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CASE STUDY: ECONOMIC VIABILITY OF COMPONENTMANAGEMENT FOR A NEW DESIGNSAMEER KUMAR a & CHARU CHANDRA ba University of St. Thomasb University of Michigan , DearbornPublished online: 31 May 2007.

To cite this article: SAMEER KUMAR & CHARU CHANDRA (2001) CASE STUDY: ECONOMIC VIABILITY OF COMPONENTMANAGEMENT FOR A NEW DESIGN, The Engineering Economist: A Journal Devoted to the Problems of Capital Investment,46:3, 205-219, DOI: 10.1080/00137910108967573

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CASE STUDY: ECONOMIC VIABILITY OF COMPONENT MANAGEMENT FOR A

NEW DESIGN

S AMEER KUMAR University of St. Thomas

CHARU CHANDRA University of Michigan, Dearborn

This paper explores the viability of standardization of design and manufacturing techniques to expedite product development and control design proliferation using an example of a leading transport refrigeration unit manufacturer. As an incremental approach to implementing standardization in a product development environment, a conceptual framework for component management decision support system is presented to build a case for its technical feasibility. The primary objective of this research case study is to provide an economic justification for implementing the proposed system. A three level decision making hierarchy is proposed with economic optimization for levels I and 2 representing standardization of system modules and capacity decisions for a prcduct line respectively and thermodynamic optimization for level 3 representing control systems to keep the system dynamically balanced in changing environments. Other potential applications amenable to classification are identified.

A number of companies today, typically have multiple autonomous divisions or

subsidiaries engaged in designing, manufacturing and selling products to customers. Some of them have undergone significant mergers and acquisitions, further compounding organizational complexity. Invariably, a proliferation of

suppliers, parts, components and materials occurs that is impossible to control without enterprise-wide decision support processes and standardized content on

parts, components, materials and suppliers. Product designers are under pressure

to get it right the first time and do it fast [1,4,14]. Decisions they make are critical to product success and the company's bottom line. During the first 10% of the product development cycle, decisions made affect up to 80% of the final

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product cost, and also have repercussions dowristream in manufacturing and service for years [6].

Companies would significantly benefit from the implementation of decision

support systems to streamline inbound supply and support collaboration among even the most independent divisions and subsidiaries. Such systems would facilitate enterprise-wide collaboration in design, procurement and operations decisions, and sharing of standardized component and supplier content. These would also enable a firm to: reduce the cost of production parls and supplies by lcvernging purchases on fewer parts from a smaller supplier basc, accelerate product innovation and time-to-market by increasing component and design re- use across the enterprise, and increase product quality by increasing usage of "prefcrred" components and suppliers.

The study reported in this paper explores the economic viability of implementing a component management system to support the proposed formalized standardization of three level decision making hierarchy for

expcditing strategic product development and control design proliferation using an exa~nple of a well known transport unit manufacturer.

A leading transport reliigcration company is expanding in new competitive intcrnntional markets. It is realized by the management of the company that its success critically depends upon - improving the design process; reducing design turnaround time; improving product quality and field services; and reducing overall cost of manufacturing and service. All of these objectives are achievable through standardization of design and manufacturing techniques. It is important that new products are designed and integrated coherently. Manufacturing is optimally distributed on a global basis. Main sub-systems of the company's product, a transport refrigeration unit, include: Refrigerant, Compressor, Evaporator, Condenser, Throttling Device, Motive Power, Lube-oil system, Piping, Seals. Joints, Couplings, Control Systems, Insulation, and Structure.

To have an idea of lack of standardization in the company, the degree of proliferation of parts in recent years is a reasonable indicator. During the recent three-year period, number of part master records grew from 191,000 to 236,000, number of product structure records grew from 865,000 to 1,068,000, and active manufacturing part numbers at the end of this three-year period were 79,000.

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Analysis of this company's operations suggests that product development decisions based on following a three-level hierarchy would be effective in responding to needs of the marketplace:

1 ) Standardization of modules for various -sub-systems. Such decisions should be taken on a global market basis.

2) Capacity decisions for a particular product line for a specific market. 3) Control systems for keeping a transport refrigeration system

dynamically balanced in changing environments. The basis for decisions at levels 1 and 2 should be economic optimization.

Thermodynamic optimization consideration should dominate at level 3. Systems are usually designed for worst environmental conditions.

Regulated controls are aimed at making units function in equilibrium in changing environments. These controls will usually imply a reduction of capacity, which is achieved either through subsystems function on "on-off

basis," or through "step down modulation." Techniques for reducing capacity and their energy implications play an important role in product design.

From our knowledge about the company, it appears that a formalized level 1 does not exist. Introducing level 1 can be an important step towards higher productivity and efficiency on a global basis.

We need to have best practice functionality for three groups of users - Design engineers, Operations users, and Management within the enterprise:

Design engineers to be able to quickly locate and select the optimal component for a new design from a central repository of existing parts, assemblies, and designs, enriched with technical and business attributes. Operations users in procurement, component engineering. and manufacturing facilitate and dynamically manage information to guide design engineers to make the right choices up front in the product development process.

Management can easily set periodic goals and measure success on enterprise-wide strategic product development initiatives.

ECONOMIC VERSUS THERMODYNAMIC OPTIMUM

A system is thermodynamically optimum if there are no exergy losses. According to the second law of thermodynamics, energy in one form is not

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completely convertible into other forms. Energy may be looked up as consisting of two parts: Exergy, is the energy in convertible forms; and Anergy, is the energy in non-convertible forms.

The first law of thermodynamics states sum of exergy and anergy is constant, while the second law of thermodynamics states sum of exergy is not

constant. An economically optimum design of thermodynamic systems in real life will have exergy losses because such a system will not necessarily be thermodynamically optimum. Criteria of optimization should be economic, and not thermodynamic. Economic optimization considers product and components' life cycles. so that company's capital investments and user's operational and maintenance costs are considered in design decisions. This leads to minimization of an objective function, which is the sum of amortized capital cosl and annual operating and maintenance costs per unit.

GENERAL MATHEMATICAL STATEMENT OFTHE DESIGN PROBLEM

Variables affecting the design problem may be classified into response and

indcpendent, the lattcr being divided into two subcategories - exogenous and design variables. Response variables define the usual multi-dimensional objective function such as, quality, performance, etc. Exogenous variables are those, which cannot be controlled, for instance operating condition, market or noise, whereas design variables are controllable. Design implies deciding on their nominal values and tolerances. A robust design implies picking design variables such that the system (in this case study, a transport refrigeration unit) is stable with respect to exogenous variables.

A-PRIORI PLANNED STANDARDIZED MODULES Exogenous variables representing operating and market conditions vary over time and space from one market to another. It will be meaningless to design a customized transport refrigeration unit for each point within the exogenous market (EM) variables space. Let us divide the system into proper subsystems. Consider subsystem j , Si. For each subsystem j , the EM variables space should be partitioned into sub-regions. As an example, for subsystem j within EMj. a standardized module 4 denoted by Sj2 would be the optimal standardized module. A total system should be synthesized out of these standardized modules. Some of the standardized modules already exist within our reservoir of designs. A few others would be added over time, as new markets are acquired. Some existing designs would be non-optimaI, which should be deleted.

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MAIN DIMENSIONS OFTHE EM VARIABLES SPACE The principal dimensions of the EM variables space include the following:

Goods to be transported giving desired temperatures required (TD). Environmental variables -Year-round ambient temperatures and humidity conditions etc.,

influencing selection of condensation temperature (Tc). Transport conditions -Road conditions, trailer sizes, loading and unloading times and

facilities, operating hours per year giving size of the unit and needed structural strengths and insulation needs.

Motive Power Source - during transport and non-transport modes. Information on costs, amortization factors, general labor rates, etc.

A CONCEITUAL FRAMEWORK FOR DRAWINGS RETRIEVAL SYSTEM

Entities responsible for developing the Drawings Retrieval System are, Product Design Engineers, as users of the retrieval system. Information Technology Engineers, as basic designers of the retrieval system in colIaboration with Product Design Engineers. Drawings Librarian, to meet Product Design Engineers' requests, once the system is operational.

The proposed engineering drawings retrieval system is primarily aimed at prolnoting standardization on an a-prinri basis. The first question to ask is, "How will the proposed system work'?" The Product Design Engineer will send a request to the Drawings Librarian. The Drawings Librarian will try to satisfy

'

the Product Design Engineer's request by providing a set of engineering drawings, similar to the subsystem the Product Design Engineer is planning to design. The next question is, "How does our environment differ from a general librarian meeting a request?'Our domain is much smaller and very well defined. An efficient retrieval system can be designed, provided we can develop a common language f& more precisely stating the Product Design Engineer's request, and the coding of drawings for retrieval by the Librarian [7,9,12,16]. The last question is, "Where is this common language going to come from?We need to understand the design process. It is sequential involving conceiving, planning, designing, prototyping, testing, evaluating, and implementing.

Three important steps in the design process are, 1. Defining relevant operating conditions and market variables for new

products. 2. Calculating macro-engineering variables.

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3. Transitioning from a macro design to a detailed product design. An optimization model (or a set of models) ideally should guide in

executing steps 2 and 3 above. Information Technology Engineers should obtain defining attributes for the coding of drawings for easy retrieval from the optimization model.

It is important to control unnecessary design proliferation of components as new products are designed. This requires that the Product Design Engineer be

provided with a list of existing parts similar to the new part that is planned for design. New part should be designed, only i f the Product Design Engineer ccrtiiies that none of thc existing parts i n the similar part class is a good substitute for the new proposed design. If the decision is taken to add a new part

that obsoletes one or more of existing parts, then it should be recorded in the database. Gradually, dominated par@) should be taken out of usage. A program or procedure wilI be required to reduce existing number of current parts and eliminating duplication of similar parts. The success of proposed design support function for variety control would depend upon the ability to group parts and retrieve thcsc groups easily on the basis of their design similarities [3,10].

Component management decision support software, such as "eDcsign" (liom Aspect Devclopmcnt, Inc., California,) enables manufacturers in strategic product development through the use of preferred parts, designs and suppliers.

MODEL FOR CLASSIFYING OBJECTS

There are two approaches for classification of objects:

I ) Classification of objects directly by Experts on the basis of their similarity judgments.

2) Development of classification capabilities based on weighting of relevant attributes.

Following steps are involved in planning for classification: a) Identify types of objects in the system that need to be classified. b) Identify purposes, and define similar class for each purpose. c) Identify attributes for objects and define their measurements.

d) Develop procedures for generation of similar classes for various purposes.

It is important that planning for the development of classification schemes

should be for the entire company's needs rather than using a piecemeal approach, taking one business function at a time [2,5,8,11].

Approach 2 is subdivided into two types of classification models: (i) Principal component analysis based classification.

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Salient features of this technique include: Detining new coordinates, whereby a fewer number of these can

explain a sizeable fraction of variance in a given data. Representing each object geometrically by a point in an n- dimensional space. The principal component analysis technique involves the rotation of coordinate axes to a new set of orthogonal coordinate axcs, wherein each of the "n" original attributes is described in terms of the "n" new principal components. These

components are uncorrelated. Basis for classification of objects will be fewer components, instead of original "n" attributes, if these account for a large percentage of the total variance.

(ii) Clusteri~y ulgor-ithnu based classificution. There are two types of clustering algorithms-partitioning and hierarchical.

A test sample of all fans of various types i n the company was selected for grouping. Design based attributes for fans used in the classification are number of blades, diameter of fan to the nearest inch, blade tip angle in degrees, direction of rotation, hub type and material. Principal component analysis revealed that attributes such as material, hub-type, rotation, and pitch angle in

that order will account for 85% of total variance of all attributes. Grouping based on these four attributes resulted in 20 groups.

Next, KMEANS clustering algorithm was applied to the output of principal component analysis (which was hierarchical grouping of objects) [5,15]. KMEANS clustering algorithm minimizes intra distance within groups and inter distance between groups.

The Design Engineer is contemplating to design a new fan. Feature vector for this fan is given as:

A set of similar fans within the existing set, which might be a suitable

substitute for the contemplated fan is shown in TABLE I . The Product Design

Number of

Blades -- 4

Diameter in

inches

1 2" 20" 1

Hub Type

-- 2

Material

I

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Engineer uses expert judgment in making choice of a suitable substitute fan from the set of similar fans shown in TABLE 1,

TABLE 1: Set of fans similar to the contemplated fan

There are issues with appropriateness of the sets presented to the Product Design Engineer. Concern can be raised if sizes of the set are too large. This is because giving larger sets for review will require more work for design engineers, whereas retrieving smaller sets will lead to a relaxed variety control. This prototype drawings retrieval system was developed using SAS statistical .

software to demonstrate the usefulness of component management information retrieval system for new product design.

Number of

Blades

Given Fan

4

Sitnilar Fans

4

4

4

4

4

4

4

4

4

4

Diarneter

in inches

12"

12"

14"

14"

14"

14"

1 6"

16"

16"

16"

22"

Pitch

Angle

20"

1 9'

1 6"

17"

18"

2 l o

21"

21"

2 2"

22"

27'

Rotation

Direction

I

I

I

I

1

I

1

I

I

I

I

Legend:

I Clockwisr:

2 Counter

clockwise

Hub Type

2

3

2

3

2

3

3

3

2

2

3

I Split block

2 Keyed

3 Set Screw

4 Pinned

5 Belt Drive

Material

I

I

I

1

I

1

1

I

I

1

I

1 Steel

2 Aluminum

3 Plastic

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BENEFIT COST ANALYSIS OF DRAWINGS RETRIEVAL SYSTEM

The following section provides benefit cost analysis using data for the company's manufacturing operation to acquire and implement the proposed "eDesignW component management software.

DESIGN COST SAVINGS New designs per year = 2,000 (@ $3,000 per new design. This includes cost of

product design engineer and draftsman's time, computer time, and computer storage time.)

Design savings per year for three, "reduction in part proliferation due to standardization" scenarios:

@ 10% = (0.10)(2,000)($3,000) = $ 600,000 @ 20% = (0.20)(2,000)($3,000) = $1,200,000 @ 30% = (0.30)(2,000)($3,000) = $1,800,000

WARRANTY COST SAVINGS It is assumed that there is a learning factor in product design. A design matures over three years. During first three years, there is a 20% higher warranty cost. Average life of a design is ten years. The design-engineering group validated these assumptions. Warranty cost savings per year = (L- L') x Warranty cost per design per year average over ten years;

where L is total number of new designs per year, L' is reduced number of new designs per year as a result of standardization project, m is number of years at a higher warranty rate, M is total life of a design.

Current average warranty cost per year = $12,000,000, rn = 3 years, and M = 10 years.

Current number of totaI designs, N, in the system = 50,000. Number of stable designs =30,000. We assume that external failure rate is due to 20,000 designs. Number of designs at higher failure rate

= (mlM)(Total Designs) = (3/10)(20,000) = 6,000 Number of designs at mature failure rate = (Number of designs at external

failure rate) - (Number of designs at higher failure rate) = 20,000 - 6,000 =14,000.

Thus. 6,000 designs are less than 3 years old incurring external failures at 1.2 times the maturity faiIure rate; and 14,000 designs are older than 3 years.

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Let x be the warranty cost per design per year at maturity failure rate. 6,000 (1.2) x + 14,000 x = $ 12,000,000 x = $566

Average warranty cost per design per year (averaged over 10 years) = 1.2 x (111 I M) + .r [I - (nr 1 M)] = $600

Annual savings per year due to fewer new designs introduced: @ 10% = (0.10)(2,000) ($600) = $120,000 '320% = (0.20)(2,000) ($600) = $240,000

@30% = (0.30)(2,000) ($600) = $360,000 Annual savings per year due to clearing of the backlog for an anticipated additional standardization of 15% of the 20,000 designs = (3,000) ($566) = $1.7

million .

ORDERING COST SAVINGS In this study, the standard EOQ N-part inventory model is used for estimating average ordering cost and avcrage cycle inventory cost per year which make up the total average inventory cost pcr year [13].

Let vi denote the unit variable cost of part i; Di, the demand per year for part i; I),: , the projcclcd demand per ycar ibr part i al'tcr standardization; Q, the rcplcnishment order quantity of part i, i n units; r, the inventory carrying rate per ycar; and A, thc ordering cost incurred wilh each replenishment, in dollars.

Therefore, if we usc the standard EOQ model for each part, we have

Now, average ordering cost per year

and average cycle inventory cost per year

After a careful review of existing design-engineering practices in the company, a conservative estimate of standardization at 20% was used in the case study.

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It is assumed that standardization at 20% results in 20% reduction from existing 30,000 total stable designs, that is, N' = 24,000. It is assumed that the

total set of 20,000 parts covers 80% ordemand. Reduction in orderirig cost per year =

where, annual carrying rate, r = 0.12; ordering cost per order, A= $100; N = 50,000; N' = 24,000; Annual demand in dollars before standardization =

Di = $8 million; and.

Annual projected demand in dollars after standardization = m

C D: v, = $20 million.

80% of annual demand in dollars before standardization = $6.4 million 80% of annual projected demand in dollars after standardization = $16 million Average annual demand in dollars per part before standardization = $6.4 million / 20,000 = $320 Average annual demand in dollars per part alier standardization

= $16 million 1 20,000 = $800 that is, Divi = $320, and Divi = $800 for each part i .

Substituting values given in Eqs. ( 3 , (6), and (7) into (4) yields Ordering cost savings per year = $528,121.35 $528,000

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CYCLE INVENTORY COST SAVINGS Equation (4) also represents cycle inventory costs savings per year. Therefore,

cycle inventory cost savings per year = $528,000 Total benefits or cost savings per year ((320% standardization) = Design cost savings + Warranty cost savings + Ordering cost savings + Cycle inventory cost'savings = $1,200,000 + ($240,000 + $1,700,000) + $528,000 + $528,000 = $4.2 million

For economic justification of undertaking engineering projects, 10% minimum attractive rate of return was the cutoff point used by the company.

Benefits of 5 years discounted at 10% per year Present Worth = (3.7907) ($4.2 million) = $15.92 million

Cost of "eDesign" Component Management System Estimated implementation cost = $5 million Benefit to Cost ratio = 15.92 / 5.0 = 3.18

OTHER POTENTIAL AI'I'LICATIONS AMENABLE TO CLASSIF~CATION

A nulnbcr of areas of opportunities identified in this company where classification approach could be applied include the following:

I . Sinlplifying operutionul managenlent andprontoting stutldardizaiion. One of the objectives of grouping is seeking reduction in a system's complexity, promoting standardization and procedures. Developing knowledge bases, whereby catego;ical decisions can be prescribed extensively, would eliminate the need to resolve recurring problems over and over again, and reinvent already known knowledge. This would relieve operating managers of routine tasks. In probabilistic environments with sizeable uncertainty, categorical decisions may not be justified. Decision support systems can be provided to aid decision-making in probabilistic environments.

2. Supporting product design department in scheduling variety control. A variety control system similar to the one described i n the case study for parts proliferation will eliminate acquisition of unnecessary tooling.

3. Supporting manufacturing department in,

a) Promoting group technology: Classification in this case will consider equivalence classes based on manufacturing process similarities.

b) Promoting higher facility utilization: This will require categorizing and reporting idle times on various equipment, namely, induced idle times due to bottlenecks, non-availability of parts. breakdowns.

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Diagnosis of various types of idle times can lead to timely corrective steps.

C) Enhancing muir~tenance cupabilities: A good classification of maintenance tasks can improve maintenance operations. Following broad categories of maintenance tasks were identified:

Scheduled preventive maintenance tasks, which have to be performed periodically. Unscheduled maintenance tasks resulting from scheduled

inspections. Unscheduled maintenance functions resulting from failures. Many of the maintenance tasks can be classified and better managed with respect to their scheduling and availability of supplies and parts.

d) Seeking improvements in purchasing and inventory costs: A good coding scheme should avoid parts from having multiple codes as it

results in split purchase orders and in stock-outs, even when inventories are present. The complexity of multi-part systems can be reduced by aggregating objects over different similar attributes such as, fast movers, slow movers, turnover rates, lead times, etc.

This research case study provides a conceptual framework for component management decision support system such as, "eDesignM for strategic product development in order to build a basis for its technical feasibility. An example of a leading transport refrigeration unit manufacturer is used to illustrate a three level decision making hierarchy with economic optimization for levels 1 and 2 representing standardization of system modules and capacity decisions for a product line respectively, and thermodynamic optimization for level 3 representing control systems to keep 'the system dynamically balanced with changing environments. The principal objective of the study is to present a detailed economic justification for implementing such a system in a product development environment. The proposed component management system can be utilized. and customized to support three levels of formalized standardization, facilitate in compressing time to market cycle for new and upgraded products and also control design proliferation. The study identifies other potential applications for classification.

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[3] DANGERFIELD, BYRON J . , and JOHN S. MORRIS, ''Relational Database Management Systems: A New Tool for Ctxling and Classification," lntertlutiorial Journal of Operutions & Productiott Manugmlent, Vol. 1 1 ,

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[5] GORDON, A.D., Classificutiotz: Methods for the exploratory analysis of multivariate data, Chapman & HalIlCRC, Boca Raton, Florida (1981).

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[9] MOSIER, CHARLES T., and RALPH E. JANARO, "Toward a Universal Classification and Coding System for Assemblies," Journal of Operations Management, Vol. 9, NO. 1 , 1990, pp. 44-64.

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[I41 SMITH, PRESTON G, and DONALD G. REINERTSEN, Developing Products in

Halfthe Time, Van Nostrand Reinhold, New York ( 1 995). [ I 51 SPATH. H., Cluster At~ulysis Algorithms for Data Reduction and

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[ 161 TANDLER, WILLIAM, "Financial Aspects of Machine Part Drawing Quality," Anlcricatl Muchinis! Magazine, September 1993, pp. 1 - 10.

SAMEER KUMAR is a professor i n the Department of Engineering and Technology Management at the University of St. Thomas, St. Paul, Minnesota. His major research areas include optimization concepts applied to supply chain management, product innovation, capital investments justification and total quality management effectiveness.

CHARU CHANDRA is an assistant prol'essor in Industrial and Manufacturing Systcms Engineering at the University of Michigan, Dearborn, Michigan. He is involved in rcsearch in Supply Chain Management, and i n designing a complex supply chain for a leading industrial sector. Specifically, his research focuses on studying complex systems with the aim of developing coopcrative models to rcprcscnt coordination and integration in an cnterprisc.

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