Collaborative Optimization for complex categories · Collaborative Optimization for complex categories. ... not more than three suppliers in total and not more than two per location

Helping Supply Management to excel in a world of growing complexity

CIPS UAE User group, Dubai June 22nd, 2011

Collaborative Optimization for complex categories

2A.T. Kearney 10/10.2007/27200d

This document is exclusively intended for selected participants of

the CIPS UAE User group, Dubai June 22nd, 2011

. Distribution, quotations and duplications – even in the form of

extracts – for third parties is only permitted upon prior written

consent of A.T. Kearney.

A.T. Kearney used the text and graphs compiled in this report

in a presentation; they do not represent

a complete documentation of the presentation.

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Content

Growing complexity in supply management

What is Collaborative Optimization

Example: Applying Collaborative Optimization in Road transportation

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External and internal trends have a huge impact on the growing complexity supply management professionals have to cope with

External trends

Internal trends

• Role of procurement shifted to proactive and strategic (away from tactical).

• Socially responsible procurement/sustainable sourcing

• Globalization

• Increased competition and price pressures

• Outsourcing

• Shortened and more complex product life cycles

• Transparency

• Governance

• Focused on the business needs

Impact on the

Sourcing process

• Data driven analytics

• Rationalization of all decisions

• Strongly increased communication with network

• Higher degree of customization

• Closer integration and collaboration with suppliers

• Concentration on value creation

• Metric-driven delivery / Performance optimization.

• Multi-tiered Sourcing

• Competition on supply and capacities

• Clear TCO calculations

• What are the "cost of decisions"?


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• Rigid structures for proposal collection

– Difficult to align requirements with

supplier capabilities

– Suppliers cannot always place best

possible offer

• Scenario generation: limited analytical

power

– Cumbersome award scenario

development (Excel/Access based)

– Labor and time intensive

• Competition focus: How much are you

paying?

– Savings driven by leverage and

supply base rationalization

Managing the growing complexity requires a new genera-tion of techniques for proposal collection and negotiation

Traditionaltechniques

• Structures enabling „Collaborative‟

proposal collection

– Enables suppliers to align capa-

bilities with requirements

– Encourages supplier innovation and

creativity

• „Collaborative‟ Scenario generation

and optimization

– Iterative, multi-stakeholder business

constraints modeling

– Combinatorial optimization („solver‟)

engines to accelerate analysis

• Collaboration focus: What and how

are you buying?

– Benefits of alternative approaches

are analyzed and quantified

„Next generation‟ techniques

Source: A.T. Kearney analysis

Co

st

Savin

gs

Reali

zati

on

Typically savings

deteriorate over

time

Potential

additional

savings with CO

Max savings

with traditional

techniques

Time

Costs of constraints are fixed / not known

Costs of constraints are explored


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Optimization & Expressive Bidding are connecting the eco-nomical "possible" of the suppliers with the "realizable" of the client

Collaborative proposal collection

Flexible proposal collection tools allow suppliers

• responding creatively to client requirements by suggesting alternative specifications or services,

• varying order quantities,

• offering conditional/volume discounts, or

• bundling their offerings into packages that highlight their unique differentiators

Optimization

Iterative scenario analyses, supported by combinatorial tools, for evaluating

and negotiating complex, inter-dependent proposals based on

• Defined Variables, conditions and interdependencies

• Flexibly modeled business constraints

• Creation and simultaneous rating of different award scenarios

• Defining of optimized awards per supplier

+


A.T. Kearney xx/mm.yyyy/00000 8

Even simple standard situations in procurement imply massive complexity

Standard case in procurement

Collaborative Optimization

Think how this standard situation could be solved using your currently available purchasing methodologies and tools.

5 Sites 5 Products5 Suppliers

5 different grades of steel

Source: A.T. Kearney

5 suppliers across different

countries

5 sitesacross different

countries


Restriction in allocation of business

When allocating business, complexity can be further driven by restrictions


1. Which restrictions could apply when allocating the spend ? Who could bring in these restriction?

2. Which allocation scenarios can you think of?





Unconstrained scenario - lowest cost but high effort to implement

Scenario 1: lowest cost allocation (“cherry picking”)




Average (weighted)

349.6

359.4

n/a

374.0

354.6

Scenario 2: only one supplier per site (“single sourcing”)

Easy to implement but at a comparably high price


Combinatorial Complexity in Sourcing

This simple scenario implies massive complexity which reveals when looking at the number of possible allocations


Even in this simple example there are 298,023,223,876,953,125 possible allocations!





Scenario 3: not more than three suppliers in total and not more than two per location

This may be a realistic and fair compromise, but how do you find the lowest cost for this allocation scenario?

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Motivation and creativity of suppliers has no limits … some proven logistics samples


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Collaborative proposal collection techniques help identify mutually attractive deals that deliver higher, more sustaina-ble savings

Technique Description Supplier Economies Benefits to Client

Bundling Suppliers offer discounts by grouping together items into combinations or bundles

Suppliers can indicate the economies of being awarded an entire set of items that helps to balance their utilization

• Drives towards a supplier allocation strategy that benefits both buyer and supplier

• Provides suppliers incentives to better align capabilities with client requirements

• Achieves greater sustainability and economic efficiency

Step Pricing Suppliers define their own price break grid and use it to provide quotes for items

Suppliers bid in more precise ways that map to their cost drivers e.g. item pricing de-fined through cost driver inputs

• Provides more precise, comprehensive and sustainable pricing

• Increased transparency allows awarding most competitive bids per sub-group

Conditional Bids

Suppliers make a series of alternative conditional offers, e.g. alternate material types, pricing for new capacity

Suppliers can invest in new capacity with multi-year commitment or offer to use alternate equipment

• Include supplier creativity to manage total costs• Understand costs and tradeoffs associated with

operational decisions• Competitive rates leveraged across a higher

proportion of the business• Cost transparency with respect to particular mate-

rial types ensures the correct item is procured

Price Plus Terms

Suppliers offer a dis-count associated with non-price incentives or conditions e.g. lead time variation

Suppliers can offer a variety of pricing if allowed to vary delivery lead time requirements

• Reduces hedging behavior and drives cost transparency

• Provides useful lever for negotiations

Suppliers match tender requirements with their capabilities and/or strategic objectives and, thus, are enabled and motivated to create and share efficiency savings


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Award constraints modeling is an algorithm based analy-ses approach that allows stakeholders to model business award „conditions‟

Business Constraints

… across entire business

Supplier & Market constraints

Government constraints

Customer constraints

Self-imposed constraints

… across organizational units

… across groups of suppliers

… for groups of items

… for specific suppliers or items

• Business constraints are to reflect real world constraints and, as such, ensure the feasibility of the solution.

• Constraints modeling will allow decision makers and stakeholders to quantify the costs and benefits of various business constraints.

expressed …


17A.T. Kearney X/mm.yyyy/00000

135

130

125

120

115

110

105

0

Scenario modeling allows to find the optimal balance between business constraints and the cost that they imply

Scenario modeling

Optimal

Award

Allocations

Supplier (collaborative) proposals and capacity

• Detailed cost breakdown• Technical alternatives• Conditional bids

• Price plus terms• Bundling• Step pricing

Business award constraints

Baseline db

… across entire business

Supplier & Market constraints

Government constraints

Customer constraints

Self-imposed constraints

… across organizational units

… across groups of suppliers

… for groups of items

… for specific suppliers or items

expressed …

Iterative

constraints

modeling

process

Optimi-

zation

Engine


Baseline

…

„Unconstrained‟: max savings, min implementability

Incumbents Only max implementability, min savings

Scenario A

Scenario B

“Most Desired Outcome”optimal balance between savings and

implementability

Spend (m GBP)

Client Example

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Summary

Collaborative Optimization ( CO) helps to:

Make the opportunity cost of your decisions / constraints transparent

Lever dormant supplier capabilities and efficiencies and gain additional savings / cost benefits

Take fact based decisions for the best combination of offer


Example 1

Applying Collaborative Optimization in Road Transportation

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Growing customer requirements and reduced flexibility on the supply markets increase the challenge to find the right strategic fit

Transportation cost in Europe increases by 3 – 6% per year due to:

• Increasing fuel cost

• Increasing labor cost

• Rising toll cost

• Increasing traffic density

• Shortage of available capacities

• Tighten European labor directives

Low profit margins (1-3%) do not allow pure price negotiations, but require a collaboration with the carriers to use

their capabilities efficiently

Transportation Sourcing is characterized by:

• Data mining & Baseline definition– Collect complete and accurate

transportation data– Allocate line haul cost and

surcharges– Define of lanes/ transportation flows– Definition of operational

requirements and constraints

• Tendering– Align lot structure with supplier

capabilities and objectives (asset utilization and/ or network leverage)

– Factor in carrier capacities and strategies

• Award analytics– Perform "what if" analysis– Factor supplier capacity constraints

into award decision

Complexity

Current Market Situation Transportation Sourcing

Complexity in transportation

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Prerequisite for the use of CO & Expressive Bidding is a clear and solid understanding of the carriers„ cost-benefit-model

Carriers„ Cost-

Benefit Drivers

• Fleet utilization

• Network efficiency

• Buying power

• Cost management

• Regional or service segment specialization

Expressive

Bidding

• Define service regions (matching their network)

• Define part-load break defaults

• Indicate capacity constraints at country, regional or lane level

• Indicate investment capabilities and willingness

• Expose accessorial charges (i.e. weekend driving)

• Offer price discounts for bundles of lanes or regions

• Offer volume discounts

• Indicate price discounts for adjusted/ revised requirements, i.e. longer lead times

Carriers are enabled and motivated to create and share efficiency savings

based on their capabilities

Example: Road Transportation

Optimisation & Expressive Bidding

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Optimization

• Sourcing Teams can define a wide array of constraints, i.e.:

– Forced usage or elimination of specific carriers

– Minimum and/or maximum number of carriers

– Minimum and/or maximum number of existing carriers

– Minimum and/or maximum volume of business awarded

• Constraints can be expressed for i.e.:

– The entire company

– Specific lanes or regions

– Organizational units (divisions, plants, brands)

– Groups of carriers (i.e. pre-approved for specific customers)

– Specific carriers on selected lanes or regions

Example: Road Transportation

Award Constraints Modeling in transportation is an algo-rithm based analyses approach that allows stakeholders to define/model business award „conditions‟

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€ 62,5

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27 32 36 37 38 39 41 45 49 51 52 53 54 57 62 67 72 74

Millio

ns

0%

10%

20%

30%

40%

50%

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100%

award_spend Lane Incumbent Origin Incumbent Incumbent

Award Logistic cost and Incumbent share by max # of suppliers(FTL & FCL Network)

Cherry Pick-

with capacity


Example: Finding the efficient supplier mix is key for implementable and sustainable savings in transportation

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_________________________________________________________________________________

Federico MariscottiDirector A.T. Kearney FZ LLC

Level 26, Media One TowerDubai Media CityPO Box 126299, DubaiUnited Arab Emirates Tel: +971 4 457 5055Mobile: +971 50 5529 335

Procurement Solutions [email protected]_______________________________________________________________________________

_________________________________________________________________________________

Frank ThewihsenVice President A.T. Kearney FZ LLC

Level 26, Media One TowerDubai Media CityPO Box 126299, DubaiUnited Arab Emirates Tel: +49-211-1377-2376 Mobile: +49 175 2659 376 (Germany)

+971 505577971 (UAE)Procurement Solutions [email protected]_______________________________________________________________________________

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Collaborative Optimization for complex categories · Collaborative Optimization for complex categories. ... not more than three suppliers in total and not more than two per location