Business

Cluster Manufacturing: A Supply Chain Perspective

• Cognizant 20-20 Insights

Executive Summary Supply chain management clusters are geographic concentrations of three or more companies directly involved in the upstream and downstream flows of products, services, finances and/or information from a source to a customer. Clusters extend downstream to channels and customers, as well as laterally to manufacturers of comple- mentary products. They also extend to companies in industries related by skills, technologies or common inputs.

Clusters exhibit some common characteristics: physical proximity; complementary core compe- tencies; activity base; collective growth potential; competitive position; and industrial organization and coordinating mechanisms.

Clusters offer many benefits to member compa- nies:

Resources are concentrated in an area, creat-1. ing the opportunity to streamline and shorten supply chains.

Geographic proximity greatly reduces supply 2. chain complexity.

Interdependence and mutual trust is height-3. ened between companies that are members of the same supply chain. Research by Noordew- ier1 and others establishes that relational

elements such as long-term orientation among cluster companies directly improves performance in buyer-seller relationships. Greater interdepen- dence also increases mutual trust, strengthens commitment levels and reduces conflict.

Productivity is increased, through faster access 4. to customers and suppliers, faster access to specialized information and better network support for supply chains.

It is easier to motivate and measure the per-5. formance of supply chain partners.

Visibility is greater, due to obvious communi-6. cation advantages.

Flexibility is increased when partners of a 7. supply chain exist in the same cluster.

Risk of failure is greatly reduced, due to focus 8. and alignment of efforts of all the partners in the supply chain.

Information is shared more quickly and effi-9. ciently. Players in a cluster are likely to gain knowledge of new business opportunities more quickly.

Gaps in products and services are identified 10. earlier.

Feedback loops are shorter, allowing for faster 11. modification of supply chains, which results in significant cost savings.

cognizant 20-20 insights | january 2011

Figure 1 offers a broad framework of the manner in which clusters benefit member companies by improving their supply chains.

Classification Existing clusters may be further classified into two types. They include value chain clusters and labor pool clusters.

Value Chain Clusters:• Value chains are essen- tially groups of businesses that buy and sell from each other (e.g., hospitals and pharmacies, auto manufacturers and parts suppliers, etc.). Although the benefits of value chain clusters are based primarily on the proximity of companies and not direct cooperation, proximity itself allows for simplified, low-cost supply chains and can be instrumental in implementing JIT (just in time) systems. Value chain clusters can be divided into the following subtypes:

> Marshallian District: This type is based mainly on studies of European clusters and consists of groups of SMEs (small and medium enterprises) cooperating with each other to

achieve economies of scale for supply chains and infrastructure. Silicon Valley is a prime example of an urban Marshallian District.

> Hub and Spoke: Groups of large companies surrounded by and interacting with a num- ber of smaller outfits.

> Satellites: Simple groupings of large, branch-plant-type entities form satellite clusters. These clusters are mainly found in rural areas.

> State-Anchored: These are large public or non-profit entities and related outfits such as supply and service firms. They depend on political support for their needs (e.g., SEEPZ, Mumbai, Cyberabad /Hi-Tech City, Hyderabad).

Figure 2 offers a summary of value chain clusters.

Risks associated with value chain clusters include the following:

Companies can become overly dependent > on SMEs for raw materials. If SMEs default,

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Cluster Advantages

Cluster Characteristics Improved Supply Chain

Physical Proximity

Core Competencies

Relationships

Integrated Behavior and Processes

Sharing of Information and Risks

Maintain Long Term Relationships

• Identical Goal

• Cooperation

• Aligned Focus

Figure 2

Cluster Type Characteristics of Member Firms Intra-cluster

Interdependencies Prospects for Employment

Marsallian Small and medium-sized lo- cally owned firms

Substantial inter-company trade and collaboration; strong institutional support

Dependent on synergies and economies provided by cluster

Hub and Spoke One or several large firms with numerous smaller suppliers and service firms

Cooperation between large (hub) compa- nies and their smaller suppliers (spokes), using the terms of the hubs

Dependent on growth prospects of large (hub) companies

Satellite Platforms Medium- and large-sized branch plants

Minimum inter-company trade and networking

Dependent on ability to recruit and retain branch plants

State-Anchored Large public or non-profit entity and related supplying and service firms

Restricted to purchase-sale relationships between public entity and suppliers

Dependent on region’s ability to expand political support for public facility

Value Chain Clusters at a Glance

Figure 1

the primary cluster player carries a greater risk of brand dilution.

Sometimes, one SME is responsible for one > type of raw material. This can lead to a mo- nopolistic setting, wherein the SME can apply supplier power over the parent company.

Lack of competition can lead to inefficien- > cies on the part of the SMEs.

It is very easy for SMEs to function as closed > units and ignore global developments.

Labor Pool Clusters:• These clusters are based on occupational categories. Companies with similar types of occupations and worker skills can draw from a larger pool of potential employees. A major advantage of this type of cluster is that it helps reduce employee search costs. For example, software manufacturers and more traditional manufacturers may both require similar skills, such as in the case of software developers. The close proximity of these companies attracts these occupations to the area of multiple job opportunities. This type of cluster helps increase the productivity of all workers in that region. Some examples of labor pool clusters are distribution, tourism, technological parks, industrial parks, airline transport, etc.

Some of the risks associated with labor pool clusters are:

Since the companies are eyeing a similar > labor pool, an excessively competitive em- ployment environment may ensue.

Since the labor demand is very high, labor > may be able to yield its power over em- ployers.

The labor wage rate might be high in these > clusters, again because the demand is high. This might lead to a major discrepancy in wage rates of labor across regions.

In general terms, regardless of the type of cluster to which a company belongs, cluster manufacturing may expose the member companies to the following significant risks:

Cluster members may fail to establish close > rapport with others belonging to the same supply chain and with customers, which may defeat the purpose of the cluster.

Cluster members may strive to maximize > return on investment individually at every stage.

Over-reliance on current best practices > within the cluster may strangle innovation.

Long-term relationships may cause part- > ners to be excessively dependent on each other.

If cluster members fail to redefine their own > goals and objectives to suit the cluster en- vironment, the supply chain within the clus- ter may not function smoothly.

Cluster members may become complacent > and ignore the importance of the constant development of resources within the cluster.

It is imperative for cluster members to ob- > tain regular updates on developments in relevant industries outside their cluster.

The Impact of Cluster on Risk To better understand the impact of a cluster on the risk profiles of SME and parent manufactur- ers, we have considered an example of an “auto

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Impact of cluster on reducing risk of supply chain

Summary of Findings of Risk Model

Figure 3

Risk Profiling of SME (Example: Avtec)

Engine Parts Transmission Steering Body Suspension Braking Electrical Accessories Average RPN

Risk Priority Number (RPN) without Cluster

162 288 36 18 128 72 288 18 126

Risk Priority Number (RPN) with Cluster 6 32 48 324 96 16 36 81 80

Risk Profiling of Parent Manufacturers (Example: Bajaj Auto)

Engine Parts Transmission Steering Body Suspension Braking Electrical Accessories Average RPN

Risk Priority Number (RPN) without Cluster 1458 972 64 2 32 648 12 24 402

Risk Priority Number (RPN) with Cluster 324 162 64 4 16 72 8 8 82

cognizant 20-20 insights 4

cluster.” What follows is a summary of the risk model, with details and methodology provided in Appendix A. The supply chain risk model for all the cases can be seen in Figure 3.

If we look at the average risk priority number (RPN) in the table in Figure 3, we can infer that the cluster offers maximum risk reduction to

the parent auto manufacturer when compared with the SME supplier. After considering all the risk-related factors and the change in RPN, we conclude that the concept of clustered manufacturing will be primarily driven by the parent auto manu- facturer, because the benefit of clustering to this company in the form of risk reduction is higher than the benefit derived by the SME. (Details of risk factors, RPN, calculation and case-spe- cific analysis are included in Appendix A.)

Through the qualitative and quantitative (model) analysis seen in Figure 4, we have concluded that Porter’s Cluster

Model2 truly benefits supply chain management within the clustered companies. However, a cluster needs to continuously develop strategies to mitigate the various risks inherent in the formation of a cluster that puts a premium on low-value suppliers, selecting quality suppliers, retaining talent and sharing knowledge.

Appendix A A typical composition of this cluster type is a “parent” (i.e., car or bike manufacturer) and various component suppliers located in the cluster. (For example, Bajaj Auto Vendor Cluster at Pantnagar.)

When Bajaj Auto formed a cluster of suppliers for key OEM parts in its new plant in Pantnagar, its vendors were divided on the basis of 16 technology skills. Only 16 vendors (one for each technology set) were selected from its pool of 800 suppliers. Since the suppliers were single sources for their respective products, each one had to make a large investment in setting up plants with sufficient capacities. Consequently, Bajaj had to directly invest only 1.5 billion rupees (or $329,000) in the facility, enabling it to have a very quick payback period.

Key components sourced from the cluster include lighting systems from Lumax, electrical components from Varroc, ignition systems from Minda and speedometers from Pricol.

The risks for an auto cluster can be specifically viewed from two perspectives: The SME manufac- turer’s and the parent manufacturer’s.

The supply chain risk profile of both would be different, and the impact/need for a cluster to both parties would be totally different from the point of view of supply chain risk.

In general, we classify any auto ancillary supplier in the cluster into eight broad categories based

Supply Chain Risk Summary With and Without Clusters

0

375

750

1125

1500

Engine Parts Transmission

Steering Body

Suspension Braking

Electrical Accessories

R isk Priority N

um ber

Figure 4

We conclude that the concept

of clustered manufacturing will be primarily driven by the parent auto

manufacturer, because the benefit

of clustering to this company in the form of risk

reduction is higher than the benefit

derived by the SME.

n SME Manufacturer With Cluster

n SME Manufacturer Without Cluster

n Parent Manufacturer With Cluster

n Parent Manufacturer Without Cluster

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on type of product, technology, value, etc.: Engine Parts, Transmission, Steering, Body, Suspension, Braking, Electrical and Accessories. Each group has varying supply chain risks and different incentives to participate in the cluster. This is different for an SME and parent manufacturer.

Developing a Risk Profile Model Methodology The supply chain risks were quantified using a concept generally used in the Failure Mode Effect Analysis of any manufacturing process. Every risk was categorized into three levels, and a risk score was assigned to each level. Also, these factors need to be carefully considered for both SME and parent.

Risk Factors for the SME1.

Criticality of the part: If the part being supplied by the SME is very critical to the parent manufacturer, then the risk for the SME going out of business is low. But if the supplier makes a substitutable product, then it has a high risk of being replaced by another company. Risk levels and score are:

Complexity of the part:• If the part supplied by the SME is complex to manufacture, then the risk for the SME going out of business is low. But if the supplier makes a simple product like a nut or bolt, then it has a high risk of being replaced by another company. So the risk levels and score are:

Size of the SME:• We can generally classify the auto SME into three categories of >5 billion rupees, 2 to 5 billion rupees, <2 billion rupees. The bigger companies are in a better bargaining position than the smaller ones, and so the risk profile changes.

Selling price to raw material ratio (value • addition): In an auto cluster, SMEs span the value chain — from simple sheet metal job workers, to complex engine and transmission manufacturers that require knowledge and design skills. We have captured the technical skills aspect in the “value addition” risk factor. If the selling price to raw material ratio is very low (1.1 to 1.25), then there would be huge competition and risk of substitution. Contrast this with an engine manufacturer, with whom the parent auto company shares all its design drawing and details. The SME with higher value addition will have less risk, because this company would develop its skill and knowledge in the process of supplying the parent.

Incentive structure:• The parent auto company may offer incentives to the SME or ancillary manufacturer in many ways, ranging from entering into a technical memorandum of understanding with promised quantity, to assigning a recurring purchase order. The risk profile changes according to the incentive:

Diversification opportunities:• If the SME is manufacturing a product with greater after- market demand (i.e., retail demand), then its dependence on the auto manufacturer is not as significant. But if the SME is highly customized and localized to the cluster, then it is in a highly risky position.

Packaging required:• Packaging plays a key role in the safety and quality of the product supplied by the SME. Huge investments in packaging and reverse logistics are performed

Score Level

1 High Criticality

2 Medium Criticality

3 Low Criticality

Score Level

1 High Complexity

2 Medium Complexity

3 Low Complexity

Score Level

1 > 5 billion rupees

2 2-5 billion rupees

3 < 2 billion rupees

Score Level

1 >2

2 1.25 to 2

3 1.1 to 1.25

Score Level

1 MOU/Technical tie-up

2 Annual rate contract

3 Recurring purchase order

Score Level

1 Strong after-market (retail) demand

2 Multiple B2B customer or multiple vertical

3 Customized / Localized

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by the SME on behalf of the parent manufac- turer. Hence, if there is high expectation for customized packaging with reverse logistics, it would have a different risk profile, compared with general/bulk packaging.

Risk Factors for the Parent Manufacturer2.

Criticality of the part:• From the parent manu- facturer’s perspective, the higher the critical- ity of the part to the end product, the greater the risk. So here, the level of risk would be the opposite of the SME manufacturer’s. Risk levels and score are:

Complexity of the part:• Extending the above argument to complexity, we can infer that the higher the complexity of the part, the higher the risk to the parent manufacturer.

Transit time to supply:• Transit time becomes another critical factor for the manufacturer. More days in transit means higher risk.

Dependence of assembly line on the part:• If the part being sourced is highly entrenched in the operations of the assembly line, and a

slight problem in part availability or another problem can cause the assembly line to come to a halt, then this needs to be classified as a

high risk. Three methods are generally adopted by manufacturers: JIT supply, milk run and push system with inventory.

Contract structure:• If the parent company enters into a technical memorandum of understanding with promised quantity, then it is in a locked condition, which adds risk to existing high supply chain challenges. But with recurring POs, the parent company can change the supplier if there is any quality or supply issue.

Multiple sources of supply:• As explained by Porter’s five forces,3 additional supply options lower a company’s risk.

Packaging required:• Packaging plays a key role in the safety and quality of product supplied by the SME. Customized packaging with reverse logistics would carry higher supply risk compared with general/bulk packaging.

Quantifying Risk: Risk Priority Number 3. (RPN) With and Without Cluster

We then take four cases and calculate the RPN for each case to quantify the risk. Higher RPN equals higher risk.

Case 1: SME perspective if it is operating without a cluster.

Score Level

1 General / bulk packaging (CFCs)

2 Standard accessories packaging

3 Customized packaging with reverse logistics

Score Level

1 Low Criticality

2 Medium Criticality

3 High Criticality

Score Level

1 Low Complexity

2 Medium Complexity

3 High Complexity

Score Level

1 < 2 days

2 2-10 days

3 >10 days

Score Level

1 JIT

2 Kanban, milk-run, etc.

3 Push system

Score Level

1 Recurring purchase order

2 Annual rate contract

3 MOU/Technical tie-up

Score Level

1 Many options

2 Few options

3 Localized options

Score Level

1 General / bulk packaging (CFCs)

2 Standard accessories packaging

3 Customized packaging with reverse logistics

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Case 1 Supply Chain Risk Profiling for SME Manufacturer: Without Cluster

7

Case 2: SME perspective if it is operating within a cluster.

Case 3: Parent manufacturer perspective if it is operating without a cluster.

Case 4: Parent manufacturer perspective if it is operating within a cluster.

These cases were created to quantify the benefit of a clustering strategy in reducing supply chain risk. While calculating the RPN, the following formula was used:

RPN = Product of risk score of all auto part groups in the cluster

We repeat with the same set of assumptions for all the cases and understand the impact of clustering to both the stakeholders in the cluster. If the RPN decreases when there is a transition from outside a cluster to within a cluster, then it is evident that clustering actually benefits the stakeholder by reducing its risk. However, as seen in the following section, clustering may not be the best option for all the supply groups.

To start with, let’s compare the SME perspective on supply chain risk.

Engine Parts

Transmission Steering Body Suspension Braking Electrical Accessories

Examples Avtec Bosch

Lucas TVS

Avtec Carraro gears

Delphi Rane Sona Koyo

Sheet metal works

Gabriel Autoshox

Bosch Sundaram

brake Rane brake

linings

_

Pricol Lumax NTN,

Goodyear

Criticality of product supplied 1 1 2 3 2 1 2 3

Complexity of part to manufacture 1 2 3 3 2 2 3 3

Size of supplier 1 2 1 3 3 1 1 3

Selling price to raw material ratio (value addition)

1 2 2 3 2 2 1 1

Incentive structure of supplier 1 1 2 2 1 1 3 3

Diversification opportunities 2 2 1 2 2 2 1 1

Packaging required 3 2 2 1 2 2 2 1

Risk Priority Number 6 32 48 324 96 16 36 81

Case 2 Supply Chain Risk Profiling for SME Manufacturer: With Cluster

Engine Parts

Transmission Steering Body Suspension Braking Electrical Accessories

Examples Avtec Bosch

Lucas TVS

Avtec Carraro gears

Delphi Rane Sona Koyo

Sheet metal works

Gabriel Autoshox

Bosch Sundaram

brake Rane brake

linings

_

Pricol Lumax NTN,

Goodyear

Criticality of product supplied 3 2 1 1 2 3 2 1

Complexity of part to manufacture 3 2 1 1 2 1 2 1

Size of supplier 1 3 3 3 2 3 3 3

Selling price to raw material ratio (value addition)

3 2 2 1 2 2 3 2

Incentive structure of supplier 1 2 3 3 2 1 2 3

Diversification opportunities 2 2 2 2 2 2 2 1

Packaging required 3 3 1 1 2 2 2 1

Risk Priority Number 162 288 36 18 128 72 288 18

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The SME perspective: It is clear that the RPN for Engine Parts, Transmission, Suspension, Braking, Electrical and Accessories is reduced, which bodes well given that an SME would be safer in a cluster if its manufacturing-critical components have higher value addition and skill requirement. In contrast, the risk of body parts suppliers or sheet metal workers rises drastically if they get into a cluster because they manufacture undifferenti-

ated products with low skill requirements. They can be easily replaced by the competition. Hence, the parent manufacturer must be mindful of this and offer incentives to contract workers or sheet metal fabricators with quantity assurance and rate agreements to retain them in the cluster.

From here, we will compare the parent manufac- turing perspective on supply chain risk.

Engine Parts

Transmission Steering Body Suspension Braking Electrical Accessories

Examples Avtec Bosch

Lucas TVS

Avtec Carraro gears

Delphi Rane Sona Koyo

Sheet metal works

Gabriel Autoshox

Bosch Sundaram

brake Rane brake

linings

_

Pricol Lumax NTN,

Goodyear

Criticality of product being sourced 3 3 2 1 2 3 2 1

Complexity of part being sourced 3 3 2 1 1 2 1 1

Transit time to supply 3 3 2 1 2 3 1 2

Dependence of assembly line on the product

3 3 2 1 2 3 3 3

Contract structure 3 2 2 2 2 2 1 2

Multiple sources of supply 2 3 1 1 2 2 1 1

Packaging required 3 2 2 1 1 3 2 2

Risk Priority Number 1458 972 64 2 32 648 12 24

Case 3 Supply Chain Risk Profiling for Parent Manufacturer: Without Cluster

Engine Parts

Transmission Steering Body Suspension Braking Electrical Accessories

Examples Avtec Bosch

Lucas TVS

Avtec Carraro gears

Delphi Rane Sona Koyo

Sheet metal works

Gabriel Autoshox

Bosch Sundaram

brake Rane brake

linings

_

Pricol Lumax NTN,

Goodyear

Criticality of product 3 3 2 1 2 3 2 1

Complexity of part being sourced 3 3 2 1 1 2 1 1

Transit time to supply 1 1 1 1 1 1 2 2

Dependence of assembly line on the product

2 1 2 2 1 1 1 1

Contract structure 3 2 2 1 2 2 2 1

Multiple sources of supply 3 3 2 2 2 3 1 2

Packaging required 2 3 2 1 2 2 1 2

Risk Priority Number 324 162 64 4 16 72 8 8

Case 4 Supply Chain Risk Profiling for Parent Manufacturer: With Cluster

About Cognizant

Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process out- sourcing services. Cognizant’s single-minded passion is to dedicate our global technology and innovation know-how, our industry expertise and worldwide resources to working together with clients to make their businesses stronger. With over 50 global delivery centers and more than 100,000 employees as of as of December 1, 2010, we combine a unique global delivery model infused with a distinct culture of customer satisfaction. A member of the NASDAQ-100 In- dex and S&P 500 Index, Cognizant is a Forbes Global 2000 company and a member of the Fortune 1000 and is ranked among the top information technology companies in BusinessWeek’s Hot Growth and Top 50 Performers listings.

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© Copyright 2011, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein is subject to change without notice. All other trademarks mentioned herein are the property of their respective owners.

About the Author Nandan Kumar is a Consultant in Cognizant’s Manufacturing and Logistics Practice. He has more than six years experience in various aspects of the heavy manufacturing industry. He has an MBA from Indian School of Business, Hyderabad, and can be contacted at [email protected].

References Performance Outcomes of Purchasing Arrangements in Industrial Buyer-Vendor Relationships,” The Journal of Marketing, Vol. 54, October 1990, pp. 80-93. http://www.jstor.org/stable/1251761

David Barkley and Mark Henry, “Rural Industrial Development: To Cluster or Not to Cluster,” Review of Agricultural Economics, Vol. 19, No. 2 (Fall/Winter 1997), pp. 311-2, 322-1.

Tom DeWitt, Larry C. Giunipero and Horace L. Melton: “Clusters & Supply Chain Management: The Amish Experience,” International Journal of Physical Distribution & Logistics Management, Vol. 36, No. 4, 2006.

“Bajaj Auto Commissions New Plant at Pantnagar, Uttarakhand,” press release, April 10 2007, http://www.domain-b.com/companies/companies_b/bajaj_auto/20070410_commissions.html

Footnotes 1 Thomas G. Noordewier, George John and John R. Nevin, “Performance Outcomes of Purchasing Arrange-

ments in Industrial Buyer-Vendor Relationships,” The Journal of Marketing, Vol. 54, Nov 4, October 1990, pp. 80-93. http://www.jstor.org/stable/1251761

2 Porter’s Cluster Model was first introduced by Michael Porter in The Competitive Advantage of Nations, Free Press, 1998.

3 Porter’s Five Forces is a framework developed by Michael Porter (Harvard Business School) to study the forces that help understand competitive intensity and attractiveness of market. The five forces are: The threat of new competitors/barriers to entry; the intensity of competition; the threat of substitutes; the bargaining power of customers; the bargaining power of suppliers.

The parent manufacturer perspective: RPN for Engine Parts, Transmission, Suspension, Braking, Electrical and Accessories is reduced, which bodes well given that the parent manufacturer would be safer to have its critical components suppliers inside

the cluster. From the manufacturer’s perspective, body parts makers would have the least impact on the parent’s risk profile, but in practice, they are the most important part of their supply chain.