The relocation of a hybrid manufacturing/distribution facility from supply chain perspectives: a case study

The relocation of a hybrid manufacturing/distributionfacility from supply chain perspectives: a case study

Hokey Mina, *, Emanuel Melachrinoudis b

aDepartment of Marketing and Transportation, College of Business, Auburn University, Auburn, AL 36849, USAbDepartment of Mechanical, Industrial and Manufacturing Engineering, Northeastern University, 375 Snell Engineering Building, 360

Huntington Avenue, Boston, MA 02115, USA

Received 1 November 1997; accepted 18 March 1998

Abstract

In this paper we present a real-world case study involving the re-location of a combined manufacturing anddistribution (warehousing) facility. The relocation decision was called to adapt to dynamic changes in businessenvironments surrounding the ®rm's supply chain operations. Such changes include changes in supplier and

customer bases, distribution networks, corporate re-engineering, business climate and government legislation. To aidmanagement in formulating a more e�cient and e�ective relocation strategy, we designed the con®guration ofsupply chain networks and assessed the viability of the proposed sites from supply chain perspectives using theanalytic hierarchy process (AHP). # 1998 Elsevier Science Ltd. All rights reserved.

Keywords: Supply chain management; Warehouse location; Analytic hierarchy process

1. Introduction

Due to its strategic importance to the supply chain

competitiveness, the problem of locating manufactur-

ing plants (or warehouses) has received considerable

attention from academicians and practitioners alike

over the last several decades [3, 4, 12, 13]. In general,

the problem of locating manufacturing (or warehous-

ing) facilities is concerned with the determination of

the optimal number, size, and geographic con®guration

of those facilities in such a way as to minimize the

total cost associated with supply chain operations (e.g.

start-up investment, material acquisition, transpor-

tation, storage and production cost), while satisfying

customer demand requirements. The location decision

usually entails the ®rm's long-term commitment to the

established facility and thus is not intended to be chan-

ged quickly. Due to unforeseen changes in both exter-

nal and internal business situations, however, some

®rms may be faced with the decision of relocating the

pre-existing manufacturing and warehousing facilities.

Such changes include changes in customer and supplier

bases, distribution networks, corporate re-engineering

(or restructuring), business climate and government

legislation. In contrast with the typical facility location

problem, the relocation decision must smooth away

di�culties involved in the phase-out of the existingfacility and transition to the re-located facility in a

fashion that minimizes any potential disruption of

supply chain activities (see Fig. 1).

In light of the above discussion, this paper describesa case study of a ®rm which plans to move its current

manufacturing and distribution facility from Boston,

Massachusetts to a new location in the Northeast US.

Hereafter, we would disguise the name of the ®rm at

the request of its tra�c manager and call it Alpha. At

this juncture, Alpha's management does not want to

reveal their relocation plan, because such a plan may

a�ect the morale of employees working for Alpha'scurrent facility in Boston. Alpha has its headquarters

in Los Angeles, California and currently operates 49

pure distribution facilities along with ®ve combined

Omega, Int. J. Mgmt. Sci. 27 (1999) 75±85

0305-0483/98/$19.00 # 1998 Elsevier Science Ltd. All rights reserved.

PII: S0305-0483(98 )00036-X

PERGAMON

* Corresponding author. Tel.: +1-334-844-2460; Fax: +1-

334-826-6939; E-mail: [email protected].

manufacturing/distribution facilities across the US.Alpha primarily manufactures and sells chain linkfences and their related hardware items such as alumi-

num bars, rods, pro®les and wire, nationwide. Toavoid any duplicated marketing e�orts and redundant

production with the pre-existing hybrid manufacturing/distribution facilities in the Midwest, South and West,Alpha prefers the re-located site in one of three states:

Maryland, Pennsylvania and West Virginia. InMaryland, the cities that Alpha is considering areBaltimore, Hagerstown and Williamsport. In

Pennsylvania, cities of Easton, East Stroudsburg, Erie,Harrisburg, Pittsburgh and Port Jervis are being con-

sidered. Wheeling, West Virginia is also a candidatecity for relocation. In particular, Alpha is looking fora site that can o�er the best level of transit times (e.g.

next day delivery service) for its main customers in thenortheast region of the US and can provide greater

access to its major break-bulk terminals located inCamp Hill, Lancaster, Harrisburg and York inPennsylvania.

Furthermore, since the newly relocated site will serveas a re-distribution point for Alpha's own products

(galvanized chain links), imported materials and parts,the optimal site should be in close proximity to inter-modal transportation comprised of barges, ocean car-

riers, motor carriers, and rail. Because of the bulkinessof its products, however, Alpha seldom uses air trans-portation and consequently does not prefer a site with

easy access to airports. At the chosen site, Alpha needsa building structure with size ranging from 80,000 to

120,000 square feet. Alpha's relocated facility is alsoexpected to occupy approximately 12 to 16 acres ofland so that the site can accommodate the planned

consolidation of the Orioles Fence (a subsidiary ofAlpha) manufacturing plant in Baltimore in late 2000.

Some of the potential sites that Alpha is consideringappear on the surface to o�er the amenities describedabove, but there is no guarantee that those sites will

bring the most attractive strategic bene®ts to Alpha'sfuture supply chain operations. For example, a candi-date site which gives transportation advantages may

turn out to be most costly because of its urban setting.On the other hand, a prospective site which incurs the

lowest cost and provides the best tax incentivepackages may be distant from the Alpha's major cus-tomer bases and transportation infrastructure. To deal

with this dilemma, systematic decision-aid tools areneeded which consider a multitude of factors a�ecting

the relocation decision and explicitly consider tradeo�samong them. Such decision-aid tools may include var-ious multiple objective programming techniques

(MOP) and scoring methods such as the analytic hier-archy process (see, for example, in Refs. [5, 11, 18]excellent discussions of MOP and scoring methods).

There are comparative advantages and disadvantagesassociated with these decision-aid tools in terms of

ease of use, data requirements, computational di�cultyand sensitivity analysis capability.Considering that the relocation problem confronting

Alpha must deal with a relatively large number ofattributes within a hierarchical framework, we propose

Fig. 1. The supply chain network diagram of Alpha.

H. Min, E. Melachrinoudis / Omega, Int. J. Mgmt. Sci. 27 (1999) 75±8576

an analytic hierarchy process (AHP), which was ®rstintroduced by Saaty [17], as our decision-aid tool.

Also, AHP is more appealing than MOP to the practi-cing logistics manager who may have a limited techni-cal background. Another advantage of the proposed

AHP model is its ability to solve a practical size lo-cation problem without serious computational di�-culty, whereas the models requiring MOP techniques

such as constrained, multi-criteria simplex and adap-tive search methods necessitate extensive search ofnon-dominated solutions and thus severely limits its

usage for the inherently di�cult location problem [6].In fact, application of AHP to the site selection pro-blem is not uncommon as illustrated by the priorworks of Wu and Wu [21], Hedge and

Tadikamalla [10], Erkut and Moran [8] andMin [15, 16].

2. Location factors

To identify the criteria to be used for the ranking ofproposed sites, we take into account a total of 45di�erent factors that may potentially a�ect the reloca-

tion decision. These factors were initially presented forreview to a team of Alpha management comprised ofthe Director of Manufacturing, the Vice President of

Sales and Marketing and the Director of Operations.This team singled out a list of factors that were felt tobe important to the supply chain activities of three div-

isions: manufacturing, sales and marketing and oper-ations. To maintain a manageable number of locationfactors for evaluation, we eliminated 12 factors that

were perceived to be insigni®cant to the relocation de-cision by the team. As a result, we compiled a total of33 location factors and broke down these factors intosix broad categories (criteria) to structure these factors

in a hierarchical form. These six categories are:(1) Site characteristics: since a modern, well-

equipped manufacturing/distribution facility requires a

large building with area of approximately 100,000square feet, the recommended depth (foundation) ran-ging from 150 to 175 feet and clear height of 24 to 40

feet, we (the location planners) have considered site-speci®c features such as building design, capacity, soilcondition (e.g. compaction, drainage and grade el-evation) and deed restrictions on the propose site. In

particular, Alpha is planning provisions for potentialexpansion that can accommodate the consolidation ofthe Orioles Fence manufacturing operation; therefore,

enough room for building expansion is an attributethat cannot be overlooked by the management team.Also, we have investigated how the proposed site is

zoned and whether the chosen location is quali®ed forspecial zone delivery rates. In an improper zone, Alphamay not obtain building, utility and Fire Marshall per-

mits which are necessary for site preparation and

building construction.(2) Cost: cost is one of the primary concerns of the

relocation decision. The total costs of relocating a

hybrid manufacturing/distribution center to a new siteencompass costs associated with land acquisition,

appraisals, building construction including railroad sid-ing, architectural fees, zoning permits, ad valoremproperty tax (e.g. levied at 40 to 60% of the appraised

value of property), maintenance including liabilityinsurance premiums and operation (e.g. labor and uti-lity). Among these cost elements, both land acquisition

and building construction would be a substantialinvestment because Alpha requires 12 to 16 acres of

land space. In the case of Williamsport in Maryland,for example, land acquisition cost for an industrial siteof such size, zoned with water, sewer and rail, can

range from US$420,000 to US$720,000. In addition,building construction cost alone may require millionsof dollars of a capital outlay. For example, Ackerman

and LaLonde [2] estimated that construction of a new24-foot clear building would cost the company

US$18.15 per square feet in 1980. Using this conserva-tive ®gure adjusted for the average annual in¯ationrate of 3%, construction cost of 100,000 square feet of

a new building today is estimated to be almostUS$3,000,000. Such a large cost ®gure would put great®nancial strain on the ®rm undertaking the relocation

project and subsequently burden the ®rm's share-holders.

(3) Tra�c access: since Alpha primarily deals withrelatively heavy and bulky hardware products, we havelooked for a site that can not only handle the large

volume of tra�c, but can also provide easy access totransportation infrastructure and tra�c related ser-vices. To elaborate, important tra�c concerns include

proximity to major interstate highways, any overheadconstruction impairing truck movement, curfew restric-

tions on hours of tra�c operations, railroad and bargeaccess, close distance to break-bulk terminals and theavailability of freight forwarding and brokerage ser-

vices. In addition, Alpha requires that the site bewithin one day delivery range to most of its main cus-tomers such as Home Depots and Lowes in the

Northeastern US. Finally, a site in the vicinity offoreign trade zones (FTZs) or customs bondage sto-

rage may be more attractive, because Alpha can saveor delay the duty payment for its materials and parts(e.g. steel fence ®ttings, pipes and fasteners) imported

mainly from China, Taiwan and South Americancountries.

(4) Market opportunity: the pro®tability of Alphadepends heavily on the market potential of the area itserves. In a broad sense, the market potential of a cho-

sen trading area is dictated by the business climate andthe competition level in the trading area. As a barom-

H. Min, E. Melachrinoudis / Omega, Int. J. Mgmt. Sci. 27 (1999) 75±85 77

eter for the local business climate, we considered the

potential customers' aggregated e�ective buying powerand buying power index in the area (metropolitan cityor county) that surrounds the proposed site. Herein,

e�ective buying power is expressed as net gross per-sonal income (=gross personal incomeÿ personal tax-esÿ non-tax payments). Buying power index is a

measure of market ability to buy, expressed as a per-centage of US totals. In addition, Alpha's past sales

volumes and forecasted future business growth aretaken into consideration. Furthermore, the regionalranking as either principal or regional business center

is factored into the site selection decision.Under the premise that a majority of customers

would be gravitated toward the ®rm's distributionfacility closer to their population center, we use proxi-mity to existing and potential customers as part of a

surrogate measure of Alpha's competitive position inthe trading area. Also, considering that the Alpha site'sbeing overlapped by its own pre-existing facility may

weaken its competitive position, we consider proximityto other Alpha facilities as another part of a surrogate

measure. Finally, since short inbound delivery to thefacility can reduce order cycle time for the entiresupply chain and subsequently help serve customers

better, proximity to Alpha's suppliers' locations is con-sidered as an indicator of Alpha's competitiveness in

the trading area. Given that the amount of rates (e.g.class rates) for the transportation of freight increasesas mileage scales (e.g. rate basis numbers) increase,

proximity to Alpha's suppliers scattered aroundIllinois, Missouri, New York, Connecticut andMassachusetts also a�ects inbound shipping cost.

Furthermore, since Alpha's sources of supply includeforeign suppliers in Far Eastern and South American

countries, proximity to major ports of entry such asHouston, New Orleans and Tampa is also factoredinto the relocation decision.

(5) Quality of living: since the proposed manufactur-ing and distribution facility is expected to hire hun-

dreds of employees including plant workers and truckdrivers whose labor productivity and turnover may bea�ected by quality of life in their residence or work,

we take into account local climate, crime rate, pol-lution, living expense and tra�c congestion for therelocation decision. For example, many truck drivers

will be reluctant to be relocated to a high crime andcongested area, even though such reluctance may

increase their unpaid `dead head' trips to their home.As such, the site with unfavorable quality of living islikely to cause high driver turnover which, in turn,

may severely disrupt Alpha's tra�c operations.Similarly, plant workers and their spouses may beunwilling to be relocated to the area where quality of

life is no better than their current residence or work.Consequently, Alpha will be forced to hire a large

number of new employees to replace current workers.Since new hires at the relocated site may be less fam-

iliar with Alpha's supply chain operations than arecurrent employees, the site with relatively poor qualityof life may a�ect labor productivity at the relocated

facility.(6) Local incentives: in an e�ort to amortize huge

start-up investment for a new site and increase the

return-on-investment from the newly established facil-ity, we have made local incentives an `up-front' con-sideration. According to the recent Conway Data

global survey of development organizations [20], localincentives such as labor quality, tax breaks and loanswere considered one of the ®ve most important lo-cation factors by thousands of economic development

executives. In particular, the Alpha management teamis greatly concerned with potential labor strife andtough local nuisance and environmental regulations.

As a matter of fact, the risk of strikes or other labordisputes is a key concern in warehousing because sucha risk can disrupt continuous distribution of products

to customers [1]. Thus Alpha wishes to ®nd a sitewhere good labor-management relations can be sus-tained over an extended period of time. In addition,

Alpha is in favor of a site where a broad range of taxincentives (e.g. enterprise zone incentives, job creationtax credits or super tax credits for capital investmentand job creation) and industrial park services are avail-

able. For example, in the state of Maryland, a com-pany located in the enterprise zone may be eligible toreceive tax credits of 80% during the ®rst ®ve years,

while US$1,500 per employee job creation tax creditsmay be o�ered under a certain condition.Given the above factors, the relocation decision

addresses the following issues:(1) Which relocation at the hybrid manufacturing

and distribution facility can maximize Alpha's long-term pro®tability.

(2) How to analyze the tradeo�s among the six lo-cation criteria described above.(3) How to evaluate the sensitivity of relocation de-

cisions with regard to changing priorities of Alpha'sstrategic plans.

3. AHP model design and application

In general, AHP is a scoring method that wasdesigned to visually structure a complex decision pro-blem into a simple hierarchy and then develop priori-

ties within each level of the hierarchy by carrying outsimple pairwise comparisons of the relative importanceof decision criteria, attributes, and alternatives. For a

conceptual foundation of AHP, the interested reader isreferred to Refs. [9, 17, 19]. The use of AHP is suitablefor a relocation decision facing Alpha for two main


reasons: (1) AHP is an e�ective tool for dealing with a

relocation decision involving a large number of tangi-ble (e.g. cost) and intangible (e.g. quality of living) lo-cation factors with di�erent scales; (2) with a user-

friendly feature, AHP allows the location planner tonot only visualize preferences among the relocationalternatives, but also identify inconsistent judgments

and correct them during the decision process.To demonstrate how the AHP model works and to

verify its usefulness, the model was applied to the real-world problem facing Alpha. As explained earlier, theAlpha management team initially identi®ed ten poss-

ible industrial sites. However, since simultaneousevaluation of the ten di�erent sites would require an

excessive number of pairwise comparisons (i.e. 7,020comparisons) and consequently overwhelm the locationplanners' consistent judgment capabilities, some of

these sites were screened using an AHP-based strati®-cation scheme analogous to Erkut and Moran'smethod [8]. For instance, some potential sites such as

East Stroudsburg, Port Jervis and Easton are relativelyclose to each other and have exhibited similar location

characteristics in terms of tra�c access, market oppor-tunities and local incentives. After a series of pairwisecomparisons of similarly located candidate sites using

AHP, we identi®ed the most dominant (single best) sitein each state. In the state of Maryland, we selected

Williamsport as the ®nal candidate site, whileHarrisburg turns out to be the most favorable siteamong six potential sites in the state of Pennsylvania.

Therefore, the three sites: (1) Harrisburg,Pennsylvania, (2) Williamsport, Maryland and (3)Wheeling, West Virginia still remain as a ®nal list of

sites for further consideration. These three ®nal candi-date sites appear at the bottom level of a hierarchy

shown in Fig. 2. To complete a hierarchical represen-tation of the relocation decision, we aggregate 33 lo-cation factors into six broad relocation criteria

(clusters) and then branch these criteria at the secondlevel of a hierarchy (see Fig. 2). To validate these cri-teria, we used the two simple rules: (1) the speci®c lo-

cation factors belonging to the same criteria arecommensurate (homogeneous) with each other and (2)

the list of criteria should be limited to a reasonablysmall number that allows for consistent pairwise com-parisons (relative measurement). In fact, Miller [14]

observed that an ordinary decision maker could nothandle more than seven or nine decision elements (e.g.

criteria and alternatives) simultaneously without beingconfused.Similar rules described above were also used to de-

®ne location attributes under the third level of a de-cision hierarchy. As such, we identi®ed 26 locationattributes that mostly coincide with original location

factors with the exception of some cost elements. Forinstance, cost elements associated with land acqui-

sition, building construction, and zoning permits are

clustered together as `start-up' costs because all ofthose are ®xed cost components expressed in dollarterms. On the other hand, variable cost elements as-

sociated with labor, maintenance, insurance, utility andcompensated fringe bene®ts are grouped together as`operating' costs. After constructing the complete hier-

archy, we have made pairwise comparisons of decisioncriteria, attributes, and alternatives rather than using

absolute measurement scales since absolute measure-ment tends to be very subjective. Relative weights ofrelocation criteria were derived from such pairwise

comparisons and summarized in Fig. 2. Herein, theserelative weights represent the Alpha management

team's judgments (or opinions) on the relative import-ance of the relocation criteria to the relocation de-cision. In the last step of the AHP process, the relative

weights are aggregated to produce a set of synthesizedpriorities (local and overall priority scores) for thethree ®nal candidate sites using EXPERT CHOICE [7].

Table 1 recapitulates local priority scores with respectto location attributes.

The overall priority for each prospective site is out-lined in Fig. 3. As shown in Fig. 3, Harrisburg turnsout to be the most preferable site among the three

alternatives, with an overall priority score of 0.374. Toelaborate, Harrisburg appears superior to the other

two alternatives with respect to tra�c access and mar-ket opportunity. For instance, the Harrisburg area isthe home to the second largest rail yard in the US and

is served by several competing class one railroads suchas Burlington Northern/Santa Fe, Conrail, Delawareand Hudson and Canada Paci®c. The Harrisburg area

also has the fourth largest interstate and regional high-way network in the US and is served by more than 30

motor freight common carriers. The major ports suchas New York and Philadelphia can all be accessed inone day, not to mention the immediate access to inter-

modal terminals and break-bulk operations in thevicinity of Harrisburg. In addition to excellent tra�caccess, Alpha can reach its major customers (home

improvement stores) located in Dickson City,Mechanicsburg, Reading, Pennsylvania and

Gaithersburg, Maryland quickly from the Harrisburgsite. Furthermore, Harrisburg is at the core of the cen-tral market that can serve more than 70 million popu-

lations within a 300 mile radius.On the other hand, Williamsport is a close second,

surpassing both Harrisburg and Wheeling sites interms of cost and quality of living, whereas Wheelingprovides the greatest location incentives among the

three potential sites. To elaborate, Williamsport's ruralsetting and relative low cost of living (e.g. cost of liv-ing index = 100.8) translate into a�ordable land prices

and low operating costs. For instance, average hourlyearnings for a warehouse specialist in Williamsport are


US$10.50, slightly below the US national average of

US$12.06. In addition, Williamsport has no county tax

and its state corporate income tax is more than 22 per-

cent lower than Harrisburg and Wheeling. Such a low

tax rate may contribute to low operating cost.

Considering the above merits, we recommend

Williamsport as a backup alternative or a possible con-

tingency. To assure that Williamsport is superior to

other sites (e.g. Pittsburgh and Easton) in

Pennsylvania, alternative sites that were eliminated in

the pre-screening process are revisited for further con-

sideration as a backup. Our analysis indicates that

Williamsport still outperformed other Pennsylvania

sites such as Pittsburgh and Easton. For instance,

Fig. 2. A hierarchical representation of the relocation of a hybrid manufacturing/distribution facility.


Williamsport produced the overall priority score of0.449, whereas Easton and Pittsburgh scored 0.328 and

0.223, respectively.

Thus far, we have identi®ed Harrisburg as themetropolitan area which is best suited for the Alpha's

relocated facility. However, considering that two separ-ate properties in the same geographical area (e.g.

Harrisburg) may have di�erent site-speci®c character-istics, a more detailed analysis has to be performed to

determine a particular property in that area. With thehelp of local development agencies such as Capital

Region Economic Development Corporation(CREDC) and Pennsylvania Power and Light

Company, we identi®ed two commercial propertiesthat meet the basic requirements for Alpha's facility lo-

cation. Both of these properties are located in Swatara

Township in Harrisburg and belong to a CommercialLimited zoning district where warehousing and manu-

facturing/assembly uses may be permitted upon reviewand approval of the local zoning o�cer. Also, they are

located just o� the Interstate Route 83 and can easilyaccess Interstate Routes 283 and 81 and the

Pennsylvania Turnpike (I-76).Since neither one of them has any distinctive advan-

tage over the other with respect to tra�c access, mar-

ket opportunity, quality of living, and local incentives,our comparison of these properties is primarily based

on their site-speci®c characteristics and cost. To elab-orate, one of them (called property A hereafter) has

114,500 square feet of an old building structure that

can be leased for a minimum of 5 years at an annual

cost of US$423,650, whereas another one (called prop-

erty B hereafter) has about 80,000 square feet of pro-

duction and warehouse space for sale at an

undisclosed but estimated price of US$2 million.

Additional cost may be incurred for both of these

properties. For example, property A requires extensive

structural repairs including the installation of an air

conditioning system, whereas property B will be

assessed with an estimated real estate tax of US$20,000

(county and school). In addition, we considered a list

of speci®c physical characteristics such as total square

footage, ceiling height, ¯oor level, column spacing,

construction materials, heating, ventilation, air con-

ditioning, plumbing, lighting, electric capacity, sprink-

ler systems, rest rooms, o�ce spaces, parking spaces,

loading docks and drive-in doors. In terms of building

speci®cations, property A seems to be superior to

property B. For example, property A has the more

desirable ceiling height (31 feet clear ceiling) and

square footage than property B. On the other hand,

property A seems to be a more costly option than

property B in the long run, since the former's 5 year

leasing cost alone may exceed the purchase price of the

latter. Therefore, the ®nal selection of these properties

may depend on Alpha's lease, buy or re-building de-

cisions which are beyond the scope of this study.

Fig. 3. A value path for the overall priority of relocation alternatives.


Table 1

Local priority scores of prospective sites

Criteria/attributes Prospective sites Priority scores Ranks

(1) Site characteristic

Land capacity and landscaping Harrisburg 0.007 1

Williamsport 0.006 2

Wheeling 0.005 3

Compatibility with the current site Harrisburg 0.007 1


Wheeling 0.004 3

Deed restrictions Harrisburg 0.014 1 (tie)

Williamsport 0.014 1 (tie)

Wheeling 0.013 3

Building con®guration and size Harrisburg 0.011 1

Wheeling 0.010 2


Room for building expansion Williamsport 0.006 1 (tie)

Wheeling 0.006 1 (tie)

Harrisburg 0.005 3

Soil conditions for the proposed sites Harrisburg 0.010 1 (tie)


Wheeling 0.009 3

(2) Cost

Facility establishment costs Williamsport 0.038 1

Wheeling 0.035 2

Harrisburg 0.027 3

Facility operating and maintenance costs Wheeling 0.028 1 (tie)


Harrisburg 0.026 3

(3) Tra�c access

Proximity to on and o� ramps to highways Harrisburg 0.024 1


Wheeling 0.008 3

Proximity to main rail lines Harrisburg 0.032 1


Wheeling 0.013 3

Proximity to truck terminals Harrisburg 0.046 1


Wheeling 0.004 3

Proximity to waterway Wheeling 0.020 1


Harrisburg 0.010 3

(4) Market opportunity

Proximity to customers locations Harrisburg 0.020 1


Wheeling 0.007 3

Proximity to suppliers locations Harrisburg 0.013 1


Wheeling 0.011 3

Proximity to competitors' locations Williamsport 0.011 1

Wheeling 0.007 2

Harrisburg 0.002 3

Proximity to other Alpha facilities Harrisburg 0.015 1 (tie)


Wheeling 0.006 3

Market potential Harrisburg 0.022 1


Wheeling 0.003 3

continued opposite


4. Sensitivity analysis

Although a base-line solution re¯ected the current

relocation scenario where tra�c access is most import-ant, the model solution can change in accordance with

shifts in Alpha's strategic plans. To explore the re-sponse of model solutions to potential shifts in the pri-

ority of Alpha's strategy, a series of sensitivity analyses

of criteria weights were performed by changing the pri-

ority (relative importance) of weights. As summarized

in Table 2, the results indicate that the model solutions

are sensitive to changes in the importance of cost,

quality of living and local incentives. Speci®cally, when

the importance of cost is increased from 0.181 to 0.35,

Williamsport is the best site. Even Wheeling will be

more attractive than Harrisburg if the weight assigned

to cost is 0.65 or greater. A similar pattern can be

observed when Alpha prioritizes quality of living for

the relocation decision. For example, when the import-

ance of quality of living is increased from 0.122 to

0.35, Williamsport dominates the other two sites.

Notice also that the rankings of the prospective sites

constantly change across the entire weight range in ac-

cordance with changes in the importance of local

incentives. Speci®cally, Williamsport will be the best

site if the weight of local incentives is increased from

0.164 to 0.40, whereas Wheeling will be the best site if

the weight of local incentives is close to 1.00. However,

it may be worth noting that Wheeling is dominated by

Williamsburg, with the exception of the fact that

Table 1Ðcontinued

Criteria/attributes Prospective sites Priority scores Ranks

(5) Quality of living

Climate and pollution Williamsport 0.013 1

Harrisburg 0.008 2

Wheeling 0.004 3

Crime rate Wheeling 0.012 1

Harrisburg 0.009 2


Living expense Wheeling 0.009 1


Harrisburg 0.008 2 (tie)

Local tra�c congestion Williamsport 0.027 1

Wheeling 0.010 2

Harrisburg 0.009 3

(6) Local incentives

Strength of local labor unions Williamsport 0.028 1

Wheeling 0.014 2

Harrisburg 0.009 3

Availability of skilled labor Harrisburg 0.012 1

Wheeling 0.006 2


Availability of investment tax incentives Williamsport 0.008 1

Wheeling 0.006 2 (tie)

Harrisburg 0.006 2 (tie)

Industrial park services Harrisburg 0.012 1

Wheeling 0.006 2


Toughness of local nuisance/environmental laws Wheeling 0.026 1


Table 2

Sensitivity analyses of location criteria

Location criteria Degree of sensitivity

Site characteristics insensitive

Cost sensitive

Tra�c access a little sensitive

Market opportunity insensitive

Quality of living sensitive

Local incentives sensitive

"Sensitive" = a ranking of all the prospective sites changes

dramatically in the entire weight range.

"A little sensitive" = a ranking of two prospective sites

changes gradually in the very limited weight range.

"Insensitive" = a ranking of all the prospective sites remains

the same in the entire weight range.


Wheeling scores slightly higher than Williamsport onlocal incentives (see Fig. 3).

On the contrary, the model solutions are insensitiveto changes in the importance of site characteristics andmarket opportunity, since Harrisburg remains the best

site regardless of changes in such importance. Finally,the model solution is little sensitive to changes in theimportance of tra�c access, because Williamsport can

be the more preferable site than Harrisburg only whenthe weight of tra�c access is decreased from 0.216 to0.15. These sensitivity analysis results provide Alpha

with practical insights into what-if scenarios. Forexample, practically speaking, Williamsport could be agood substitute for Harrisburg if Alpha is severelyrestrained by a budget limit or Alpha depends heavily

on private carriers for its inbound and outbound trans-portation of goods. Also, if a majority of Alpha's cur-rent employees are reluctant to relocate to a new site

from the existing facility in Boston due to their con-cerns over the deteriorating quality of life,Williamsport can be more appealing than Harrisburg.

In the case that local incentives o�er Alpha a substan-tial reduction in tax burden and risks of a labor strife,either Wheeling or Williamsport may be preferred over

Harrisburg.

5. Concluding remarks

Since location of a hybrid manufacturing/distri-

bution facility dictates the physical ¯ow of goods fromsources of supply to consumption points, such a lo-cation decision is at the core of strategic supply chain

network planning. We have applied the AHP approachto the relocation problem facing a ®rm which primarilymanufactures and distributes home improvement hard-ware. The proposed AHP model was designed to ®nd

the most preferred site as well as its relative advantagesover other candidate sites. The model is capable ofhandling multiple con¯icting objectives such as the

minimization of cost, tra�c accessibility, the maximi-zation of market opportunities and local incentives.Consequently, it can aid the location planner in ana-

lyzing the various tradeo�s among the competingobjectives and the implications of strategic relocationdecisions.As part of contingency planning, the model enables

the location planner to evaluate `what-if' scenarios as-sociated with shifts in the company's management phil-osophy and competitive positions. In addition, the

model enables the location planner to determine theextent of con¯icts among the competing objectives.Unlike MOP techniques, the proposed model not only

captures many realistic dimensions involving a largenumber of location factors, but also poses little com-putational di�culty. As such, the model is likely to be

recommended as a practical decision making tool bymany practitioners confronting similar problems.

Despite the above merits, the proposed model pointsto a number of directions for future work: the modelcan be expanded to include the element of risk and

uncertainty involved in the hybrid plant/warehouserelocation project and it can be tested for an expandedregional area and for a longer time period.

Acknowledgements

The authors wish to thank Charlotte Chancellor of

the Conway Data, Mary Burkholder of the MarylandDepartment of Business and Economic Development,Stephen L. Christian, Jr. of the West Virginia

Development O�ce, Sheila Cerulli of the PennsylvaniaPower and Light Company, Carol Kilko of the CapitalRegion Economic Development Corporation and

Charles Van Keuren, Jr. of the Lehigh ValleyEconomic Development Corporation for providing theauthors with invaluable data and insights into the sitefeasibility and evaluation studies. Also, the authors

want to express the deepest gratitude to the manage-ment team and tra�c manager of the anonymous com-pany for providing the authors with valuable

assistance in the case study reported in this paper. Lastbut not least, the authors want to thank three anon-ymous reviewers for their invaluable comments on an

earlier version of the paper.

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The relocation of a hybrid manufacturing/distribution facility from supply chain perspectives: a case study