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Multi-attribute global supply chain outsourcing risk management: a sensitivity analysis approach.


Some of the challenges and issues that the pharmaceutical firms have been facing include shorter product life cycles, increasing expenditure on sales and marketing, poor financial performance, damaged reputation, expiring patents, shrinking future product pipelines, reduction in FDA approval times for new drugs and changing requirements, impact of generic drugs, increased competition, among others (PriceWaterHouseCoopers, 2007; Nelson, 2004; CFO Research Services, 2004; Dimasi, Hansen, & Grabowski, 2003; IBM, 2003; Grabowski & Vernon, 2000). To reverse this trend and improve time to market and market share, gain access to innovative ideas, maximize the use of existing resources, contain expenses, restructure distribution networks, spread risks, focus on issues imperative to survival, gain competitive advantage, achieve higher customer satisfaction, and ensure future growth (Dhar & Rajan, 2007; CFO Research Services, 2004; Srivastava, 2002; Piachaud, 2002; Datamonitor, 2006; Person & Virum, 2001; Graig, 2003) pharmaceutical firms are turning to global supply chain logistics outsourcing. Arguably, global supply chain outsourcing is increasingly considered as an important integral part of successful business operation by the pharmaceutical firms.

Global Outsourcing strategy enables organizations to focus their limited resources on core activities thus helping them to benefit from the specialized expertise of the third-party providers as well as improving their financial position (Christopher, 1998; Yoon & Naadimuthu, 1994). Although global supply chain outsourcing strategy has enabled the pharmaceutical firms to concentrate on their core competencies and gain competitive advantage, the same strategy has significantly increased their risk profiles, placing profit margins, and brand equity in serious jeopardy. Indeed, "while freeing companies to concentrate their efforts on areas of greatest value-add, outsourcing also brings with it new risks and problems, which nullify the competitive advantages that it creates" (Kam, n.d). Lindholm and Suomala (2004) assert that the goal of outsourcing is to attain optimal performance within a firm. Christopher (1999) noted that it is only those firms willing to leverage the collective strengths and competencies of network partners that will be able to achieve a faster responsiveness to changing global marketplace requirements. Piachaud (2002) points out that successful firms tend to leverage their resources by relying on the wealth of expertise provided by specialist external sources. Arguably, as the pharmaceutical supply chain outsourcing continues unabated, risks associated with it are growing as well. However, through appropriate risk management, outsourcing can help firms to expand their R&D pipelines and provide a greater opportunity for a drug to ultimately reach launch phase (PricewaterhouseCoopers, 2006).

This paper utilizes a gradient SA for pharmaceutical global supply chain risk management in which the goal being pursued has multiple, often conflicting attributes. The AHP developed by Saaty (1980) is used to perform SA on the weights of the decision criteria or objectives and the performance values of the alternative risk response options expressed in terms of the decision objectives. AHP is a multi-attribute decision making process which enables decision makers set priorities and deliver the best decision when both quantitative and qualitative aspects of a decision must be considered. AHP encompasses three basic functions, including structuring complexity, measuring on a ration scale, and synthesizing. It is a powerful operational research methodology useful in structuring complex multi-criterion problems or decisions in many fields such as supply chain management, marketing, engineering, education, and economics. Merits associated with AHP include its reliance on easily derived expert judgment data, ability to reconcile differences (inconsistencies) in expert judgments and perceptions, and the existence of Expert Choice Software that implements the AHP (Calantone, R.A., Di Benedetto, R.A., & Meloche, M. (1989) SA represents one of the methods to risk evaluation when values of coefficients are subject to variation (Rappaport, 1967). AHP can allow SA of the various criteria, allowing decision makers to investigate the effects of making different assumptions about the relative importance of different criteria Dunlop, M., Poldy, F., & Turner, G.M. (2004). SA is used to examine the sensitivity of the alternatives to changes in the objectives' priorities. Erkut and Tarimcilar (1991) assert that SA can be a relevant tool in eliminating alternatives, enhancing a group decision process and/or in providing actionable information as to the robustness of a decision. According to Rapport (1967), SA "tests the responsiveness of model results to possible variations in parameter values, and thereby offers valuable information for appraising the relative risk among alternative course of action." For that to happen, Insua (1990) notes SA must provide a sensitivity measure and recommend means to improve on the current condition, help to identify critical judgments, utilize only available known information, and be palatable to implement. Indeed, SA of supply chain outsourcing risks can be beneficial to C-level executives by indicating which alternative risk response options have the most impact on the pharmaceutical outsourcing risks.

The remainder of this paper is structured as follows. Section 2 briefly discusses sources of pharmaceutical supply chain outsourcing risk. SA is discussed in section 2. The research methodology, data collection and analysis are presented in section 3. The results are discussed in section 4. Finally, the conclusions and implications are presented in section 5.


Risk is quintessential part and parcel of supply chain operations. Rappaport (1967) once said that "in a society characterized by change, uncertainty [and risk are well] ... established and accepted fact of life. Notwithstanding the difficulties, the decision maker is expected to attain satisfactory results as measured by the goals of the organization." Sources of pharmaceutical global supply chain outsourcing risk can be internal as well as external. Harland, Brenchley, & Walker (2003) assert that in the past, when firms manufactured in-house, sourced locally and sold direct to the customer, risk was less diffused and easier to manage. With the advent of increased product/service complexity and outsourcing of supply networks across international borders, risk is increasing and the location of risk has shifted through complex changing supply networks. Some of the identified pharmaceutical outsourcing risks include protecting IP through patients, FDA regulations, maintaining quality, confidentiality issues, technical knowledge, resource availability, capacity, information sharing and management issues (Dhar & Rajan, 2007; Hosseiny, 2004).

Schiff (2007) attest that the two primary sources of risk in global pharmaceutical sourcing are business and technical risks. However, the pharmaceutical outsourcing risks considered in this paper include regulatory risk, business risk, technical risk, and intellectual property risk. Although risk management is an issue of great importance, it has received limited attention from a number of pharmaceutical firms engaged in global supply chain outsourcing relationships. Arguably, given the array of risks associated with global outsourcing, it is imperative that the pharmaceutical firms implement risk management. Global supply chain logistics outsourcing risk management entails the process of anticipating and controlling portfolio of risks through risk identification (implies what, where, when and how), risk analysis (implies quantification or measuring the effect of risk), risk evaluation (i.e., prioritization of identified risk), and risk treatment (i.e., instituting risk mitigation strategies to manage risk).


SA is a means of investigating the impact of reasonable changes in base-case assumptions Eschenbach (1992) or an approach which allows decision makers to explore the impact on the optimal decision(s) of potential changes in any of the problem variables True-man (1974). Some of the uses of SA include determining the impact on the ranking of alternatives of changes in various model assumptions, making better decisions, deciding which data estimates should be refined before making a decision, and enabling management to focus attention on the most critical elements during decision implementation (Kirkwood 1997, Eschenbach, 1992). Because of SA imperative in decision making, it has been applied in such areas as pharmaceuticals, medicine, civil engineering, political science and computer science (Steenland & Greenland, 2004, Blake, J. T., Reibman, A. L., & Trivedi, K. S. 1988, Castillo, E., Minguez, R., & Castillo, C. (2006). Rappaport (1967) assert that in the face of risk and uncertainty, the most recurring questions to be answered by organizations are of the form, "what if? "What if analysis or the so called SA is a technique used to assess how possible changes in parameter values impact model outputs and helps to facilitate a better understanding of risk (Rappaport, 1967). Essentially, SA "tests the responsiveness of model results to possible variations in parameter values, and thereby offers valuable information for appraising the relative risk among alternative courses of action. Degarmo, E.P, Sullivan, W.G., Bontadelli, J.A., & Wicks, E.M. (1997). (1997) point out that SA is an important decision making non-probabilistic method that can provide information regarding the potential effect of uncertainties in estimated values.

Rappaport (1967) contend that from the humble beginning of business development that decision makers have leveraged SA tests for evaluating relative risk of alternative courses of action. Indeed, the imperative of SA "as an integral part of risk analysis varies with the degree of uncertainty [because] the less certainty there is about values of model parameters, the more important it is to study the results of possible variations among parameter values (Rappaport, 1967). It is worth noting that in discussing the imperatives of subjecting models to SA, Arnoff and Netzorg (1965) emphasized that "the use of operations research is especially important and advantageous in that ... one can assess the sensitivity (response) of the system to a wide variety of conditions--without requiring either the time, expense, or risks associated with experimenting with system itself. [Thus,] hidden relationship can be brought to light and brought to bear upon decisions and control of activity." Triantaphyllou and Sanchez (1997) argue that "often data in multi-criteria decision making (MCDM) problems are imprecise and changeable." As a result, an essential step in many uses of MCDM such as AHP is to execute an SA on the weights of the decision objectives and performance values of the alternative options expressed in terms of the decision objectives (Triantaphyllou & Sanchez, 1997). Samson (1998) suggests that SA should be an important part of decision making process thinking in real time. Wallace (2000) asserts that SA is utilized to facilitate decision making under uncertainty by way of parametric linear programming.


Evaluation and management of pharmaceutical supply chain outsourcing risk represents a typical multi-criteria decision making that entails multiple criteria that can be both qualitative and quantitative. AHP is used to model risk in the global supply chain outsourcing. AHP is selected because it permits decision-makers to model a complex problem in a hierarchical structure showing the relationships of the overall goal, objectives, and alternatives. Although the positive attributes associated with AHP has been widely reported in the literature, there has been a small number of descending opinions (e.g., Belton & Gear, 1986; Dyer & Wendel, 1985). However, because of its usefulness, AHP has been widely used by in research. For example, it has been used in pharmaceutical supply chain (Enyinda, 2008, Enyinda, C. I. Mbah, H.N., & Ogbuehi, O. A. (2010), and pharmaceutical marketing and management (Ross & Nydick, 1994, Enyinda, C.I., Briggs, C., & Backhar, K. (2009). The hierarchy structure for managing risk in global pharmaceutical supply outsourcing is composed of three levels as depicted in Figure 1. The top level contains the overall goal of the problem, the middle level houses the multiple criteria that define the decision alternatives, and the lower level contains competing alternative cause of actions.


Establishment of Pairwise Compariso n Matrix A

Assuming [C.sub.1], [C.sub.2], [C.sub.3], ... [C.sub.n] to be the set of elements and a, representing a quantified opinion or judgment on a pair of elements [C.sub.i], [C.sub.j]. The relative importance of two elements [C.sub.i], [C.sub.j] is assessed using a preference scale on an integer-valued 1-9 developed by Saaty (2000) for pairwise comparisons. According to Saaty, a value of 1 between two criteria indicates that both equally influence the affected node, while a value of 9 indicates that the influence of one criterion is extremely more important than the other. It allows the transformation of qualitative judgments and/or intangible attributes into preference weights (level of importance) or numerical values. The pairwise comparisons are accomplished in terms of which element dominates or influences the order. AHP is then used to quantify these opinions that can be represented in n-by-n matrix as follows:


If [c.sub.i], is judged to be of equal importance as [c.sub.j], then ([a.sub.ij]) = 1

If [c.sub.i], is judged to be more important than [c.sub.j], then ([a.sub.ij]) > 1

If [c.sub.i], is judged to be less important than [c.sub.j], then ([a.sub.ij]) < 1

([a.sub.ij]) = 1/[a.sub.ji],(i, j, = 1, 2, 3, ..., n), [a.sub.ij] [not equal to] 0.

Where matrix A represents a reciprocal matrix, [a.sub.ij] is the inverse of the entry [a.sub.kj] which indicates the relative importance of [C.sub.i] compared with attribute [C.sub.j]. As an example, [a.sub.12] = 3 indicates that [C.sub.1] is 3 times as important as [C.sub.2]. In matrix A, it becomes the case of assigning the n elements [C.sub.1], [C.sub.2], [C.sub.3], ... [C.sub.n] a set of numerical weights [W.sub.1], [W.sub.2], [W.sub.3], ... [W.sub.n], that represents the recorded experts' judgments. If A is a consistency matrix, the links between weights [W.sub.i] and judgments [a.sub.ij[ are given by [W.sub.i]/ [W.sub.j] = [a.sub.ij] (for i, j = 1, 2, 3, ..., n).

Eigenvalue and Eigenvector

Saaty (1990) recommended that the maximum eigenvalue, [[lambda].sub.max], can be determined as

[[lambda].sub.max]= [n.summation over (j=l)][a.sub.ij] [W.sub.j]/[W.sub.i]. (2)

Where [[lambda].sub.max] is the principal or maximum eigenvalue of positive real values in judgment matrix, [W.sub.j] is the weight of [] factor, and [W.sub.i] is the weight of [] factor. If A represents consistency matrix, eigenvector X can be determined as

(A - [[lambda].sub.max] I)X = 0 (3)

Consistency Test

Both AHP and Expert Choice Software does not impose on the pharmaceutical firms to be perfectly consistent, rather a consistency test is performed to examine the extent of consistency as well as each judgment once the priorities are determined. Saaty (1990) recommended using consistency index (CI) and consistency ration (CR) to check for the consistency associated with the comparison matrix. A matrix is assumed to be consistent if and only if [a.sub.ij] * [a.sub.jk] = [a.sub.jk] [for all] [i.sub.jk] (for all i, j, and k). When a positive reciprocal matrix of order n is consistent, the principal eigenvalue possesses the value n. Conversely, when it is inconsistent, the principal eigenvalue is greater than n and its difference will serve as a measure of CI. Therefore, to ascertain that the priority of elements is consistent, the maximum eigenvector or relative weights/[[lambda].sub.max] can be determined. Specifically, CI for each matrix order n is determined by using (3):

CI = ([[lambda].sub.max] - n)/n - 1 (4)

Where n is the matrix size or the number of items that are being compared in the matrix. Based on (4), the consistency ratio (CR) can be determined as:

CR = CI/RI = [([[lambda].sub.max] - n)/n - 1]/RI. (5)

Where RI represents average consistency index over a number of random entries of same order reciprocal matrices shown in Table 1. CR is acceptable, if its value is less than or equal to 0.10. If it is greater than 0.10, the judgment matrix will be considered inconsistent. To rectify the judgment matrix that is inconsistent, decision-makers' judgments should be reviewed and improved.

Calculation of Overall Priorities of Alternatives

The composite priority score of the alternatives can be derived by multiplying the relative priorities of an alternative by the relative priorities of the corresponding criteria and added over all criteria. Specifically,

[S.sub.i] = [n.summation over (j=1)] [w.sub.j][p.sub.ij] for i = 1, 2, ..., n (6)

Where [S.sub.i] is the composite score for the ith alternative risk management strategy, [p.sub.ij] is the score of the ith alternative risk management strategy with respect to the jth supply chain risk criterion, and [w.sub.j] is the priority weight of the [j.sub.the] supply chain risk criterion in the second level.


A survey questionnaire approach was used for gathering relational data to assess the order of importance of the pharmaceutical supply chain outsourcing risk. Thus, from the hierarchy tree, we developed a questionnaire to enable pairwise comparisons between all the risk attributes at each level in the hierarchy. The pairwise comparison process elicits qualitative judgments that indicate the strength of the experts' preference in a specific comparison according to Saaty's 1-9 scale. The experts were requested to respond to several pairwise comparisons where two categories at a time were compared with respect to the goal. The result of the survey questionnaire (not provided due to limited space) technique was then used as input for the AHP. The final pairwise comparison matrix is shown in Table 2.


Table 3 reports on the priority scores of risk response alternatives. For the major decision attributes, regulatory risk (0.383) is the most important risk to manage followed by intellectual property risk (0.342), technical risk (0.168), and business risk (0.107). With respect to the overall priority scores of risk response alternatives, risk reduction (0.388) is most preferred strategy followed by transfer risk (0.300), avoid risk (0.186), and accept risk (0.126), respectively.


Fiacco (1983) described SA as the effect of local perturbation over results and stability analysis as the effect of finite perturbation over results behavior. If decision makers believe that an attribute might be more or less important than originally indicated, they can drag that attribute's bar to the right (increase) or left (decrease) and then observe the impact on alternatives. The objective of SA in pharmaceutical global supply chain outsourcing risk management is to determine how the small changes (perturbation) in input parameters, including regulatory risk, business risk, technical risk, and intellectual risk will impact the ranking of the risk response alternatives. Min (1994) emphasized that "the sensitivity analyses are necessary because changing the importance of criteria requires different levels of resource commitment, ..." Figure 2-5 show the gradient sensitivity graphs of the alternatives' priorities with respect to regulatory, business, technical, and intellectual property risks at a time. The vertical line indicates the attribute's priority with respect to goal, while the diagonal lines are the priorities of the alternatives at each position of the vertical line. In each gradient graph, we performed a series of sensitivity analyses using AHP-based Expert Choice Software to investigate the impact of changing the priority of the attributes or criteria on the ranking of the risk response alternatives. In Figure 2, increasing the priority of regulatory risk from 0.38 to 0.65 or decreases to 0.20 did not change the choice of the alternative with respect to regulatory risk.


Therefore, minimizing the pharmaceutical global supply chain logistics outsourcing risk is insensitive to changes in the importance of regulatory risk. With respect to Figure 3, increasing the priority of business risk from 0.09 to 0.33 or decreases to 0.05, the ranks of the alternative remained stable or robust. For Figure 4, increasing (decreasing) the priority of technical risk from 0.16 to 0.45(0.10) is insensitive to changes in the importance of technical risk. Figure 5 shows that a ranking of alternatives remains the same, when the priority of the intellectual property risk increased (decreased) from 0.34 to 0.68(0.15). Based on the entire gradient sensitivity analyses, the overall priority of alternative is robust or stable to changes in the importance of all the attributes or criteria.





Although outsourcing strategy allows firms to become closer to global markets, the extended global supply chain come with great risks. Although the benefits and risks linked to pharmaceutical supply chain logistics outsourcing have been acknowledged widely, much of the inherent risks tend to be overlooked. However, risk management is imperative for achieving successful global supply chain outsourcing relationships. For a pharmaceutical firm, business risk such as the supplier's inability to meet agreed deadline can delay launching of a new drug and in turn lead to market share loss and profit margins. For the technical risk such as loss of control by the pharmaceutical firm, it can result in poor quality of the process that can result in attracting the attention of the FDA for possible sanction. Hence it pays to embrace the implementation of risk management in the global pharmaceutical outsourcing market.

Rapport (1967) notes "the stability of the optimal solution to changes in coefficients has important managerial implications." The application of SA in the evaluation of risk for pharmaceutical supply chain outsourcing is imperative because it can help C-level executives in improving their decision making. A well executed SA can aid the decision maker gain confidence in the solutions, comprehend what the critical judgments represent, and develop valuable insight into the problem and the effects certainty (Guikema & Milke, 2003) and risk parameter changes have on the risk management options.


Pharmaceutical Global Supply Chain Outsourcing RISK Analysis Survey Questionnaire


My name is --. I am a professor of Logistics/Supply Chain Management & International Business, Department of Management and Marketing, School of Business, Alabama A & M University, Normal, AL.

I am writing to elicit your opinion as an expert on risk management and/or enterprise risk management. I am investigating the opinions of experts by means of a survey questionnaire. Experts do not have to agree on the relative importance of the criteria, sub-criteria or the rankings of the alternative.

This questionnaire uses Analytic Hierarchy Process (AHP) to model risk management in the pharmaceutical supply chain logistics outsourcing market. As an expert on risk management and/or enterprise risk management, your opinion will be significantly invaluable to my research.


For your opinion as an expert, the pair-wise comparison scale by Saaty, reported in Table 1, can be used to assess or express the importance of one element over another.


Please mark or circle the criteria number (code) that you assess more or equal important than other, with respect to the goal: "managing risk" and express on the verbal scale the importance of the more or equal important criteria over the other.

Once again, thank you so much for your time and for offering your expert opinion.
Table 1. Saaty Scale--Pair-wise Comparison Values
or Scale of Preference between two Elements

Preference     Definition of Verbal Scale   Explanation

1              Equally preferred or         Two activities or
               equal importance of both     elements contribute
               elements                     equally to the objective

3              Moderately preferred         Experience and judgment
               or moderate importance of    slightly favor activity
               one element over another     or element over another

5              Strongly preferred or        Experience and judgment
               strong importance of one     strongly or essentially
               element over another         favor one activity over

7              Very strongly preferred      An activity is strongly
               or very strong importance    favored over another and
               of one element over          its dominance
               another                      demonstrated in practice

9              Extremely preferred or       The evidence favoring one
               extreme importance of one    activity over another is
               element over                 of the highest degree
                                            possible of affirmation

2,4,6,8        Intermediate values          Used to represent
                                            compromise between the
                                            preferences listed above
                                            or used to compromise
                                            between two judgments

Reciprocals    In comparing elements I
  of above     and j if i is 3 compared
               to j; then j is 1/3
               compared to i

If you mark or circle "4" in the following
question, means that "Business Risk" is 4 times
more important in your expert opinion than the
"Regulatory Risk."

1  Regulatory   9   8   7   6   5   4   3   2   1

   2   3   4   5   6   7   8   9   Business Risk

Conversely, marking or circling the number "1" in
the following question, means that "Regulatory
Risk" is as important as "Business Risk."

2   Regulatory   9   8   7   6   5   4   3   2   1

    2   3   4   5   6   7   8   9   Business Risk

Moreover, marking or circling "4" in the following
question, means that "Regulatory Risk" is 4 times
more important than the "Technical Risk."

3   Regulatory   9   8   7   6   5   4   3   2

    1   2   3   4   5   6   7   8   9   Technical Risk

It is my hope that the above examples are very
helpful. Please contribute your expert opinion by
marking (X) or cycling (O) for your choice of

Major Risk Criteria or Factors

Question 1. Please mark or circle the criteria
number (code) that you assess more or equal
important than other, with respect to the goal:
"to minimize risk in the pharmaceutical global
supply chain outsourcing."

1   Regulatory Risk   9   8   7   6   5   4   3   2   1
2   Regulatory Risk   9   8   7   6   5   4   3   2   1
3   Regulatory Risk   9   8   7   6   5   4   3   2   1
4   Business Risk     9   8   7   6   5   4   3   2   1
5   Business Risk     9   8   7   6   5   4   3   2   1
6   Technical Risk    9   8   7   6   5   4   3   2   1

1   2   3   4   5   6   7   8   9   Business Risk
2   2   3   4   5   6   7   8   9   Technical Risk
3   2   3   4   5   6   7   8   9   Intellectual Property Right Risk
4   2   3   4   5   6   7   8   9   Technical Risk
5   2   3   4   5   6   7   8   9   Intellectual Property Right Risk
6   2   3   4   5   6   7   8   9   Intellectual Property Right Risk

Risk Management Alternatives

Question 2. Please mark or circle the alternative
number (code) that you assess more or equal
important than other, with respect to criterion
"regulatory risk."

1   Reduce Risk    9   8   7   6   5   4   3   2   1
2   Reduce Risk    9   8   7   6   5   4   3   2   1
3   Reduce Risk    9   8   7   6   5   4   3   2   1
4   Reduce Risk    9   8   7   6   5   4   3   2   1
5   Reduce Risk    9   8   7   6   5   4   3   2   1
6   Reduce Risk    9   8   7   6   5   4   3   2   1

1   2   3   4   5   6   7   8   9   Accept Risk
2   2   3   4   5   6   7   8   9   Avoid Risk
3   2   3   4   5   6   7   8   9   Transfer Risk
4   2   3   4   5   6   7   8   9   Avoid Risk
5   2   3   4   5   6   7   8   9   Transfer Risk
6   2   3   4   5   6   7   8   9   Transfer Risk

Question 3. Please mark or circle the alternative number (code) that
you assess more or equal important than other, with respect to
criterion "business risk."

1   Reduce Risk    9   8   7   6   5   4   3   2   1
2   Reduce Risk    9   8   7   6   5   4   3   2   1
3   Reduce Risk    9   8   7   6   5   4   3   2   1
4   Reduce Risk    9   8   7   6   5   4   3   2   1
5   Reduce Risk    9   8   7   6   5   4   3   2   1
6   Reduce Risk    9   8   7   6   5   4   3   2   1

1   2   3   4   5   6   7   8   9   Accept Risk
2   2   3   4   5   6   7   8   9   Avoid Risk
3   2   3   4   5   6   7   8   9   Transfer Risk
4   2   3   4   5   6   7   8   9   Avoid Risk
5   2   3   4   5   6   7   8   9   Transfer Risk
6   2   3   4   5   6   7   8   9   Transfer Risk

Question 4. Please mark or circle the alternative number (code) that
you assess more or equal important than other, with respect to
criterion "technical risk."

1   Reduce Risk    9   8   7   6   5   4   3   2   1
2   Reduce Risk    9   8   7   6   5   4   3   2   1
3   Reduce Risk    9   8   7   6   5   4   3   2   1
4   Reduce Risk    9   8   7   6   5   4   3   2   1
5   Reduce Risk    9   8   7   6   5   4   3   2   1
6   Reduce Risk    9   8   7   6   5   4   3   2   1

1   2   3   4   5   6   7   8   9   Accept Risk
2   2   3   4   5   6   7   8   9   Avoid Risk
3   2   3   4   5   6   7   8   9   Transfer Risk
4   2   3   4   5   6   7   8   9   Avoid Risk
5   2   3   4   5   6   7   8   9   Transfer Risk
6   2   3   4   5   6   7   8   9   Transfer Risk

Question 5. Please mark or circle the alternative number (code) that
you assess more or equal important than other, with respect to
criterion "intellectual property right risk."

1   Reduce Risk    9   8   7   6   5   4   3   2   1
2   Reduce Risk    9   8   7   6   5   4   3   2   1
3   Reduce Risk    9   8   7   6   5   4   3   2   1
4   Reduce Risk    9   8   7   6   5   4   3   2   1
5   Reduce Risk    9   8   7   6   5   4   3   2   1
6   Reduce Risk    9   8   7   6   5   4   3   2   1

1   2   3   4   5   6   7   8   9   Accept Risk
2   2   3   4   5   6   7   8   9   Avoid Risk
3   2   3   4   5   6   7   8   9   Transfer Risk
4   2   3   4   5   6   7   8   9   Avoid Risk
5   2   3   4   5   6   7   8   9   Transfer Risk
6   2   3   4   5   6   7   8   9   Transfer Risk


Arnoff, E. L., & Netzorg., M. J. (January-February 1965). Operations Research-the Basics. Management Services, 46.

Baron, H., & Schmit, C. P. (1988). Sensitivity Analysis of Additive Multi-attribute Value Models. Operations Research, 36(1: 122-127.

Belton, V., & Gear, T. (1983). On a Shortcoming of Saaty's Method of Analytic Hierarchy. Omega, 11(3): 228-30.

Blake, J. T., Reibman, A. L., & Trivedi, K. S. (1988). Sensitivity Analysis of Reliability and Performability Measures for Multiprocessor Systems. ACM SIGMETRICS conference on Measurement and modeling of computer systems Santa Fe, New Mexico, United States.

Calantone, R.A., Di Benedetto, R.A., & Meloche, M. (1989). The analytic hierarchy process as a technique for retail store location selection. Journal of Business Strategies, 6 (1): 61-74.

CFO Research Services. (2004). Outsourcing Among Pharmaceutical and Biotech Firms: The Growing Imperative for a More Aggressive Approach to Outsourcing. CFO Publishing Corporation, 1-22.

Christopher, M. (1998). Logistics and Supply Chain Management. Second Edition, Prentice Hall, New York.

Christopher, M. (1999). Creating the Agile Supply Chain, Documents/creating_the_agile_supply_chain.pdf

Castillo, E., Minguez, R., & Castillo, C. (2006). Sensitivity Analysis in Optimization and Reliability Problems. In ESREL.

Datamonitor. (August 2006). Pharmaceutical Outsourcing: New opportunities in a fully Integrated Global Outsourcing Strategy. Datamonitor Pharmaceutical Report.

Dimasi, J. A., Hansen, R. W., & Grabowski, H. G. (2003). The Price of Innovation: New Estimates of Drug Development Costs. Journal of Health Economics, 22, 151-185.

Dunlop, M., Poldy, F., & Turner, G.M. (2004) Environmental Sustainability Issues Analysis for Victoria. Retrieved August 30, 2009 from$File/CSIRO.pdf.

Dyer, J.S. & Wendel, R.E. (1985). A Critique of Analytic Hierarchy Process. Working Paper 84/85-4-24. Department of Management, The University of Texas at Austin.

Dyer, R.F., & Forman, E.H. (1991). An Analytic Approach to Marketing Decisions, Englewood Cliffs: Prentice Hall.

Enyinda, C. I. (2008). Modeling Risk Management in Pharmaceutical Industry Global Supply Chain Logistics Using Analytic Hierarchy Process Model. Ph.D. Dissertation, North Dakota State University, Fargo.

Enyinda, C. I. Mbah, H.N., & Ogbuehi, O. A. (2010). An Empirical Analysis of Risk Mitigation in the Pharmaceutical Industry: A Developing Country Perspective. Thunderbird International Business Review, 52(1): 45-54.

Enyinda, C.I., Briggs, C., & Backhar, K. (2009). Analytic Hierarchy Process Framework for Assessing Risk in Pharmaceutical Supply Chain Outsourcing. Journal of Business and Accounting, 2(1): 32-41.

Eschenbach, T. G. (November/December 1992). Spiderplots versus Tornado Diagrams for Sensitivity Analysis, Interfaces, 22 (6): 40-46.

Erkut, E., & Tarimcilar, M. (1991). On Sensitivity Analysis in the Analytic Hierarchy Process IMA Journal of Management Mathematics, 3(1): 61-83.

Fiacco, A.V. (2003). Introduction to Sensitivity and Stability Analysis in Nonlinear Programming, Academic Press.

Grabowski, H. G., & Vernon, J. (2002). The Distribution of Sales Revenue from PharmacoEconomics. Supplement 1, 18(3): 21-23.

Graig, T. (2003). 4PL versus 3PL: A Business Process Outsourcing Option for International Supply Chain Management. Retrieved September 30, 2009 from

Guikema, S.D., & Milke, M.W. (September 2003). Sensitivity Analysis for Multi-Attribute Project Selection Problems. Civil Engineering and Environmental Systems, 20(3): 143-162.

Gunawan, R., Cao, Y., Petzold, L., & III, F. J. D. (2005). Sensitivity Analysis of Discrete Stochastic Systems. Biophysical Journal, 88, 2530-2540.

Harland, C., Brenchley, R., & Walker, H. (2003). Risk in Supply Networks. Journal of Purchasing & Supply Management, 9, 51-62.

Henaidy, H. (n.d). The Usefulness of Sensitivity Analysis in Capital Budgeting Decisions. Retrieved August 30, 2009 from 47006_18619.pdf.

Hosseiny, A. (2004). Risk Assessment of Outsourcing Pharmaceutical Packaging. Retrieved March 10, 2009, from sitebuilderfiles/riskass.pdf.

IBM. (2003). Beyond Mere Survival: Pharmaceutical Firms Adapting and Thriving Through on Demand Operations, IBM Business Consulting Services, 1-22.

Insua, D. (1990). In Beckmann, M. and Krelle, W. (Eds.), Sensitivity Analysis in Multi Objective Decision Making, in Lecture Notes in Economics and Mathematical Systems. Springer-Verlag, Berlin, Germany.

Kam, H. Booi (n. d). Managing Outsourcing Risks in the Global Supply Chain: An Exploration of Approaches. Retrieved July 30, 2009 from itlcsge/kam.pdf.

Lindholm, A. & Suomala, P. (2004). The Possibilities of Life Costing in Outsourcing Decision Making. Frontiers of E-Business Research. Retrieved February 17, 2009 from

Min, H. (1994). Location Analysis of International Consolidation Terminals Using the Analytic Hierarchy Process. Journal of Business Logistics, 15(2): 25-44.

Persson, G., & Virum, H. (2001). Growing Strategies for Logistics Service Providers: A Case Study. International Journal of Logistics Management, 12(1): 53-64.

Perez, P. (1995). Some Comments on Saat's AHP. Management Science, 41(6): 1091-1095.

Piachaud, B. S. (2002). Outsourcing in the Pharmaceutical Manufacturing Process: an Examination of the CRO Experience. Technovation, 22(2): 81-90.

PriceWaterHouseCoopers. (2008). The Changing Dynamics of Pharma Outsourcing in Asia: Are you Readjusting your Sights? Retrieved April 20, 2009 from extweb/pwcpublications.nsf/docid/8C2A9298CA38A53D802574BE003B8A2D].

PriceWaterHouseCoopers. (2009). Pharma 2020: Challenging Business Models-Which Path will you Take? Retrieved April 20, 2009 from /docid/89150A737EF CF98B802575A50032D4A8.

PriceWaterHouseCoopers. (2007). Pharma 2020: The Vision - Which Path will you Take? Retrieved April 20, 2009 from 91BF330647FFA 402852572F2005ECC22.

PriceWaterHouseCoopers. (2009). Pharma 2020: Marketing the Future - Which Path will you Take? Retrieved April 20, 2009 from docid/B1221EC9D 057309F852575610076732C.

PriceWaterHouseCoopers. (2006). How can Pharmaceutical and Life Sciences Companies Strategically Engage Global Outsourcing? Retrieved March 15, 2009 from http://

Rappaport, A. (July 1967). Sensitivity Analysis in Decision Making. The Accounting Review, 441-456.

Ross, M.E., & Nydick, R.L. (1994). Structuring the Selection Process of Licensing Candidates in the Pharmaceutical Industry Using the Analytic Hierarchy Process. Journal of Pharmaceutical Marketing and Management, 8(1): 21-36.

Saaty, T.L. (2000). Fundamentals of Decision Making and Priority Theory (2nd ed), Pittsburgh, PA: RWS Publications.

Saaty, T.L. (1980). The Analytic Hierarchy Process. New York: McGraw-Hall.

Schiff, R. (2007). Risk Management in Pharmaceutical Outsourcing. Retrieved April 15, 2009 from

Srivastava, N. (September 2002). The Trends in the Biotech/Pharmaceutical Outsourcing Industry. Can Biotech BioMed Outsourcing. Newsletter: 1, Issue 1.

Steenland, K., & Greenland, S. (2004). Monte Carlo Sensitivity Analysis and Bayesian Analysis of Smoking as an Unmeasured Confounder in a Study of Silica and Lung Cancer. American Journal of Epidemiology, 160(4): 384-392.

Triantaphyllou, E. & Sanchez, A. (Winter 1997). A Sensitivity Analysis Approach for Some Deterministic Multi-criteria Decision Making. Decision Sciences, 28(1): 151-194.

Trueman, R. E. (1974). An Introduction to Quantitative Methods for Decision Making. New York: Holt, Rinehart and Winston, Inc.

Wallace, S. W. (January-February 1998). Decision Making Under Uncertainty: Is Sensitivity Analysis of Any Use? Operation Research, 48(1): 20-25

Chris I. Enyinda

Alabama A & M University

Fesseha Gebremikael

Calhoun Community College

Chris I. Enyinda, Ph.D., Ph.D. is Professor and Coordinator of Logistics/Supply Chain Management and International Business Programs, Department of Management and Marketing, Alabama A & M University. Chris is the President of the International Academy of African Business and Development. He has published scores of papers in a number of journals and peer-reviewed proceedings. He has received many outstanding/best paper awards. His research interests include supply chain risk management, pharmaceutical supply chain, sustainable supply chain, transportation management, global supply chain management, and International Business.

Fesseha Gebremikael, M.S., MPIED, Assistant Professor of Economics, Department of Business/ Computer Information System, Calhoun Community College. He has numerous publications in Journals and peer-reviewed proceedings to his credit. He has received many outstanding paper awards. And was also a recipient of 2008-2009 Steward Fellowship Award. His research interests include supply chain economics, Supply chain risk management, healthcare economics, labor supply chain economics, and International Business.
Table 1
The Reference Values of RI for Different Numbers of n

n     2    3      4     5      6      7      8      9      10
RI    0   0.58   0.9   1.12   1.24   1.32   1.41   1.45   1.51

Table 2
Pair-wise Comparison Matrix for the Four Criteria

                   Regulatory   Business   Technical   Intellectual
                      Risk        Risk       Risk      Property Risk

Regulatory Risk        1           3           3             1
Business Risk         1/3          1           2             3
Technical Risk         1          1/2          1             2
Intellectual           1          1/3         1/2            1
  Property Risk

Table 3 Priority Scores of Risk Response Alternatives

Attribute                Alternatives    Priority Scores    Ranks

Regulatory               Transfer Risk        0.451           1
  Risk (0.383)            Reduce Risk         0.261           2
                          Accept Risk         0.169           3
                          Avoid Risk          0.119           4
Business Risk (0.107)    Transfer Risk        0.513           1
                          Reduce Risk         0.226           2
                          Accept Risk         0.193           3
                          Avoid Risk          0.068           4
Technical Risk (0.168)    Reduce Risk         0.56            1
                          Avoid Risk          0.249           2
                          Accept Risk         0.095           3
                         Transfer Risk        0.095           4
Intellectual              Reduce Risk         0.527           1
   Property (0.342)       Avoid Risk          0.241           2
                         Transfer Risk        0.129           3
                          Accept Risk         0.109           4
Overall                   Reduce Risk         0.388           1
                          Avoid Risk          0.300           2
                         Transfer Risk        0.186           3
                          Accept Risk         0.126           4
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Author:Enyinda, Chris I.; Gebremikael, Fesseha
Publication:International Journal of Business and Economics Perspectives (IJBEP)
Article Type:Abstract
Geographic Code:7IRAN
Date:Sep 22, 2010
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