5.00% 5.22% %
Asset Turnover 3.51 3.54 3.85 4.17 Times
ROIC 16.60% 16.84% 19.25% 21.76% %


21.76% 53 5.22% 670 4.17
19.25% 61 5.00% 642 3.85
16.84% 70 4.75% 610 3.54
* *
BC 16.60% 71 BC 4.73% 607 BC
*
3.51
RoCE DiI NPM NP Asset Turnover

Profit Profit Sales
RoCE = = x
Capital employed Sales Capital employed

Capital Employed
*
BC 3656
3624
Target Company FY11-12 3335
-1 day reduction in DiL
3079
DiI - Peer Average
DiI - Best Competitor
BC – Base Case
DiI – Days in Inventory


10% RoCE 15% RoCE 20% RoCE

Desired level of service
Reduced operation cost
Profit 21.76%
21.76%
Enhanced profitability
Sales 19.25%
19.25% Increased asset utilization
(Margin) 16.84%
16.84%
16.6%
16.6%
Increased RoCE
Increased competitive
advantage

Target Company FY11-12 - 71 days
-1 day reduction in DiI – 70days
Sales
Cap Emp DiL - Peer Average - 61 days
(Asset Turn) DiI - Best Competitor – 53 days



2. Optimization





7. Summary


Shareholder Value

ROI

Profit Investment

Cost Revenue # Trucks

Less mileage Business
(10 to 30% )

Service Level .

Planning Lead time
Errors (5 to 10%)



2. Optimization





7. Summary


Data driven Approach
Robust solution for uncertain environment Observ
e
• Expected variation,
• Rare events (Tail Risk)

Optimiz Analyz
Identify optimization opportunities e
e
Prioritize it
Implement one or a group
Stabilize solution
Go to the next opportunity


Other RDBMS

Database DB2

Top Management
.
.
.
.
Web Server Intranet

Firewall Planning & Execution
External Staffs
SPSS Server Router
(Optional)

Optimization
Server

Dealers
Internet

External Data Server
(Optional) Suppliers

• Lower resource
consumption
• Reduced Carbon Emission
• Greener Earth

• Better Returns
• Higher Satisfaction
• Superior Value
• Happy Customers

• Lower Stress
• Improved Employee
satisfaction
• Higher Morale
Dealers

Suppliers



2. Optimization





7. Summary


India 14 % 14%

Japan 11 %

Europe 10 %

USA 8%

0 5 10 15 20


___________________________________________________________________________________________________________________________


Bala. Padmakumar
Ph: +91 98406 18472 / +91 44 4501 8472
Email: optimize@optiriskindia.com
Web: http://www.optiriskindia.com/

Asia Pacific, Africa, Australia & Europe & America:
Middle East: OptiRisk R&D House,
No 12, 25th Cross Street One Oxford, Uxbridge,
Thiruvalluvar Nagar , Middlesex, UB9 4DA,
United Kingdom
Thiruvanmiyur,
Chennai –600041, India


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