0115 966 7955 Today's Opening Times 10:00 - 20:00 (BST)

Supplier Selection Process in the Aerospace Sector

Disclaimer: This dissertation has been submitted by a student. This is not an example of the work written by our professional dissertation writers. You can view samples of our professional work here.

Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of UK Essays.

Executive Summary

The ever increasing competition in global markets today has led businesses and companies to find different methods for reducing production and manufacturing costs in order to maintain their competitive edge. The competition has no longer remained company to company but has become supply chain to supply chain. From a buyers perspective a qualified supplier is a key factor to reduce costs. Thus supplier selection and evaluation has gained vital importance in the supply chain management environment. It is extremely essential to develop a supplier selection model which is efficient, effective and considers all the aspects required by the company.

A number of supplier selection methods are available in the current literature. Creating a model based on these methods that addresses the particular requirements of the company is vital. The following paper is in 5 sections. The literature review in the first section is on the various methods for supplier selection and evaluation. The following methods are reviewed.

  • Mathematical Programming
  • Data Envelopment Analysis
  • Analytical Hierarchy Process
  • Analytical Neural Network
  • Fuzzy Set theory

Along with the review of the methods a discussion on the evolution of supplier selection criteria is also included in the first section. In the second section, two existing supplier selection models in the aerospace sector have been critically reviewed. The description of the Aerospace industry and comparison between the two models is included in the third section. To determine the essential criteria to be included in the model and prioritizing them, the research methodology used was a survey design. The results of the survey are included in the fourth section. The fifth section contains recommendations for building a new model for supplier selection in the aerospace sector.


One of the major topics discussed in most of the production and operations management literature is supplier selection and performance evaluation of suppliers. It is one of the most critical activities of firms due to the increasing significance of the purchasing function (De Boer et al., 2001). The main objective of a supplier selection process is to maximize overall value to the purchaser, reduce the purchase risk and develop a close and long term relationship between the buyer and supplier. Supplier selection is a multi-criterion decision making problem and a number of conflicting factors affect its outcome. The factors taken into consideration are wide ranged and are both quantitative as well as qualitative (Ho et al., 2009). Operational research offers a range of methods and techniques in the form of models which can support the supplier selection decision making. A number of supplier selection methods have been proposed such as data envelopment analysis (DEA), analytic hierarchy process (AHP), mathematical programming, fuzzy set theory and vague set theory, multi attribute rating systems etc. A literature review of international journal articles discussing different multi-criteria supplier selection methods is carried out in this paper. The methods that are prevalently applied in practice, the priority of the evaluating criteria and evolution of selection criteria are also discussed and reviewed. The aim of this paper is to carry out a literature review of the various methods and criteria for supplier selection available in the current literature, in order to produce a set of recommendations for building a new model for the supplier selection process in the aerospace sector. To achieve this, two supplier selection models were critically reviewed, one of which is currently implemented in an aerospace industry and the other one is a theoretical model. A survey on global sourcing and supplier selection process containing 25 questions on various aspects of strategic sourcing was also carried out in order to identify the different characteristics that influence sourcing decisions.

Supplier selection methods:

Supplier selection methods or techniques are the models which are used by decision makers to conduct the supplier selection process. They act as supporting tools for the selection process. The selection of an appropriate method is essential for the overall selection process and can significantly influence the outcome of the selection results (Li et al., 1997). There are number of supplier selection methods available in the literatures.

Mathematical Programming (MP):

MP allows the formulation of the decision problem in the form of a mathematical objective function which needs to be minimised or maximised depending on the objective function by varying the values of the variables. It is an optimization method which selects a number of suppliers in order to maximize either a single criteria or multi criteria objective function subjected to supplier or buyer constraints (DeBoer et al., 2001).

Talluri and Narasimhan (2003) used mathematical programming in the form of a linear programming model to first minimise and then maximise the performance of the suppliers against the best target measures set by the buyers, thus providing a wide-ranging understanding of supplier performance. The authors applied this model considering a set of six suppliers to a Fortune 500 Pharmaceutical company in the process of implementing a JIT system. They regarded price, quality and delivery as the top three criteria for evaluating the suppliers. One of the key features of this max-min approach was that it could identify a set of suppliers with identical characteristics, thus providing the buyer with effective alternates to make their final decision. For the supplier selection problem Ng (2008) developed a weighted linear programming model with an objective function of maximizing supplier score. He implemented the model considering 18 suppliers to a manufacturing firm producing agriculture and construction equipment. He included five criteria namely quality, supply variety, delivery, distance and price. In order to maximise the revenue function Hong et al. (2005) developed a mixed integer linear model to optimize the number of suppliers and order quantity. He applied the model to the supply chain of the agriculture industry in Korea as the customer demand varied seasonally over a period of time. Similarly O'Brien et al. (2001) created a mixed integer non-linear model to optimize the allocation of products to suppliers thus minimizing the annual purchasing costs. Narasimhan et al. (2006) and Wadwa et al. (2007) constructed theoretical multi-objective programming models to optimize supplier selection and order quantity and to minimise lead time, price and number of rejects. Karpak et al. (1999) constructed a goal programming model and applied it to an international manufacturing firm to minimise costs and maximise quality and delivery reliability for selection of suppliers and allocation of products between them. The authors considered cost, quality and delivery reliability as the criteria for supplier evaluation.

On one hand Mathematical programming is advantageous as compared to the other approaches as it takes into account all the constraints during the formulation of the problem. Hence it is much easier to work when a large number of constraints are considered. It can also be used for multiple supplier selection as the current situation can be taken into account in an MP model. On the other hand some of the drawbacks of using an MP model are that it often only considers the more quantitative criteria neglecting the qualitative criteria which are important in supplier selection especially when the goal is to build supplier partnership. Most of the theoretical MP models are complicated to build for the supplier selection problem, due to the large number of variables, but as it can be seen from the above mentioned examples, they can be implemented in an industry as they can be simulated and solved by computers. They are not considered as the most effective method for vendor evaluation as they do not take into account qualitative factors and are incapable of performing a qualitative analysis which is an important aspect of the supplier selection process, thus limiting their use.

Data Envelopment Analysis (DEA):

The concept of DEA is constructed on the basis of calculating the efficiency of the decision alternatives or suppliers. The DEA is a non-parametric method the measures the efficiency without specifying the form of the production function or the weights of different inputs and outputs. The efficiencies are evaluated on the basis of benefits as output and cost as the input criteria (DeBoer et al., 2001). The efficiency of a supplier can be defined as the ratio of the weighted sum of the suppliers outputs to the weighted sum of his inputs, thus the DEA method calculates the most favourable set of weights for each supplier alternative classifying them into efficient and inefficient suppliers. The favourable set of weights that are calculated maximise the supplier efficiency ratings without altering its own rating or making the other suppliers efficiency ratings more than one (DeBoer et al., 2001).

In order to measure the efficiency of alternative suppliers Braglia and Petroni (2000) applied the DEA method by proposing nine evaluating factors to measure the supplier ratings. The authors applied their proposed methodology to the supplier selection process of a middle-sized company manufacturing bottling machinery to test its efficiency. They also calculated the Cross efficiencies in which the weights chosen for a particular supplier can be applied to the weights of the inputs and outputs of the other suppliers as well as Maverick index which is the percentage relative difference between cross efficiency and simple efficiency in order to avoid the selection of false positive supplier. Talluri and Barker (2002) and Talluri and Sarkis (2002) applied DEA to evaluate suppliers, manufacturers and distributors as a three phase approach for a logistics distribution network. They also employed the DEA to measure the performance of the suppliers using six evaluating factors having two inputs and four outputs. Ross et al. (2006) evaluated the supplier performance with respect to the performance attributes of both buyer and supplier by using DEA. The author carried out three sensitivity analysis; the first one computed supplier efficiency scores without taking into account the evaluation team's and the buyers weights. The second analysis considered the evaluation taking into account the team's preferences and the third analysis considered the buyers preference.

Liu et al. (2000) constructed a DEA model to evaluate the overall performance of a supplier considering three inputs namely price index, delivery performance and distance factor and two outputs which were supply variety and quality. The authors applied the DEA model to a firm manufacturing agriculture and construction equipment containing a multi modal assembly line. The model could select suppliers with a high supply variety, thus reducing the number of suppliers. Seydel et al. (2006) developed a DEA model to evaluate technology suppliers considering three factors. He included amount of know-how transfer as a qualitative factor in the model. The author developed a five point scale to rank the suppliers in term of the qualitative factor.

The DEA method provides a means to evaluate and select suppliers on the basis of their performance over a period of time. It compares supplier performance in a multi criterion setting thus allowing the purchasing firm to evaluate each supplier's performance relative to the performance of the best supplier in the market by calculating the efficiency measures. Observed supplier performance data is used in a DEA method, thus the purchasing firm does not have to calculate its own utility functions as is required in the other techniques. Some of the limitations of the DEA approach are that its focus is not on selection an optimal supplier as the other mathematical programming models; hence it cannot be used if the purchasing firm requires the selection of an optimal supplier. The DEA model also makes some assumptions like any other supplier selection model thus limiting its use. (Garfamy et al., 2006)

Evolution of the Analytic Hierarchy Process (AHP):

Linear Weighting Model:

Weights are assigned to the criteria with the largest weight corresponding to the highest priority, in a linear weighted model. The ratings of the criteria are then multiplied with their respective weights and the sum of weights is assigned to each supplier, thus the supplier with the highest overall rating can be selected. There are a few imprecision's in the rating mechanism such as difficulty to determine the score of a supplier on a criterion or importance of some criterion with a high degree of precision. To overcome these imprecision's the use of analytic hierarchy process (AHP) was proposed (DeBoer et al., 2001).

Analytic Hierarchy Process:

The AHP is a decision making method first introduced by Saaty (1980) which prioritizes alternatives or suppliers when considering multiple criteria, thus allowing the decision maker to restructure complex problems in the form of a set of integrated levels or a hierarchy. It is one of the most commonly applied methods in practice as it incorporates qualitative as well as quantitative criteria and is relatively simple to understand. Various adaptations of AHP have been developed since its introduction.

Muralidharan et al. (2002) developed an AHP model consisting of five stages to rate and select suppliers considering nine criteria. Some of the major criteria that the author considered were quality, delivery, price and technical ability. The model was then applied to the supplier selection process to evaluate six suppliers of a leading organization manufacturing bicycles. Liu and Hai (2005) created an AHP model and used Noguchi's voting and ranking system thus allowing each manager to determine the order of criteria instead of weights for the selection and evaluation of suppliers. They used a six step process for supplier evaluation and considered eight criteria in their analysis, some of them being quality, responsiveness, delivery, technical capabilities etc. The authors applied this model for selecting one of ten suppliers for the Umbrella Scheme of Malaysia's furniture industry.

Chan and Chan (2004) constructed an AHP model considering six criteria namely cost, delivery, flexibility, innovation, quality and service with twenty sub-factors among them. They applied the model to the supplier selection process of a leading company that manufactures and supplies semiconductor assembly equipment assuming that the supplier had to be chosen for a critical product. The relative priority ratings were calculated based on customer or buyer requirements. Hou et al (2007) developed a decision support system based on AHP in a mass customization environment considering internal and external factors to meet market requirements. The author applied the model to the selection process of a subsidiary company of a local Chinese printer manufacturer. Chan (2003) created an AHP based interactive selection model which determined the relative importance of evaluating criteria without being subjected to human judgment. The AHP model can also be integrated with other supplier selection models in order to achieve optimized selection results.

Ramanathan (2007) suggested that the qualitative and quantitative information gained from the total cost of ownership model and AHP model can be utilized to evaluate the performance of a supplier using the DEA method. The author considered the costs from total cost of ownership as inputs and the weights gained from the AHP method as outputs. Sevkle et al. (2007) applied the AHP-DEA integrated method to solve the supplier selection problem of a major Turkish TV manufacturer BEKO, in which he used AHP to derive local weights from a given comparison matrix and summed up the local weights to get the overall weights. In order to calculate the efficiency scores of all the suppliers DEA was used on the decision making units. Percin (2006) applied integrated AHP-GP method, where AHP was used to measure the priority weightings of alternate suppliers considering twenty evaluating factors. The author used the weightings obtained by AHP Goal programming method as the coefficients for five objective functions. The integrated model was used to optimize the order quantity from the most appropriate supplier considering the capacities of the suppliers.

Mendoza et al. (2008) offered an integrated AHP- GP model in order to reduce a large number of potential suppliers to a manageable figure. He ranked the alternatives considering five evaluating criteria to optimize the order quantity. Xia and Wu (2007) applied the AHP model to calculate the performance scores of potential suppliers. The authors then applied the scores as coefficients of one of the four objective functions in a multi-objective mixed integer programming model. The model was developed in order to determine the optimal number of suppliers and to select the best set of suppliers.

Some of the advantages of AHP method are as follows (Chan et al., 2003)

  • The system can be represented in a hierarchical manner to explain the changes in priority and its effect at upper and lower levels.
  • The desired performance of the supplier is characterized by hierarchical selection criteria viz. the management of the suppliers is better if the suppliers performance is evident to the buyer.
  • It utilizes multiple paired comparisons of criteria to rank order alternatives and it is the most exceptional Multi-criterion decision making approach.
  • Efficiently progresses through modular construction and final assembly of modules than those assembled as a whole, this is known as hierarchical assembly of natural systems.
  • Identifies the key elements assisting in making more accurate business decisions and is a structured method which obtains information from target respondents (decision makers or experts).
  • It provides information regarding the structure and function of a system in the lower levels of the hierarchy and gives the outline of the criteria and their purposes in the upper levels. Limitations on the elements in a level are best denoted in the next higher level to ensure they are satisfied.
  • It has stability and flexibility, stability as small changes have small effects and flexible in the sense that the performance is not hampered if there are any additions to a well structured hierarchy

Disadvantages: (Chan et al., 2003)

  • Most of the supplier selection problems do not have a single hierarchy.
  • Utilization of this statistical method is complicated for most of the users and this makes the process unmanageable.
  • It is not cost effective to procure the essential information i.e. due to lack of information /willingness to compare two alternatives with respect to some criterion the supposition of comparability is invalid.
  • To reach an agreement with the team members by reviewing the models is time consuming.
  • The presumption that the relative importance of criteria affects the supplier's performance is definite which cannot effectively take into account the risk and uncertainty in assessment of supplier's potential performance.

Analytic network process (ANP):

Sarkis and Talluri (2000) suggested the use of analytic network process, which was a more sophisticated version of the AHP method. The authors believed that the supplier evaluating factors could influence each other and this interdependency needed to be considered in the process. They applied the ANP process to evaluate and select suppliers in a company manufacturing custom-designed high technology metal-based products, considering organizational factors and strategic performance matrix. The model included seven evaluating criteria namely cost, quality, flexibility, delivery time etc. also considering their interdependencies. Bayazit (2006) implemented an ANP model considering ten evaluating criteria. Some of the important criteria included were on time delivery, quality, flexibility and delivery lead time. He classified the criteria into supplier performance and capabilities clusters and the interdependencies among them were formulated by considering each cluster as a controlling factor for a pair wise comparison matrix.

Demirtas and Ustun (2008) developed an integrated model in which they used ANP to evaluate the performance of potential suppliers considering 14 criteria. The weights were then considered in one of the three objective functions of a multi-objective mixed integer programming model. Similarly the authors integrated the ANP and the GP methods of supplier selection and evaluation in 2009. The only difference to the previous model was that there were four goals in the GP model. Gencer et al. (2007) developed ANP model considering various evaluating criteria. He classified them into three clusters to take into account their interrelationships to evaluate and select the most appropriate supplier.

Some major advantages of ANP process over AHP are that ANP provides with additional insight as most of the real world supplier selection problems have interdependencies among the evaluating criteria. It also incorporates both qualitative as well as quantitative factors which are important in supplier selection. The ANP method can deal with various uncertainties and complexities as it makes use of ratio scales to incorporate a variety of interactions. In spite of the advantages, the ANP method does have a few limitations as it is a very complex method and requires additional effort and time as compared to AHP.

Fuzzy Set Theory:

The fuzzy set theory is used to model uncertainty and imprecision in the supplier selection situation. Fuzzy set systems make use of linguistic rules which are very well suited to describe the behavior of practical problems. In most of the real world applications, fuzzy rules are created by the decision makers with a few input variables. When the number of input variables increases, the possible number of fuzzy rules for a particular system increases exponentially. It is rather difficult for the decision maker to generate a complete set of rules to assess the supplier selection system (Chan et al., 2006).

Chan et al. (2006) presented a hierarchy model based on the fuzzy set theory which could deal with both quantitative and qualitative criteria. The author used linguistic values to assess the ratings and the weights for the evaluation factors. The ratings were arranged in triangular fuzzy numbers. They created a hierarchical structure of the decision problem and applied the model to a high technology manufacturing company to select a suitable supplier to supply material for key components of a new product. Sarkar and Mohapatra (2006) used a fuzzy set method to eliminate the imprecision in a number of subjective characteristics of suppliers. The authors evaluated and selected the suppliers on the basis of performance and capabilities as the two major measures for evaluation. They considered a hypothetical case to exemplify their model by considering a pool of ten suppliers and the goal being to reduce that number and select the best two suppliers.

Kahraman et al. (2003) applied the integrated fuzzy AHP approach to select the most appropriate supplier for the biggest white goods manufacture in Europe to supply the plastic part scroll housing for their new model of aspirators. In this model the decision makers could specify their preferences in terms of linguistic variables regarding the priority of each evaluating criteria. Chan and Kumar (2007) also applied a fuzzy AHP methodology for selection of suppliers. The authors used triangular fuzzy numbers and fuzzy synthetic extent analysis methods to choose the final priorities of different criteria. The authors applied the model to the supplier selection process of a manufacturing company to select the best global supplier for one of their critical parts used in the assembling process. The criteria considered in the model for evaluation were overall cost, quality of product, service performance, supplier profile and risk factors.

Amid et al. (2006) formulated an integrated fuzzy multi-objective linear programming model which took into account the vagueness and imprecision of the input data in order to optimize the order quantity. The author developed an algorithm to solve the model which incorporated three objective functions with different weights. They considered a hypothetical case to select three suppliers for supplying a new product to a market. The purchasing criteria considered for the model were net price, quality, service and capacity. The author also formulated a fuzzy multi-objective mixed integer programming model which was similar to the earlier model but it also took into account the quantity discount. The price discount was directly proportional to the quantities ordered (Amid et al, 2006).

One of the primary advantages of using fuzzy set theory for supplier selection is that it makes use of linguistic variables, which are highly beneficial when the performance values cannot be expresses in terms of means of the numerical values. Thus, taking into consideration the uncertainty and imprecision of the quantitative data gathered by the purchasing company or provided by the supplier. It is beneficial and easier to use linguistic variables instead of numerical values while assessing potential suppliers with respect to criteria and weights. A modified fuzzy set theory is capable of handling both qualitative as well as quantitative data ratings and is flexible in use, which is an added advantage (Chan et al, 2006). Some of the disadvantages of fuzzy set theory are that the analysis is based on the theory and not exploratory data; hence validation of the data may be required. It is a subjective methodology, thus justification for each step is necessary. As the number of variables increase the complexity increases, thus requiring a number of procedures in the sub-systems of the methodology.

Other Methods:

A number of other methodologies exist for the supplier selection problem such as artificial intelligence and expert systems which includes case based reasoning (Choy et al, 2005; 2002; Humphreys et al, 2003) and Bayesian belief networks (Kreng et al, 2003). Multi-criteria decision methods which include outranking methods (DeBoer et al, 1998; Dulmin et al, 2003), judgmental modeling (DaSilva et al., 2002; Naude and Lockett, 1993), interpretive structural modeling (Mandal and Deshmukh, 1994) and categorical methods (Houshyar and Lyth, 1992). Multivariate statistical analysis that incorporates structural equation modeling (Lin et al., 2005; Tracey and Tan, 2001), Factor analysis (Krause et al., 2001; Tracey and Tan, 2001) and confidence interval approach (Muralidharan et al., 2001). Group decision methods (Han and Ahn, 2005; Mandal and Deshmukh, 1994) and multiple integrated methods also exist for supplier selection.

All the methods that are utilized for selections of suppliers have their own advantages and disadvantages. No method can be said to be the perfect method which covers all aspects of the entire selection process. Modifications and improvements can be made to every method in according to the requirements of the decision makers. The selection process can be improved by integrating different techniques in order to negate the limitations of the techniques taken individually. Considering this procedure, the fuzzy integrated AHP model and the DEA integrated ANN model are comparatively the best combination of methods that can be implemented for supplier selection.

Supplier Selection Criteria:

Evolution of supplier selection criteria:

A number of criteria need to be considered for the supplier selection decision making process which makes the selection of suppliers a complicated practice. Since the early 1960's, practitioners and academics have been focusing on the analysis of supplier selection criteria and measurement of supplier performance. Dickson et al, (1966) suggested "From the purchasing literature is fairly easy to abstract a list of at least 50 distinct factors that are presented by various authors as being meaningful to consider in a vendor selection decision"Â?. In his work he carried out a survey to identify the most important criteria required for the selection of suppliers. The author came up with 23 criteria and their relative importance for vendor selection. The following table summarizes the 23 criteria and their level of importance.

Weber et al. (1991) conducted a similar study on the bases of the 23 criteria identified by Dickson (1966). The authors reviewed and classified 74 related articles appearing between 1966 and 1990. Their study provided a clear indication of the issues concerning selection of suppliers. Both the studies indicated net price, quality, delivery and production facility and capacity as the top 4 criteria for supplier evaluation. These two studies were the primary studies done on supplier selection criteria and were the bases of a number of papers in the forthcoming years.

A number of changes at a profound level have taken place in the business environment, including purchasing and procurement since Weber et al.'s work in 1991. The basic definitions of Dickson's 23 criteria have undergone change and expansion and new criteria have emerged due to a substantial growth in business and supply chain needs. Dickson (1966) defined net price as price offered by each vendor including discounts and freight charges. In the development of the net price criteria, the term net price had been replacement by the term cost which includes a number of costs such as fixed cost, inventory costs, ordering costs, supplier costs and costs associated with quality, after-sales and technology (Current and Weber, 1994). The term total cost of ownership has also become important in recent times which include not only the purchasing price but also purchasing related costs (Bhutta et al, 2002).

The delivery criterion was defined by Dickson (1966) as the ability of each vendor to meet specified delivery schedules. The delivery criterion has now been developed to incorporate lead time, cycle time, shipment quantity and quality, delivery capacity etc (Karpak et al, 1999). According to Dickson quality was defined as the ability of each vendor to meet quality specifications consistently. The quality criterion has now been extended to include inspections and certain specifications such as the ISO9001 system (Lee et al, 2003)

In addition to the evolution and development of the basic criteria a number of new criteria have emerged in literature from various authors. Some of the new criteria are flexibility, which includes process and production flexibility, response to change, responsiveness to customer needs (Ghodsypour et al, 2001), flexibility to change the order and order quantity and ability to respond to fluctuating demand (Verma et al, 1998). A product design and development criterion consists of commitment to continuous improvement, product development and improvement, design capabilities and continuous improvement in product and process (Chan et al, 2003). Supplier relationship is another criterion that has gained importance in recent years due to integration of various sections of supply chain. Supplier relationship has two aspects, strategic and tactical. The criterion can be sub divided into 4 sections namely strategic long term relationship, tactical long term relationship, strategic short term relationship and tactical short term relationship. Due to the growth in the businesses, buying firms prefer to integrate the suppliers in their supply chain, thus forming a strategic long term alliance with their suppliers.


A number of factors have contributed towards the development of the aforementioned criteria. The focus of companies and businesses has shifted from mass production in the 1980's and 90's to higher customer satisfaction and optimum quality. Due to this shift in goals supply chain management has gained major focus for both small and large scale industries. The companies are striving for seamless collaboration between the different components of the supply chain. Thus strategic long term relationships and integrated information sharing has become widely important. The ever changing business environment, fluctuation in customer demand and focus on customized product requirements has resulted in reduction of lot sizes and increase in delivery frequencies. Thus, responsiveness to customers and flexibility criteria has gained importance in the supplier selection process. Buyers encourage their suppliers to participate in the design and development process for their products in order to fast track the development cycle of their products. Therefore, product design and supplier integration have emerged as important criteria. A number of other criteria such as financial stability, risk assessment and environmental considerations have also been included in recent literature. Financial stability and risk assessment emerged as important criteria especially due to the growing political, socio-economical and cultural problems encountered while sourcing globally. Environmental consideration developed into an important criterion for global supplier selection due to the increasing concerns of impact of manufacturing practices on the environment.

Application of Existing Supplier Selection Models in the Aerospace Industry

Characteristics of the aerospace industry

The aerospace industry, more precisely aerospace and defence industry, is one of the most dynamic and competitive industries in the world. For many countries, the development and advancement of the aerospace industry represents not only a symbol of highly advanced technology, competence and innovation, but also pride and wealth (Lefebvre and Lefebvre, 1998). High-tech and regulated products, such as civil aircraft, military aircraft, missiles, land and space vehicles, have contributed to some specific characteristics of the industry and its supply chain. Thus, the purpose of the report is to introduce the aerospace industry, based on some relevant data adapted from the American and European official institutions. It consists of three parts: Part 1 will focus on the industry structure. In Part 2, some characteristics of the supply chain of the industry will be represented. Finally, Part 3 will display the condition of the aircraft market associated with demand/supply, and current competitors.

Industry structure

For several decades, the aerospace industry has been seen as a foundation of Western export leadership in terms of manufacturing sectors. It requires high levels of design, engineering and manufacturing expertise, and is a diverse field which comprises several different product sectors. Basically, the aircraft industry can be categorised into two main parts: commercial and government. The overall industry structure and existing competitors are shown, in Figure 4, below.

The commercial sector falls into two categories in terms of the size of aircraft and different target markets: Business General Aircraft (BGA) and Air Transportation Regional (ATR). The smaller aircraft belong to the BGA sector, such as business jets and private aircraft. The target customers of the BGA sector are personal users. As for the ATR sectors, it is known as commercial aviation and the main customers airlines or transportation companies. The major products include large civil aircrafts and cargo aircraft.

Under the government part, defence aircraft and space products are included. For military aircraft, it contains any products related to the military and defence, for example, defence aircraft, guided missiles, military transports. The space vehicles sector mainly includes three types of products: satellite operators (and any other unmanned spacecraft), launchers, and ground systems (for satellite and launch operations or any space system) (Eurospace). Other related products in the sector are planetary exploration systems (e.g. landers, rovers) or space infrastructure elements. According to the classification of Standard and Poor's Market Analysis, space vehicles sectors can also be classified by different scopes of use: commercial, non-defense government/university, and military (Collegian, 2004).

Other related products play an essential role in the smooth functioning of the aerospace industry, such as testers, samplers, and inspectors. These products are responsible for quality control and safety examinations. However, there is another important part that covers the entire aerospace field, providing maintenance, repair and overhaul services (MROs). MRO services are one of the profitable sectors in the industry catering to the aerospace aftermarket (Simons, 2009). MRO firms can be divided into three groups: Original Equipment Manufacturers (OEMs), airlines and independent contractors.

Supply chain

The complexity of aerospace products has created several distinctive characteristics visible in the products and supply chain. One example would be the long product life- time. Due to the complexity of aerospace products, the manufacturing process is inevitably prolonged. For example, it can take around 18 months to manufacture a commercial aircraft. Thus, under such circumstances, the cost of work-in-process inventories is somewhat higher than in other industries. In addition, given the long product lifetimes, a largely fixed network of tiers of suppliers exists in the aerospace industry, and new entrants might find it very difficult to enter the industry (Humphreys, 2000).

Barriers to entry in the aerospace industry are very high due to the high capital investment required. The high capital commitments is not only in designing and producing products, companies also need to invest a lot in processes and quality management systems in order to meet the stringent regulatory requirements. Another factor which might explain the high entry barriers is the specific characteristics of aerospace products "" high value and low volumes. The dominant characteristic of the aerospace products is that the value of each component is high and each type of products is required in low volume (Simons, 2009). To make aerospace products in small quantities does put manufacturers under pressure to deliver a range of sophisticated products. Smaller quantities and complex products translate into more frequent product line changeovers which might result in errors and higher costs. For any potential entrant, it could be a factor that raises the entry barrier higher.

On the other hand, engineering tolerances in the aerospace industry are extremely small, since great precision and high quality are required in aviation products (Kronemer and Henneberger, 1993). For example, in other manufacturing industries, engineering tolerances might allow fitting errors of one-eighth of an inch or more, but the very limited error tolerances in the high-performance aerospace industry might not allow fitting errors to exceed one-thousandth of an inch (Kronemer and Henneberger, 1993).

Although the plant size of a typical aerospace manufacturing unit and aircraft belonging to the high technology product category is hugh, the assembly tasks are not all completed by high-tech production techniques or automotive machines (Kronemer and Henneberger, 1993). Instead, the assembly process is moderately labour intensive. Two reasons account for this. First of all, most buyers request customised products which significantly limit the possibility for extensive automation (Kronemer and Henneberger, 1993). Secondly, the unit volumes of production are much lower than most manufacturing industries (Kronemer and Henneberger, 1993). In sum, its production process type is closer to job shop or batch production.

The aerospace industry is regarded as one of the cutting-edge high-technology sectors in the world. It complex manufacturing process differs from other manufacturing industries which needs many suppliers to offer support capability providing the ability to develop advanced product technologies. For example, there are at least 300 suppliers delivering about 1,000 to 2,000 components to build an aircraft engines (Simons, 2009). Corresponding to the advanced technology requirement, the industry has a highly research-intensive sector. Research budgets absorb a considerable share of the industry's value added (Vekeman, 2006). Generally, when comparing the share of R&D expenditures of the aerospace industry in the value-added chain and the share of R&D expense of other manufacturing industries, it shows that the aerospace industry is more R&D-intensive than others high-technology industries (OECD, 2004). For example, in France, the industry's value-added allocation for R&D expenses is 28%, while for other manufacturing industries it is only 7%; 17% of value-added expense in the UK is devoted to R&D, 12% more than the 5% average of manufacturing industry as a whole (Vekeman, 2006).

Concerning these distinctive characteristics, supplier selection for the major aircraft companies is a vital step in sourcing externally. Generally, in order to serve the aerospace industry, suppliers need to meet several requirements. Craig (2009) has highlighted the following three requirements: (1) implementing information systems that provide for detailed traceability of components and raw materials, for example, the items on suppliers' purchase orders are required to be compliant with the Berry Amendment which asks for the documentation to ensure the traceability of any source of supply (Simons, 2009); (2) products need to be tested to meet certain certification requirements, for example, the Federal Aviation Administration (FAA), European Aviation Safety Agency (EASA) and the Civil Aviation Administration of China (CAAC); (3) quality management systems which can capture and track inspections and certification testing. The widely adopted and standardized quality management system for the aerospace industry is known as AS9100. Furthermore, it is apparent that technological superiority would be one of the important factors for evaluating suppliers from a design perspective (Simons, 2009); and significant investment R&D could help a subcontractor in his candidacy on the supplier selection process in the aerospace industry.

Lefebvre and Lefebvre (1998) focused their attention on five performance dimensions in aircraft manufacturing that are considered in the subcontractor selection process. Firstly, since a high quality requirement is implemented throughout the whole supply chain in the aerospace industry, quality is an essential requirement and the highest quality standard must be met. Second, subcontractors should have the ability to offer high quality customer service. For example, Airbus might directly ask their suppliers to provide technical services to the final customers, usually airlines. Thirdly, although aerospace products have longer lead times than other manufacturing industries, the ability to shorten lead times is still required from subcontractors. Fourth, flexibility is clearly essential to keep long-term relationships intact with prime contractors. As the industry frequently requires high-variety and customised goods, subcontractors would need to maintain high flexibility to handle customer requirements and adapt to any special conditions. Finally, cost is undoubtedly one of the major considerations for evaluating subcontractors. In fact, it is believed that competition will inevitably arise among the subcontractors in an industry due to the recent increasing numbers of subcontractors in developing countries. Thus, a lower-cost competitive advantage of subcontractors in the developing countries can be one thing attracting OEM companies' attentions. However, there are still some concerns over whether subcontractors in low-cost countries can meet the technology requirements and offer sufficient product support for their customers, and whether the general infrastructure in developing countries can provide efficient logistics systems and sufficient information technology infrastructure (Simons, 2009).

In recent years, most the US aircraft companies have moved towards the systems integration which have been used by its European competitors ""Airbus has done for decades (ITA, 2009). Apart from the traditional manufacturing process, the adoption of a systems integration approach relies to a great extent on external suppliers' participation in the process of product development. With the drift towards systems integration, design and engineering tasks for aircraft development will be distributed across an international network to risk-sharing partners (Pritchard and MacPherson, 2007). One of the advantages is that this approach significantly reduces the initial capital investment into new launch projects compared to the self-funded launch programmes that have traditionally been adopted in this industry in the past (Pritchard and MacPherson, 2007). At the same time, the supply chain of the industry is reorganised and rationalised (Niosi and Zhegu, 2005). Therefore, original manufacturers (e.g. Airbus, Boeing, Bombardier, Leer and Lockheed) can concentrate on their core competencies through increasing their outsourcing percentages. Suppliers in this industry would be required to share risks in engineering, testing and manufacturing. Following this trend, the extra vital factors for selecting suitable external suppliers nowadays would include potential suppliers being successful risk-sharing partners with financial and technical capabilities and a sufficient engineering workforce (Pritchard and MacPherson, 2007).

The aircraft market

The aircraft market is rapidly changing and highly competitive. Market demand in different sectors is varies with different aspects: in the military sector, the factors driving demand are the current political climate, military strategies, and economic factors (Kronemer and Henneberger, 1993). Defence spending is one driver in military sector sales, while the Russian collapse and a decreased in large-scale war could be major reasons for reduced defence spending (Simons, 2009). As for the space vehicle sector, government funding for space exploration in each country could be an important factor influencing market demand. On the other hand, the demand for commercial aircraft is more susceptible to market trends. According to Kronemer and Henneberger (1993), although the demand for new civil aircraft clearly depends on the circumstances of air transportation, such as passenger and cargo shipper demand, wide swings in demand still exists in the market due to the imbalance between passenger demand and available aeroplane seats. The number of passengers increases at a particular rate, while the number of aircraft seats remains fixed in a given time period. As a result, to meet market demand, most airlines place orders which create more seats than current demand from passengers in consideration of the long lead times required for the delivery of aviation products. Then, substantial backlogs are generated in aircraft manufacturing which causes delivery dates to be pushed back to years later. In the end, widely fluctuating demand for commercial aircraft is produced.

On the one hand, the industry may respond to changes slowly under general economic conditions (Kronemer and Henneberger, 1993). For example, despite the tough economic situation in the USA in 2008, total sales of the US aerospace industry have not been seriously affected and achieved a 2.1 % increase in sales (AIA 2009). One of the reasons might be that the great number of orders during the last few years has created significant backlogs for aircraft manufacturing companies (AIA Research Centre, 2009). On the other hand, the industry is also shaped by sudden and often unpredictable volatility in demand (Kronemer and Henneberger, 1993). For example, the attacks on the US on 11 September 2001 have had a severe impact on the aerospace industry. The 9/11 terrorist attacks have caused a decrease in airline passenger numbers which led airline companies to cut capacity and routes. Thus, the STR sector has suffered a serious drop in demand as most airlines have tempered their aircraft replacement and expansion plans, extending delivery dates. However, in contrast to the STR sector, sales in the BGA sector grew significantly owing to an increase in demand for business jets and private aircrafts. One reason accounting for this is that as flight security became a major issue after the terrorist attacks, more people preferred buying a private aircraft to taking commercial flights (Simons, 2009). In addition, the value proposition for investing in a private jet changed at that time (Simons, 2009). On the other hand, the boost in demand from the US government for military aircrafts has increased the sales of military aircrafts. But at the same time, the attack and the subsequent wars in Iraq and Afghanistan have caused downward pressure on the military and defence sectors because of the US government's restriction on exports of military products to other countries; it thus balances the demand for military aircrafts (Collegian, 2004).Overall, the market concentration level in the aerospace industry is high. For each sector (BGA, ATR, military aircraft, and space vehicles) there are only a few competitors. In the BGA market, it is lead by the following major companies: Airbus, Boeing, Bombardier and Embraer. As for the ATR market, Dassault, Gulfstream and Leer are three leading companies (Simons, 2009). Then, there are four US leading companies in the space vehicle sector: Boeing's satellite systems division, Lockheed Martin's space systems segment, Loral Space & Communications Ltd.'s Space Systems/Loral (SS/L), and Orbital Science Corp. Contrary to the civil aircraft and space vehicle sector that have many competitors, in the military aircraft sector, this is still dominated by a handful of major OEM companies, such as Lockheed Martin Corp., the Boeing Co., Northrop Grumman Corp., and BAE Systems (AIA Research Centre). Moreover, there are some aftermarket players which play a vital role in providing aftermarket products and services: General Electric (GE), United Technologies, Honeywell's aerospace unit, Goodrich's aerospace segment, and Boeing's aviation support services division (Collegian, 2004).

Although market concentration in the industry is high, rivalry among firms in the global aerospace industry is however strong (Niosi and Zhegu, 2005). One of the reasons explaining this phenomenon is that an aircraft contract, usually associated with billions of dollars, is often at stake (Kronemer and Henneberger, 1993). In addition, the global aerospace market contains significant market uncertainty deriving from fiercely high-tech competition, unpredictable market demand, timeliness requirements and strong cyclical evolutions. Thus, it involves highly technical and financial risks, and increases market competition in the aerospace industry (Hollanders et al., 2008). Furthermore, the positive relationship between R&D intensity and competition in the aerospace sector also shows that the highly R&D-intensive trend might build up the intensity of competition pressures in the industry (Hollanders et al., 2008).

According to a market analysis report from Datamonitor (2009), the US remains the largest aerospace market accounting for 51.9% of the market value in 2008. The US aerospace industry had total sales reaching $204bn. in 2008, followed by the EU, Canada and Japan (AIA Research Centre, 2009). Also, a report conducted by RNCOS (2009) indicated that developing countries such as China, Mexico, India and Brazil are expected to emerge as big marketplaces for aerospace products due to their increasing demand in air traffic. However, there is insufficient evidence to show whether these emerging markets will dominate the growth of the global aerospace industry in the future (Simons, 2009).


AEROSPACE INDUSTRIES ASSOCIATION (AIA). 2009. [online]. [Accessed 22 Jul 2009]. Available form World Wide Web:<http://www.aia-aerospace.org/industry_information/reports_white_papers/>

AIA RESEARCH CENTRE. 2009. 2008 Year End Review and 2009 Forecast "" An Analysis. [online]. [Accessed 22 Jul 2009]. Available form World Wide Web: <http://www.aia-aerospace.org/assets/2008-year-end-report.pdf,>

AMID, A., Ghodsypour, S.H., O'Brien, C. 2006. Fuzzy multiobjective linear model for supplier selection in a supply chain.. International Journal of Production Economics. 104 (2), p.394""407.

BAYAZIT, O. 2006. Use of analytic network process in vendor selection decisions. Benchmarking: An International Journal. 13 (5), p.566""579.

BHUTTA, K. S. and Huq, F. 2002. Supplier selection problem: a comparison of total cost of ownership and analytical hierarchy process approach. Supply Chain Management: An International Journal. 7(3), pp.126-135.

BRAGLIA M., Petroni A. 2000. A Quality Assurance - Oriented methodology for handling trade-offs in supplier selection. International Journal of Physical Distribution and Logistics Management. 30(2), pp.96-111.

CHAN F.T.S., Chan H.K. 2004. Development of Supplier selection model- A case study in the advanced technology industry. Journal of Engineering Manufacture. 218(2), pp.1807-1824.

CHAN, F.T.S., Kumar, N.. 2007. Global supplier development considering risk factors using fuzzy extended AHP-based approach.. OMEGA "" International Journal of Management Science. 35 (4), p.417""431.

CHARLES A. WEBER, John R. Current and W.C. Benton. 1991. Vendor selection criteria and methods. European Journal of Operational Research. 50, pp.2-18.

CHEN, C.T., Lin, C.T., Huang, S.F. 2006. A fuzzy approach for supplier evaluation and selection in supply chain management.. International Journal of Production Economics. 102 (2), p.289""301.

CHOY, K. L., Lee, W. B. and Lo, V. 2002. Development of a case based intelligent customer-supplier relationship management system. Expert Systems with Applications. 23(3), pp.281-297.

CHOY, K. L., Lee, W. B., Lau, H. C. W. and Choy, L. C. 2005. A knowledge-based supplier intelligence retrieval system for outsource manufacturing. Knowledge-Based Systems. 18(1), pp.1-17.

COLLEGIAN, B. 2004. INDUSTRY REPORT: Aerospace and Defence. [online]. [Accessed 31 Jul 2009]. Available form World Wide Web: <http://findarticles.com/p/articles/mi_qa3628/is_200410/ai_n9471414/?tag=untaggd>

CRAIG, A. Aerospace needs to leverage automotive industry supply chain to meet customer demands. [online]. [Accessed 31 Jul 2009]. Available form World Wide Web: <http://www.onlineamd.com/article.cfm?id=1912>

CURRENT, J. and Weber, C. 1994. Application of facility location modeling constructs to vendor selection problems. European Journal of Operation Research. 76, pp.387-392.

DA SILVA, R.V., Davies, G. and Naude, P. 2002. Assessing customer orientation in the context of buyer/supplier relationships using judgmental modelling. Industrial Marketing Management. 31(3), pp.241-252.

DATAMONITOR. 2009. Aerospace and Defense: Global Industry Guide. [online]. [Accessed 27 Jul 2009]. Available form World Wide Web:

DE BOER, L., van der Wegen, L. and Telgen, J. 1998. Outranking methods in support of supplier selection. European Journal of Purchasing & Supply Management. 4, pp.109-118.

DEMIRTAS, E.A., 2009. Analytic network process and multi-period goal programming integration in purchasing decisions.. Computer and Industrial Engineering. 56 (2), p.677""690.

DEMIRTAS, E.A., 2008. An integrated multi-objective decision making process for supplier selection and order allocation.. OMEGA "" International Journal of Management Science. 36 (1), p.76""90.

DICKSON, G.W. 1966. An analysis of vendor selection systems and decisions. Journal of Purchasing. 2(1), pp.5-17.

DULMIN, R. and Mininno, V. 2003. Supplier selection using a multi-criteria decision aid method. Journal of Purchasing and Supply Management. 9(4), pp.177-187.

Eurospace: Eurospace 2008 Facts and Figures survey report. [online]. [Accessed 22 Jul 2009]. Available form World Wide Web: <http://pagesperso-orange.fr/eurospace/fandfreport2008web.pdf>

F.T.S., Chan. 2003. Interactive selection model for supply selection process: An analytical hierarchy process approach. International Journal of Production Research. 41(15), pp.3549-3579.

GENCER, C., D.. 2007. Analytic network process in supplier selection: A case study in an electronic firm.. Applied Mathematical Modeling. 31(11), p.2475""2486.

GHODSYPOUR S.H., O'Brien C. International Journal Of Production Economics. The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria, and capacity constraint. 2001. 73(1), pp.15-27.

HAN, C. H. and Ahn, B. S. 2005. Interactive group decision-making procedure using weak strength of preference. Journal of the Operational Research Society. 56(10), pp.1204-1212.

HOLLANDERS, H. 2008. Sectoral innovation systems in Europe: the case of the aerospace sector. [online]. [Accessed 07 Aug 2009]. Available form World Wide Web: <http://www.europe-innova.org/docs/SIW_SR_Aerospace_20080509.pdf>

HONG G. H., Park S. C., Jang D. S., Rho H. M. 2005. An effective suplier selection method for constructing a competitive supply - relationship. Expert Systems with Applications. 28(4), pp.629-639.

HOU J., Su D.. 2007. EJB - MVC oriented supply selection system for mass customization. Journal of Manufacturing Technology Management. 105(2), pp.510-523.

HOUSHYAR, A. and Lyth, D. 1992. A systematic supplier selection procedure. Computers and Industrial Engineering. 23(4), pp.173-176.

HUMPHREYS, P.K. 2000. A decision support framework for strategic purchasing. Journal of Materials Processing Technology. 107, pp.353-362.

HUMPHREYS, P., McIvor, R. and Chan, F. 2003. Using case-based reasoning to evaluate supplier environmental management performance. Expert Systems with Applications. 25(2), pp.141-153.

I, Ng W. 2008. An efficient and simple model for mutilple criteria supplier selection problem. European Journal Of Operational Research. 186(3), pp.1059-1067.

J., Seydal. 2006. Data envelopement analysis for decision support. Industrial Management And Data Systems. 106(1), pp.81-95.

KAHRAMAN, C., Cebeci, U., Ulukan, Z. 2003. Multi-criteria supplier selection using fuzzy AHP.. Logistics Information Management. 16 (6), p.382""394.

KARPAK B., Kamcu E., Kasunganti R.R.. 2001. Purchasing Materials in Supply chain : managing a multiobjective task. European Journal of Purchasing Supply Management. 7(3), pp.209-216.

KARPAK, B., Kasuganti, R. R. and Kumcn, E. 1999. Multi-objective decision making in supplier selection: an application of visual interactive goal programming. The Journal of Applied Business Research. 15(2), pp.57-71.

KRAUSE, D. R., Pagell, M. and Curkovic, S. 2001. Toward a measure of competitive priorities for purchasing. Journal of Operations Management. 19(4), pp.497-512.

KRENG, V. B. and Chang, C.-H. 2003. Bayesian network based multiagent system--application in e-marketplace. Computers & Mathematics with Applications. 46(2), pp.429-444.

KRONEMER, A and Henneberger, J.E. June 1993. Productivity in aircraft manufacturing. Monthly Labour Review.

L., Saaty T. 1980. The Analytic Hierarchy Process. NY: McGraw- Hill.

LEE, M. S., Lee, Y. H. and Jeong, C. S. 2003. A high-quality-supplier selection model for supply chain management and ISO 9001 system. Production Planning & Control. 14(3), pp.225-232.

LEFEBVRE, E. and Lefebvre, L. 1998. Global strategic benchmark critical capabilities and performance of aerospace subcontractors. Technovation. 18(4), pp.223-234.

LI CC, Fun Y.P. 1997. A new measure for supply performance evaluation. IIE Transactions. 29(1), pp.753-758.

LIN, C., Chow, W. S., Madu, C. N., Kuei, C.-H. and Yu, P. P. 2005. A structural equation model of supply chain quality management and organizational performance. International Journal of Production Economics. 96(3), pp.355-365.

LIU F.H.F., Hai H.L. 2005. The voting analytic hierarchy process method for selecting suppliers. International Journal of Production Economics. 97(3), pp.308-317.

LIU J., Ding F.Y., Lal V. 2000. Using Data envelopment analysis to compare suppliers for supplier selection and performance improvement. Supply Chain Management : An International Journal. 5(3), pp.143-150.

LUITZEN DE BOER, Eva Labro, Piearngela Morlacchi. 2001. A Review of methods supporting Supplier Selection. European Journal of Purchasing And Supply Management. 7, pp.75-89.

MANDAL, A. and Deshmukh, S. G. 1994. Vendor Selection Using Interpretive Structural Modelling (ISM). International Journal of Operations and Production Management. 14(6), pp.52-59.

MENDOZA A., Ventura J.A. 2008. An effective method to supplier selection an order quantity allocation. International Journal of Business and Systems Research. 2(1), pp.1-15.

MURALIDHARAN, C., Anantharaman, N. and Deshmukh, S. G. 2001. Vendor rating in purchasing scenario: a confidence interval approach. International Journal of Operations and Production Management. 21(10), pp.1306-1325.

MURLIDHARAN C., Ananthraman N., Deshmukh S.G. 2002. A multi-criteria group decision-making model for supply rating. Journal of Supply Chain Management. 38(4), pp.22-33.

NARSIMHAN R R., Talluri S., Mendez D. 2001. Supplier evaluation and rationalization via data envelopment analysis : and emperical examination. Journal Of Supply Chain Management. 37(3), pp.28-37.

NAUDE, P. and Lockett, G. 1999. Market Analysis via Judgemental Modelling: An Application in the UK Chemical Industry. European Journal of Marketing. 27(3), pp.5-22.

NIOSI, J. and Zhegu,M. 2005. Aerospace Clusters: Local or Global Knowledge Spillovers? Industry and Innovation. 12(1), pp.1-25.

PRITCHARD, D. and MacPherson, A. Strategic Destruction of the North American and European Commercial Aircraft Industry: Implications of the System Integration Business Model. Canada-United States Trade Centre Occasional Paper. 35.

R., Ramanathan. 2007. Supplier selection problem: integrating DEA with approaches of total cost of ownership and AHP. Supply Chain Management : An International Journal. 12(4), pp.258-261.

RNCOS. 2009. Aerospace Industry Forecast to 2013. [online]. [Accessed 02 Aug 2009]. Available form World Wide Web: <http://www.automobilenews.net/Report/IM176.htm>

ROSS A., Buffa F.P., Droge C.,Carrington D. 2006. Supplier evaluation in a dynamic relationship : an Action research approach. Journal of Business Logistics. 27(2), pp.75-102.

S., Percin. 2006. An application of the integrated AHP-PGP model in supplier selection. Measuring Business Excellence. 10(4), pp.34-49.

SARKAR, A., Mohapatra, P.K.J.. 2006. Evaluation of supplier capability and performance: A method for supply base reduction.. Journal of Purchasing and Supply Management. 12 (3), p.148""163.

SARKIS, J., Talluri, S. 2002. A model for strategic supplier selection.. Journal of Supply Chain Management. 38(1), pp.18-28.

SEVKLI M., Koh S.C.L.,Zain S., Demirbag M., Tatoglu E. 2007. An application of data envelopment analytic hierarchy process for supplier selection: a case study of BEKO in Turkey. International Journal Of Production Research. 45(9), pp.1973-2003.

SIMONS, B. 2009. Interview with Brian Simons, Senior Director of Transportation, Logistics, and Supply Chain Planning for Honeywell on 11/08/2009.

TALLURI S, Narsimha R. 2003. Wendor evaluation with performance variability: A max- men approach. European Journal Of Operational Research. 146(3), pp.543-552.

TALLURI S., Sarkis J. 200. A Model for performance monitoring of suppliers. International Journal of Production Research. 40(16), pp.4257-4269.

TALURI S., Baker R.C

To export a reference to this article please select a referencing stye below:

Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.

Request Removal

If you are the original writer of this dissertation and no longer wish to have the dissertation published on the UK Essays website then please click on the link below to request removal:

More from UK Essays

Get help with your dissertation
Find out more