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A marginal analysis guided technology evaluation and selection
Int. J. Production Economics 131 (2011) 15–21
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Int. J. Production Economics journal homepage: www.elsevier.com/locate/ijpe
A marginal analysis guided technology evaluation and selection Kim Hua Tan a,n, James Noble b, Yuji Sato c, Ying Kei Tse a a b c
Nottingham University Business School, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, England, UK Department of Industrial and Manufacturing Systems Engineering, University of Missouri—Columbia, E3437 Engineering Building East, Columbia, MO 65211, USA Graduate School of Policy Science, Mie Chukyo University, 1846 Kubo, Matsusaka, Mie, 515-8511 Japan
a r t i c l e in fo
abstract
Available online 29 September 2010
Making decisions on strategic investments, such as early stage manufacturing technology (MT), is a complicated task. Early stage technologies are usually costly, and surrounded by uncertainty. The potential benefits are often hard to quantify prior to implementation. Thus, how could managers make good decisions in a high-risk, technically complex business when the information they need to make those decisions comes largely from the project champions who are competing against one another for resources? Traditionally, in this problem domain, decisions are made based upon gut-feeling and past experience, sometimes with the support of some multi-criteria decision-support tools. The criteria evaluation process is very subjective and relies heavily on managers’ experience, knowledge, as well as intuition. Thus, the evaluation approach is often not effectively carried out as there is lack of visibility and traceability in the decision making process. The impact of this scenario is that managers are not confident that resources are being optimised and applied to a mixed portfolio of projects to maximise benefits. This paper proposes a marginal analysis directed branch and bound approach for evaluating and selecting early stage manufacturing technology (MT) projects. A case study is used to demonstrate the application of the proposed approach. Implications of the proposed approach to practitioners and academia are discussed and future research outlined. Crown Copyright & 2010 Published by Elsevier B.V. All rights reserved.
Keywords: Decision support systems Early stage technologies Marginal analysis Visualisation
1. Introduction Many early stage manufacturing technologies (MT) are of strategic importance and may create future competitive opportunities. It is a challenge for manager to clarify the right manufacturing technology alternatives as the number of technologies is increasing and the technology are becoming more and more complex (Torkkeli and Tuominen, 2002; Shehabuddeen et al., 2006). Therefore, managers need better methods that can evaluate the strategic value of MT investment when the future is uncertain (Auerswald et al., 2005). This paper defines early stage as the early development of fundamental manufacturing processes or technologies. Many researchers have developed approaches to support technology evaluation and selection. The evaluation and selection procedures have attracted the interest of a range of different tools and techniques (Branke et al., 2007). Several common elements were apparent, Henriksen and Traynor (1999) categorizes these methods and techniques into the following categories:
n
Corresponding author. E-mail addresses: [email protected] (K.H. Tan), [email protected] (J. Noble), [email protected] (Y. Sato), [email protected] (Y.K. Tse).
(a) unstructured peer review, (b) mathematical programming (LP, NLP, etc.), (c) economic models (IRR, NPV, etc.), (d) decision analysis (MAUT, Decision Trees, etc.) interactive methods (Dephi, Q-sort, etc.), (e) artificial intelligence (Expert Systems, CBR, Fuzzy logic, etc.), and (f) portfolio optimisation. However, some of these methods are so mathematically elaborate that it is difficult for managers to use them in practice. Loch and Kavadias (2002) pointed out that existing selection models proposed in the literature are highly complex, not transparent in application, and as a result, have not been commonly used in management practice. Often, determining which technology to pursue combines both quantitative and qualitative data that tend to stretch over an extended time horizon before a final decision is made. One potential pitfall is that both during and after the decision process the rationale for specific decisions can become unclear and difficult to defend. A visual decision path that captures the logic behind the variety of decisions made over the course of a technology adoption process can provide both the decision support and corporate memory necessary to ensure success in the future. There are numbers of literatures proposing decision support models for solving technology investment problems (Khouja, 2005; Sriram and Stump, 2004; Debo, et al., 2005; Wallenius et al., 2008), but only few models have been included for representing
0925-5273/$ - see front matter Crown Copyright & 2010 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2010.09.027
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K.H. Tan et al. / Int. J. Production Economics 131 (2011) 15–21
a transparent decision pathway of decision making process. The importance of a visual representation to support decision making has been emphasized by many researchers (Klein and Thomas, 2009; Caridi et al., 2009; Lohse et al., 1994; Tufte, 1990; Foil and Huff, 1992; Eden and Ackerman, 1998; Tan and Platts, 2003). They argue that visualisation transforms raw data into pictures that people can understand quickly, and it is a form of knowledge representation. Managers perform better when their problem solving processes are adapted to a visual problem representation (Vessey, 1991). Mckim (1972) sees visualisation as a ‘visual’ vehicle of thought to assist managers in making decision. Foil and Huff (1992) point out that a visual representation such as a mental map can provide new ways of examining and improving managerial judgement. A mental map is in itself a useful form for helping managers to make sense of complexity. Visual representation can both simplify ideas and facilitate the transmission of complex ideas from individual to individual and unit to unit. Most importantly, visual representation helps to divorce ideas from specific speakers, making them more accessible to debate and modification. Moreover, they argue that visual representations are of potential interest to managers because they are a means of displaying graphically the firm’s current strategic position, as various observers understand it, and because they hold the promise of identifying alternative routes to improving that position. When setting out on the task of technology evaluation, managers have available to them several existing visual techniques/models to aid structuring and analysing information, monitoring decision proceses, and communicating strategic direction. These techniques are Influence diagrams, DCF, Decision trees, Roadmapping, and Portfolio matrices. By and large, there are limitations with these techniques when it comes to decision of visualisation support:
Overly simplistic—DCF and Decision trees may not be suitable for complex evaluation involving multiple criteria
Qualitative—Roadmapping and Influence diagrams are helpful
to develop a structured network of thought that leads to better insight but network relationships are difficult to quantify. Lack of visibility—Portfolio matrices provide little ‘traceability’ on the decisions that lead to the matrix construction.
Clearly, the visual support for technology evaluation provided by the above approaches is inadequate. And this is where managers begin to feel uncomfortable. They ask: I have a range of alternative technologies to invest in, but how could I visualise the decision process to improve my confidence in the decision made? Thus, managers need a better way to make decisions, they need traceability of the decisions made in the process to be presented in a meaningful format. Lohse et al. (1994) pointed out that appropriate visual representations can facilitate problem solving and discovery by providing an efficient structure for expressing data. By being able to visualise the path of decisions, managers are able to consider and give a fair appraisal of their merits. This paper reports on the on-going work in a project to apply the marginal analysis-based branch and bound approach as the basis of directing the selection of MT so that an integrated risk/ multi-criteria decision path can be developed. The proposed marginal analysis approach is a sequence of transparent steps to provide clarity of thought into the evaluation and selection process that managers undertake. In the following sections, the development of the proposed approach is explained. A case is used to illustrate the application of the proposed approach. The results are described and implications of this research for industrialists and academics are discussed.
2. Marginal analysis directed branch and bound An approach for determining the effect of taking a given decision path (Noble and Tanchoco, 1993, 1995) is needed to analyze the trade-off between different decision paths. When considering the development of a decision path to support selection of early stage technology it quickly becomes obvious that most decisions are incremental in nature (Eilon, 1984; Tan and Noble, 2007; Tse et al., 2009). Marginal Analysis Directed Branch and Bound was first proposed by Noble and Tanchoco (1995) for directing the design process of a complex material handling system, in which a combined economic and performance justification path is developed to the engineers for final judgment. Tse et al. (2009) has adopted Marginal Analysis Directed Branch and Bound in mitigating the product quality risk in global supply chain by considering the incremental benefit for configuring firm’s supply network. Moreover, Tan and Noble (2007) proposed a Marginal Analysis Directed throughput analysis approach, which employs incremental calculus (Eilon, 1984) for evaluating the relationships among various decision variables in a performance model quantitatively. For example, a XYZ technology-based system has capabilities that tend to be a function of discrete decisions (whether they are small parameter level changes or system level changes) and the design process tends to reflect this since decisions occur in a serial manner. Therefore, it would be desirable for decision makers to be guided down the most promising path if a method of projecting the effect of each discrete decision were available. Fig. 1 illustrates the concept of a decision path. In Fig. 1, all priority and risk criteria are explored sequentially starting from the criteria that is weighted the greatest. The approach proposed in this paper consists of determining an initial instance of the solution, then as the decision process proceeds different solution changes are explored to determine which provides the greatest marginal benefit. Overall, the goal of the marginal analysis guided approach is to develop a decision path that can serve as the basis of a combined multi-criteria (financial, strategy, health/safety, supply security, quality, importance, deployment, novelty, intellectual property, customer service) and risk justification argument. The decision path provides the reasoning from a marginal analysis perspective as to why a specific aspect of the technology was selected. The procedure requires that the decision maker determine the initial selection criteria. The nature of the procedure enables the decision maker to re-evaluate the selection criteria and to conduct marginal analysis on the effect of changing selection criteria. This is desirable since it is possible during the course of a selection process that the decision maker might modify the selection criteria due to insight gained. The result of evaluating not only the technology alternatives, but also the decision criteria, is that the final decision will be fully justified. An additional benefit of justifying each decision (incrementally versus end-of-process) is that the rationale behind each decision is captured (decision path) and can then be used as the basis of an overall justification argument. Alternatives are evaluated using a branching and bounding process with respect to each alternatives performance for the selection criteria (see Fig. 2). In the process of evaluating an alternative it is common for new alternatives to be generated incrementally. This incremental alternative generation is analogous to the branching that occurs when exploring the solution space of a combinatorial optimisation problem (Land and Doig, 1960). The decision maker then utilizes the results of the marginal analysis to bound the solution back toward the required performance. Hence, the marginal analysis serves as the basis for exploring the solution space via a branch and bound approach.
K.H. Tan et al. / Int. J. Production Economics 131 (2011) 15–21
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Selection Criteria Risk Financial Strategy
Incremental Changes in Criteria
HSE
+0.1
Supply Security Quality
+0.2 -0.2
Importance Deployment Novelty IP Potential Customer Fig. 1. A technology selection decision path.
Branching
Bounding
Criteria 1 MT3 MT1 MT4 MT2
MT Alternatives
MT4 MT2 MT1 MT3 MT…
Ranked Alternatives
Criteria 2
Performance Analysis Marginal Analysis
Decision
Criteria n
Justification
Fig. 2. A branch and bound process.
3. A decision path example The following presents an example to illustrate the application of the marginal analysis approach. Company ABC must make a decision concerning the adoption of a new product identification/tracking technology. Initially, there are two different vendors and configurations associated with implementing this technology, both have different performance levels compared with a set of evaluation criteria (Table 1). The company has determined the overall priority ranking between the four primary decision criteria (1st – System flexibility, 2nd – Compatibility with existing systems, 3rd – Implementation cost, and 4th – Vendor reputation). The marginal analysis is initiated by establishing Alternative 1 as the baseline, then evaluating Alternative 2 relative to Alternative 1 (Table 2). As can be seen, Alt 2 performs slightly better ( +4.4%) with respect to System flexibility (the highest ranked decision criteria), but it is significantly worse with respect to Compatibility ( 30.4%). Its performance on Compatibility alone is sufficient to bound it from further consideration, so the fact that it also performs poorly on Cost ( 10.4%) and somewhat better
Table 1 Primary decision criteria. Criteria priority rank
Alt 1 Alt 2
1st
2nd
3rd
4th
Flexibility
Compatibility
Cost
Vendor
45 47
23 16
67 60
45 50
( +11.1%) with respect to Vendor does not influence the decision to remove Alternative 2 from further consideration. At this point Company XYZ explores other potential providers of the technology from which Alternatives 3, 4, and 5 are generated (or branched) as shown in Table 3. The marginal analysis proceeds by comparing Alterative 3–Alternative 1 (Table 4). Alt 3 is slightly better with respect to the top ranked criteria ( +11.1%) and the lowest ranked criteria ( +6.7%), Flexibility and Vendor, respectively. However, it is significantly better ( + 30.4%) with respect to Compatibility. The only concern that is raised is that it is slightly less desirable
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K.H. Tan et al. / Int. J. Production Economics 131 (2011) 15–21
Table 2 Marginal analysis on Alt 1 and 2.
Flexibility Compatibility Cost Vendor
Table 5 Marginal analysis on Alt 4 and Alt 3.
Alt 1
Alt 2
Alt 2 vs Alt 1
45 23 67 45
47 16 60 50
4.4% 30.4% 10.4% 11.1%
Ok Not acceptable Not acceptable Good Reject Alt 2
Table 3 Other alternatives. Criteria priority rank
Alt 3 Alt 4 Alt 5
Alt 4 vs Alt 3
48 30 66 46
4.0% 0.0% 1.5% 4.2%
Concern Ok Concern Reject Alt4
Table 6 Marginal Analysis on Alt 5 and Alt 3. 1st
2nd
3rd
4th
Flexibility
Compatibility
Cost
Vendor
50 48 55
30 30 30
65 66 66
48 46 55
Table 4 Marginal analysis on Alt 3 and Alt 1.
Flexibility Compatibility Cost Vendor
Flexibility Compatibility Cost Vendor
Alt 4
Alt 3
Alt 3 vs Alt 1
50 30 65 48
11.1% 30.4% 3.0% 6.7%
Good Good Concern Ok Accept Alt 3
( 3.0%) with respect to Cost. However, the management team deems this a valid trade-off and accepts Alternative 3 over Alternative 1. This illustrates the role of the marginal analysis, branch and bound, approach. It is not designed to prescribe solutions, but rather to assist decision makers in supporting decision making, capturing decision logic, and representing the branching and bounding decision process. Alterative 4 is compared with Alternative 3 (Table 5). The result of the comparison reveals that across the board Alternative 4 has little additional to offer over Alternative 3. On three of the four criteria is equal or worse and only on the third ranked criteria, Cost, is it slightly better ( + 1.5%). Alternative 4 is bound from further consideration and therefore rejected. Finally, Alternative 5 is compared with Alternative 3 (Table 6). The marginal analysis reveals that for all criteria Alternative 5 is better or equal to Alternative 3. Therefore, Alternative 5 is accepted and is considered to be the best overall alternative currently available for implementing this new technology. Fig. 3 illustrates the overall decision path as a result of the marginal analysis of the different technology alternatives. Presenting the branch and bound decision logic in this manner provides a visual of what decisions were made and the rationale for making them. The above example of a marginal analysis guided selection process has illustrated the development of a decision path as a means to support the evaluation and selection of a technology. The approach provides a mechanism for analysing the impact of each decision trade-off on overall technology performance so that a quantitative analysis of the reasoning behind each decision results. The resulting decision path provides a strong basis for justifying the selection of a specific technology. It is important to stress that the marginal analysis approach is different from the widely known pairwise comparison approach (Saaty, 1996). The proposed marginal analysis provides the basis
Flexibility Compatibility Cost Vendor
Alt 5
Alt 5 vs Alt 3
55 30 66 55
14.6% 0.0% 1.5% 14.6%
Good Good Ok Good Accept Alt 5
for conducting pairwise comparisons and developing a sequential decision path. A full pairwise comparison is not necessary (and therefore, waste valuable decision maker time) if managers can bound alternatives from further consideration, which is possible when incrementally justifying each additional performance increment using marginal analysis.
4. Case study A case study conducted by Tan et al. (2006) is used to further illustrate the applicability of the proposed marginal analysis approach. The case was conducted in Company Pharma, a worldleading manufacturer in the pharmaceutical industry. Traditionally, prioritisation and funding decisions for proof of concept proposals on significant, large MT projects in Company Pharma were mainly made by a central committee. The approach, however, could lead to gaps in the consistency of decision making. Decisions were reached mainly through consensus and the criteria used by the committee members to evaluate a proposal were not explicitly known and documented. In the previous work, a hybrid intelligent decision support system is developed for supporting managers in making timely and optimal MT investment decision. The intelligent system adopts Fuzzy ARTMAP (FAM) neural network modelling techniques for retrieving the historical evaluation results. FAM is employed to guide the retrieval process of the adapted case (i.e. the MT investment project prioritisation and evaluation) in the repository. For a full description of the hybrid intelligent system decision mechanism, please refer to authors’ former work (Tan, et al., 2006). However, the intelligent system approach has a major drawback in decision visibility, i.e., the pattern learning mechanisms and the retrieval process are ‘‘blackbox’’ mechanisms. The decision makers of Pharma cannot know why this decision is made, what factors they are considering, and what trade-offs has been taken. As a result, the management team was not confident that the resources had been optimised, as the decision process is lack of transparency and traceability. Thus, the marginal analysis approach is proposed to direct the justification of MT investment decision making. The visibility of decision making is improved by providing a clear justification pathway in which each criteria trade-offs are clearly illustrated to the committee members. The following example illustrates how the marginal analysis approach can be applied at the MT Concept Phase for Company Pharma. Table 7 represents 10 candidate MT projects with their
K.H. Tan et al. / Int. J. Production Economics 131 (2011) 15–21
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Alt 1
Alt 2
Alt 3
Alt 4
Flexibility
45
4%
11%
-4%
15%
Compatibility
23
-30%
30%
0%
0%
Cost
67
-10%
-3%
2%
2%
Vendor
45
11%
6%
-4%
15%
Reject
Accept
Reject
Accept
Alt 5
Fig. 3. Marginal analysis decision path.
with respect to the ratio of change between two pairs of performance criteria.
Table 7 Set of candidate MT projects. MT projects
Priority
Risk
1 2 3 4 5 6 7 8 9 10
63.4 64.3 72.9 54.9 69.4 40.4 55.2 40.1 31.7 40.6
44.8 52.8 59.2 34.4 44.8 44.8 30.4 12.0 22.4 67.2
respective Priority and Risk scores. The priority and risk scores are determined in the evaluation module, which employs the FAM techniques to access the criteria performance of each potential MT project. The marginal analysis approach involves a series of incremental priority and risk calculations, which are explained as following:
Incremental priority and incremental risk—determining the
incremental priority and risk allows for a relative measure of how each performance measure changes with respect to each alternative. Given that the absolute measures of priority and risk have no real meaning (unlike cost or profit that have a monetary unit)—an incremental approach provides a more robust basis for conducting the analysis Difference between incremental priority and incremental risk— calculating the difference between the incremental priority and incremental risk provides an aggregate measure of how multiple measures of goodness combine (in our example we assume that they are weighted equally, but if they were not then the difference could be weighted accordingly). For example, a positive incremental change in priority is desirable and a negative incremental change in risk is desirable, hence when both priority and risk have a relatively equivalent positive incremental change—then the net effect is that there is not an overall incremental benefit for the challenger alternative. Incremental priority/Incremental risk—this ratio provides additional insight into the magnitude of the overall incremental changes, as such it is an auxiliary measure and not crucial to a marginal analysis. It is also only effective when considering pairs of performance criteria. Its role is to provide a relative measure of the marginal differences between two alternatives
Table 7 presents marginal analysis results based on justifying additional priority with respect to a changing risk profile and the resulting selection decision. In this case Project 9 has the lowest Priority score ¼31.7 and on this basis alone it is rejected. The marginal analysis then proceeds by comparing the next highest Priority score ( ¼40.1 for Project 8, with an associated Risk score ¼ 12.0). The marginal change in Priority score is 26.5% ¼(40.1–31.7)/ 31.7, with a corresponding marginal change in Risk score ¼ 46.4%. This results in a marginal difference of 73%¼26.5– ( 46.4) and a ratio of Incremental Priority to Incremental Risk of 57% ¼26.5/ 46.4. Both a positive marginal difference and a negative Inc Priority/Incr Risk ratio reveals an improvement compared with Project 9. However, based on Project 8 having a low overall Priority score it is classified as a ‘‘maybe’’ project. Fig. 4 illustrates the resulting decision path with the Incremental Priority, Incremental Risk, and Differences between Incremental Priority and Incremental Risk given. Table 8 provides the sequential marginal analysis calculations and resulting actions for Company Pharma. As the MT analysis continues into another MA evaluation, a more detailed marginal analysis is conducted and a corresponding decision path is generated (Fig. 5). In this case the projects ranked as ‘‘Maybe’’ and ‘‘Yes’’ are further analyzed sequentially starting from the lowest overall Priority score and considering the highest individual Priority criteria first. As the more detailed analysis proceeds projects will drop out of contention based on their marginal contributions. As shown in Table 9, the Pharma managers identified the sequence of conducting marginal analysis. The sequence is based on the importance of the criterion for selecting the MT investment. In each marginal analysis, (i) the incremental criterion performance (obtained from the intelligent system in previous work), (ii) the difference between incremental priority and incremental criterion, and (iii) the ratio of incremental priority and incremental criterion are calculated. In this case, there are eleven marginal analysis planed to be conducted, but not all the marginal analysis will be gone through since the final solution usually is obtained before all the marginal analysis are processed. The results indicate that the proposed marginal analysis approach could create a powerful decision path that helps Pharma managers to gain insight into MT evaluation and selection process. It is easy to apply, and the transparency of the decision process helps to address one of the major limitations of existing
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K.H. Tan et al. / Int. J. Production Economics 131 (2011) 15–21
Differences between Incremental Priority and Incremental Risk 7
+12
5
4
-32
9
-27
1
8 +73
-16
+84
Accept
+23 3
2
Consider
-273
Reject 6 -49 10
Solid line = Inc Priority Dashed line = Inc Risk
x
Fig. 4. Illustrative decision path for technology selection—risk marginal analysis.
Table 8 Marginal analysis on priority and risk for 10 candidate MT projects. MT projects
Priority
Risk
Incr. priority (%)
Incr. risk (%)
Difference between Incr. priority – Incr. risk (%)
Incr. priority/Incr. risk (%)
9 8 6 10 4 7 1 2 5 3
31.7 40.1 40.4 40.6 54.9 55.2 63.4 64.3 69.4 72.9
22.4 12.0 44.8 67.2 34.4 30.4 44.8 52.8 44.8 59.2
0 26.5 0.7 0.6 35.1 0.5 15.0 1.4 7.9 5.1
0 46.4 273.3 50.0 48.8 11.6 47.4 17.9 15.2 32.1
73 273 49 84 12 32 16 23 27
57 0.3 1 72 5 32 8 52 16
Action
Reject Maybe Reject Reject Yes Yes Maybe Maybe Yes Maybe
Selection Criteria Risk Financial
Incremental Changes in Criteria
Strategy
Accepted Projects
HSE
P4 +0.1
P4
P7
P7
P1
P5
P1
P5
P5
P3
Supply Quality
+0.0 -0.2
Importance
P8
Deployment
P2 Rejected Projects
Novelty IP Customer Fig. 5. Illustrative decision path for technology selection—second phase.
selection model i.e. not transparent (Loch and Kavadias, 2002). The visibility of the decision process allows Pharma managers to trace back how various different decisions were explored to determine which project provides the greatest marginal benefit.
Nevertheless, the marginal analysis approach does require the decision maker to provide for some quantification of the benefits/ costs associated with an alternative, this does require more time and effort. The marginal analysis approach does not prescribe
K.H. Tan et al. / Int. J. Production Economics 131 (2011) 15–21
Table 9 Sequence of conducting marginal analysis in pharma case study. Sequence of conducting marginal analysis
Criterion
1 2 3 4 5 6 7 8 9 10 11
Risk Financial Strategy Health/Safety (HSE) Supply Quality Importance Deployment Novelty Intellectual property (IP) Customer
a solution (which some might consider a limitation), but rather it provides rationale for why a decision is made.
5. Discussions and summary The above case study has demonstrated the possibility of using a marginal analysis approach to support managers in the difficult task of evaluating and selecting manufacturing technology. The marginal analysis guided approach develops a decision path as a means to support MT evaluation. A marginal analysis is used to evaluate alternatives with respect to all decision criteria. Then a decision path is developed that captures the logic behind the variety of decisions made over the course of a technology adoption process. The decision path provides the reasoning from a marginal analysis perspective as to why a specific aspect of the technology was selected, thereby, providing the corporate memory necessary to ensure success in the future. A marginal analysis approach is a systematic approach for transforming opaque decision problems into transparent problems by a sequence of transparent paths. In other words, it offers the possibility to managers of replacing confusion by clear insight into justification. The decision path provides new clarity to the analysis process, allowing visualisations that are both easily understandable and traceable. The path helps in understanding and permits managers to assure both effectiveness and efficiency in evaluation and justification. The power of the marginal analysis transforms opaque decision situation into transparent ones. Thus, it helps to increase transparency in the early stage MT evaluation process and ensure managers the quality of thinking behind the evaluation was consistent. Although encouraging initial results have been achieved, the proposed approach needs to be further validated. It is planned that continued application of the approach in a
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couple of case companies will reveal the value of the proposed approach in a real environment. References Auerswald, P.E., Branscomb, L. M., Demos, N., Min, B.K., 2005. Understanding private-sector decision making for early-stage technology development, A between invention and innovation, Project Report, NIST GCR 02–841A, September. Branke, J., Chick, S.E., Schmidt, C., 2007. Selecting a selection procedure. Management Science 53 (8), 1916–1932. Caridi, M., Crippa, L., Perego, A., Sianesi, A., 2009. Do virtuality and complexity affect supply chain visibility? International Journal of Production Economics. 10.1016/j.ijpe.2009.08.016. Debo, L.G., Toktay, L.B., Van Wassenhove, L.N., 2005. Market segmentation and product technology selection for remanufacturable products. Management Science 51 (8), 1193–1205. Eilon, S., 1984. The art of reckoning—analysis of performance criteria. Academic Press, London. Eden, C., Ackerman, F, 1998. Making strategy. Sage Publications, London. Foil, C.M., Huff, A.S., 1992. Maps for managers: where are we? Where do we go from here?. Journal Of Management Studies 29 (3) 267–285. Khouja, M., 2005. Joint inventory and technology selection decisions. Omega 33, 47–53. Klein, T., Thomas, A., 2009. Opportunities to reconsider decision making processes due to Auto-ID. International Journal of Production Economics 121 (1), 99–111. Henriksen, A.D., Traynor, A.J., 1999. A practical R&D project selection scoring tool. IEEE Transactions on Engineering Management 46 (2), 158–170. Land, A.H., Doig, A.G., 1960. An automatic method for solving discrete programming problems. Econometrica 28, 497–520. Loch, C.H., Kavadias, S., 2002. Dynamic portfolio selection of NPD programs using marginal returns. Management Science 48 (10), 1227–1241. Lohse, G., Biolsi, K., Walker, N., Rueter, H., 1994. A classification of visual representations. Communications of the ACM 37 (12), 36–49. Mckim, R.H., 1972. Experiences in visual thinking. Wadsworth Publishing, California. Noble, J.S., Tanchoco, J.M.A., 1993. A framework for material handling system design justification. International Journal of Production Research 31 (1), 81–106. Noble, J.S., Tanchoco, J.M.A., 1995. Marginal analysis guided design justification: a material handling example. International Journal of Production Research 33 (12), 3439–3454. Saaty, T., 1996. The analytic network process-decision making with dependence and feedback. RWS Publications, Pittsburgh, PA. Shehabuddeen, N., Probert, D., Phaal, R., 2006. From theory to practice: challenges in operationalising a technology selection framework. Technovation 26, 324–335. Sriram, V., Stump, R., 2004. Information technology investments in purchasing: an empirical investigation of communications, relationship and performance outcomes. Omega 32, 41–55. Tan, K.H., Lim, CP., Platts, K., Koay, HS., 2006. Managing manufacturing technology investments: an intelligent learning system approach. International Journal of Computer Integrated Manufacturing 19 (1), 4–13. Tan, K.H., Platts, K., 2003. Linking objectives to action plans: a decision support approach based on the connectance concept. Decision Sciences 34 (3), 569–593. Tan, K., Noble, J., 2007. Plug and play modeling approach to throughput analysis. Journal of Manufacturing Technology Management 18 (7), 807–817. Torkkeli, M., Tuominen, M., 2002. The contribution of technology selection to core competencies. International Journal of Production Economics 77, 271–284. Tse, Y. K., Tan, K. H., Ng, S., 2009. Managing product quality risk in global supply chain. In: Proceedings of the 3rd international conference on operations and supply chain management. pp. 259–267. Tufte, E., 1990. Envisioning information. Graphics Press, Cheshire. Vessey, I., 1991. Cognitive fit: a theory-based analysis of the graphs versus tables literature. Decision Sciences 22 (2), 219–240. Wallenius, J., Dyer, J.S., Fishburn, P.C., Steuer, R.E., Zionts, S., Deb, K., 2008. Multiple criteria decision making, multiattribute utility theory: recent accomplishments and what lies ahead. Management Science 54 (7), 1336–1349.
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Int. J. Production Economics journal homepage: www.elsevier.com/locate/ijpe
A marginal analysis guided technology evaluation and selection Kim Hua Tan a,n, James Noble b, Yuji Sato c, Ying Kei Tse a a b c
Nottingham University Business School, University of Nottingham, Jubilee Campus, Nottingham NG8 1BB, England, UK Department of Industrial and Manufacturing Systems Engineering, University of Missouri—Columbia, E3437 Engineering Building East, Columbia, MO 65211, USA Graduate School of Policy Science, Mie Chukyo University, 1846 Kubo, Matsusaka, Mie, 515-8511 Japan
a r t i c l e in fo
abstract
Available online 29 September 2010
Making decisions on strategic investments, such as early stage manufacturing technology (MT), is a complicated task. Early stage technologies are usually costly, and surrounded by uncertainty. The potential benefits are often hard to quantify prior to implementation. Thus, how could managers make good decisions in a high-risk, technically complex business when the information they need to make those decisions comes largely from the project champions who are competing against one another for resources? Traditionally, in this problem domain, decisions are made based upon gut-feeling and past experience, sometimes with the support of some multi-criteria decision-support tools. The criteria evaluation process is very subjective and relies heavily on managers’ experience, knowledge, as well as intuition. Thus, the evaluation approach is often not effectively carried out as there is lack of visibility and traceability in the decision making process. The impact of this scenario is that managers are not confident that resources are being optimised and applied to a mixed portfolio of projects to maximise benefits. This paper proposes a marginal analysis directed branch and bound approach for evaluating and selecting early stage manufacturing technology (MT) projects. A case study is used to demonstrate the application of the proposed approach. Implications of the proposed approach to practitioners and academia are discussed and future research outlined. Crown Copyright & 2010 Published by Elsevier B.V. All rights reserved.
Keywords: Decision support systems Early stage technologies Marginal analysis Visualisation
1. Introduction Many early stage manufacturing technologies (MT) are of strategic importance and may create future competitive opportunities. It is a challenge for manager to clarify the right manufacturing technology alternatives as the number of technologies is increasing and the technology are becoming more and more complex (Torkkeli and Tuominen, 2002; Shehabuddeen et al., 2006). Therefore, managers need better methods that can evaluate the strategic value of MT investment when the future is uncertain (Auerswald et al., 2005). This paper defines early stage as the early development of fundamental manufacturing processes or technologies. Many researchers have developed approaches to support technology evaluation and selection. The evaluation and selection procedures have attracted the interest of a range of different tools and techniques (Branke et al., 2007). Several common elements were apparent, Henriksen and Traynor (1999) categorizes these methods and techniques into the following categories:
n
Corresponding author. E-mail addresses: [email protected] (K.H. Tan), [email protected] (J. Noble), [email protected] (Y. Sato), [email protected] (Y.K. Tse).
(a) unstructured peer review, (b) mathematical programming (LP, NLP, etc.), (c) economic models (IRR, NPV, etc.), (d) decision analysis (MAUT, Decision Trees, etc.) interactive methods (Dephi, Q-sort, etc.), (e) artificial intelligence (Expert Systems, CBR, Fuzzy logic, etc.), and (f) portfolio optimisation. However, some of these methods are so mathematically elaborate that it is difficult for managers to use them in practice. Loch and Kavadias (2002) pointed out that existing selection models proposed in the literature are highly complex, not transparent in application, and as a result, have not been commonly used in management practice. Often, determining which technology to pursue combines both quantitative and qualitative data that tend to stretch over an extended time horizon before a final decision is made. One potential pitfall is that both during and after the decision process the rationale for specific decisions can become unclear and difficult to defend. A visual decision path that captures the logic behind the variety of decisions made over the course of a technology adoption process can provide both the decision support and corporate memory necessary to ensure success in the future. There are numbers of literatures proposing decision support models for solving technology investment problems (Khouja, 2005; Sriram and Stump, 2004; Debo, et al., 2005; Wallenius et al., 2008), but only few models have been included for representing
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a transparent decision pathway of decision making process. The importance of a visual representation to support decision making has been emphasized by many researchers (Klein and Thomas, 2009; Caridi et al., 2009; Lohse et al., 1994; Tufte, 1990; Foil and Huff, 1992; Eden and Ackerman, 1998; Tan and Platts, 2003). They argue that visualisation transforms raw data into pictures that people can understand quickly, and it is a form of knowledge representation. Managers perform better when their problem solving processes are adapted to a visual problem representation (Vessey, 1991). Mckim (1972) sees visualisation as a ‘visual’ vehicle of thought to assist managers in making decision. Foil and Huff (1992) point out that a visual representation such as a mental map can provide new ways of examining and improving managerial judgement. A mental map is in itself a useful form for helping managers to make sense of complexity. Visual representation can both simplify ideas and facilitate the transmission of complex ideas from individual to individual and unit to unit. Most importantly, visual representation helps to divorce ideas from specific speakers, making them more accessible to debate and modification. Moreover, they argue that visual representations are of potential interest to managers because they are a means of displaying graphically the firm’s current strategic position, as various observers understand it, and because they hold the promise of identifying alternative routes to improving that position. When setting out on the task of technology evaluation, managers have available to them several existing visual techniques/models to aid structuring and analysing information, monitoring decision proceses, and communicating strategic direction. These techniques are Influence diagrams, DCF, Decision trees, Roadmapping, and Portfolio matrices. By and large, there are limitations with these techniques when it comes to decision of visualisation support:
Overly simplistic—DCF and Decision trees may not be suitable for complex evaluation involving multiple criteria
Qualitative—Roadmapping and Influence diagrams are helpful
to develop a structured network of thought that leads to better insight but network relationships are difficult to quantify. Lack of visibility—Portfolio matrices provide little ‘traceability’ on the decisions that lead to the matrix construction.
Clearly, the visual support for technology evaluation provided by the above approaches is inadequate. And this is where managers begin to feel uncomfortable. They ask: I have a range of alternative technologies to invest in, but how could I visualise the decision process to improve my confidence in the decision made? Thus, managers need a better way to make decisions, they need traceability of the decisions made in the process to be presented in a meaningful format. Lohse et al. (1994) pointed out that appropriate visual representations can facilitate problem solving and discovery by providing an efficient structure for expressing data. By being able to visualise the path of decisions, managers are able to consider and give a fair appraisal of their merits. This paper reports on the on-going work in a project to apply the marginal analysis-based branch and bound approach as the basis of directing the selection of MT so that an integrated risk/ multi-criteria decision path can be developed. The proposed marginal analysis approach is a sequence of transparent steps to provide clarity of thought into the evaluation and selection process that managers undertake. In the following sections, the development of the proposed approach is explained. A case is used to illustrate the application of the proposed approach. The results are described and implications of this research for industrialists and academics are discussed.
2. Marginal analysis directed branch and bound An approach for determining the effect of taking a given decision path (Noble and Tanchoco, 1993, 1995) is needed to analyze the trade-off between different decision paths. When considering the development of a decision path to support selection of early stage technology it quickly becomes obvious that most decisions are incremental in nature (Eilon, 1984; Tan and Noble, 2007; Tse et al., 2009). Marginal Analysis Directed Branch and Bound was first proposed by Noble and Tanchoco (1995) for directing the design process of a complex material handling system, in which a combined economic and performance justification path is developed to the engineers for final judgment. Tse et al. (2009) has adopted Marginal Analysis Directed Branch and Bound in mitigating the product quality risk in global supply chain by considering the incremental benefit for configuring firm’s supply network. Moreover, Tan and Noble (2007) proposed a Marginal Analysis Directed throughput analysis approach, which employs incremental calculus (Eilon, 1984) for evaluating the relationships among various decision variables in a performance model quantitatively. For example, a XYZ technology-based system has capabilities that tend to be a function of discrete decisions (whether they are small parameter level changes or system level changes) and the design process tends to reflect this since decisions occur in a serial manner. Therefore, it would be desirable for decision makers to be guided down the most promising path if a method of projecting the effect of each discrete decision were available. Fig. 1 illustrates the concept of a decision path. In Fig. 1, all priority and risk criteria are explored sequentially starting from the criteria that is weighted the greatest. The approach proposed in this paper consists of determining an initial instance of the solution, then as the decision process proceeds different solution changes are explored to determine which provides the greatest marginal benefit. Overall, the goal of the marginal analysis guided approach is to develop a decision path that can serve as the basis of a combined multi-criteria (financial, strategy, health/safety, supply security, quality, importance, deployment, novelty, intellectual property, customer service) and risk justification argument. The decision path provides the reasoning from a marginal analysis perspective as to why a specific aspect of the technology was selected. The procedure requires that the decision maker determine the initial selection criteria. The nature of the procedure enables the decision maker to re-evaluate the selection criteria and to conduct marginal analysis on the effect of changing selection criteria. This is desirable since it is possible during the course of a selection process that the decision maker might modify the selection criteria due to insight gained. The result of evaluating not only the technology alternatives, but also the decision criteria, is that the final decision will be fully justified. An additional benefit of justifying each decision (incrementally versus end-of-process) is that the rationale behind each decision is captured (decision path) and can then be used as the basis of an overall justification argument. Alternatives are evaluated using a branching and bounding process with respect to each alternatives performance for the selection criteria (see Fig. 2). In the process of evaluating an alternative it is common for new alternatives to be generated incrementally. This incremental alternative generation is analogous to the branching that occurs when exploring the solution space of a combinatorial optimisation problem (Land and Doig, 1960). The decision maker then utilizes the results of the marginal analysis to bound the solution back toward the required performance. Hence, the marginal analysis serves as the basis for exploring the solution space via a branch and bound approach.
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Selection Criteria Risk Financial Strategy
Incremental Changes in Criteria
HSE
+0.1
Supply Security Quality
+0.2 -0.2
Importance Deployment Novelty IP Potential Customer Fig. 1. A technology selection decision path.
Branching
Bounding
Criteria 1 MT3 MT1 MT4 MT2
MT Alternatives
MT4 MT2 MT1 MT3 MT…
Ranked Alternatives
Criteria 2
Performance Analysis Marginal Analysis
Decision
Criteria n
Justification
Fig. 2. A branch and bound process.
3. A decision path example The following presents an example to illustrate the application of the marginal analysis approach. Company ABC must make a decision concerning the adoption of a new product identification/tracking technology. Initially, there are two different vendors and configurations associated with implementing this technology, both have different performance levels compared with a set of evaluation criteria (Table 1). The company has determined the overall priority ranking between the four primary decision criteria (1st – System flexibility, 2nd – Compatibility with existing systems, 3rd – Implementation cost, and 4th – Vendor reputation). The marginal analysis is initiated by establishing Alternative 1 as the baseline, then evaluating Alternative 2 relative to Alternative 1 (Table 2). As can be seen, Alt 2 performs slightly better ( +4.4%) with respect to System flexibility (the highest ranked decision criteria), but it is significantly worse with respect to Compatibility ( 30.4%). Its performance on Compatibility alone is sufficient to bound it from further consideration, so the fact that it also performs poorly on Cost ( 10.4%) and somewhat better
Table 1 Primary decision criteria. Criteria priority rank
Alt 1 Alt 2
1st
2nd
3rd
4th
Flexibility
Compatibility
Cost
Vendor
45 47
23 16
67 60
45 50
( +11.1%) with respect to Vendor does not influence the decision to remove Alternative 2 from further consideration. At this point Company XYZ explores other potential providers of the technology from which Alternatives 3, 4, and 5 are generated (or branched) as shown in Table 3. The marginal analysis proceeds by comparing Alterative 3–Alternative 1 (Table 4). Alt 3 is slightly better with respect to the top ranked criteria ( +11.1%) and the lowest ranked criteria ( +6.7%), Flexibility and Vendor, respectively. However, it is significantly better ( + 30.4%) with respect to Compatibility. The only concern that is raised is that it is slightly less desirable
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Table 2 Marginal analysis on Alt 1 and 2.
Flexibility Compatibility Cost Vendor
Table 5 Marginal analysis on Alt 4 and Alt 3.
Alt 1
Alt 2
Alt 2 vs Alt 1
45 23 67 45
47 16 60 50
4.4% 30.4% 10.4% 11.1%
Ok Not acceptable Not acceptable Good Reject Alt 2
Table 3 Other alternatives. Criteria priority rank
Alt 3 Alt 4 Alt 5
Alt 4 vs Alt 3
48 30 66 46
4.0% 0.0% 1.5% 4.2%
Concern Ok Concern Reject Alt4
Table 6 Marginal Analysis on Alt 5 and Alt 3. 1st
2nd
3rd
4th
Flexibility
Compatibility
Cost
Vendor
50 48 55
30 30 30
65 66 66
48 46 55
Table 4 Marginal analysis on Alt 3 and Alt 1.
Flexibility Compatibility Cost Vendor
Flexibility Compatibility Cost Vendor
Alt 4
Alt 3
Alt 3 vs Alt 1
50 30 65 48
11.1% 30.4% 3.0% 6.7%
Good Good Concern Ok Accept Alt 3
( 3.0%) with respect to Cost. However, the management team deems this a valid trade-off and accepts Alternative 3 over Alternative 1. This illustrates the role of the marginal analysis, branch and bound, approach. It is not designed to prescribe solutions, but rather to assist decision makers in supporting decision making, capturing decision logic, and representing the branching and bounding decision process. Alterative 4 is compared with Alternative 3 (Table 5). The result of the comparison reveals that across the board Alternative 4 has little additional to offer over Alternative 3. On three of the four criteria is equal or worse and only on the third ranked criteria, Cost, is it slightly better ( + 1.5%). Alternative 4 is bound from further consideration and therefore rejected. Finally, Alternative 5 is compared with Alternative 3 (Table 6). The marginal analysis reveals that for all criteria Alternative 5 is better or equal to Alternative 3. Therefore, Alternative 5 is accepted and is considered to be the best overall alternative currently available for implementing this new technology. Fig. 3 illustrates the overall decision path as a result of the marginal analysis of the different technology alternatives. Presenting the branch and bound decision logic in this manner provides a visual of what decisions were made and the rationale for making them. The above example of a marginal analysis guided selection process has illustrated the development of a decision path as a means to support the evaluation and selection of a technology. The approach provides a mechanism for analysing the impact of each decision trade-off on overall technology performance so that a quantitative analysis of the reasoning behind each decision results. The resulting decision path provides a strong basis for justifying the selection of a specific technology. It is important to stress that the marginal analysis approach is different from the widely known pairwise comparison approach (Saaty, 1996). The proposed marginal analysis provides the basis
Flexibility Compatibility Cost Vendor
Alt 5
Alt 5 vs Alt 3
55 30 66 55
14.6% 0.0% 1.5% 14.6%
Good Good Ok Good Accept Alt 5
for conducting pairwise comparisons and developing a sequential decision path. A full pairwise comparison is not necessary (and therefore, waste valuable decision maker time) if managers can bound alternatives from further consideration, which is possible when incrementally justifying each additional performance increment using marginal analysis.
4. Case study A case study conducted by Tan et al. (2006) is used to further illustrate the applicability of the proposed marginal analysis approach. The case was conducted in Company Pharma, a worldleading manufacturer in the pharmaceutical industry. Traditionally, prioritisation and funding decisions for proof of concept proposals on significant, large MT projects in Company Pharma were mainly made by a central committee. The approach, however, could lead to gaps in the consistency of decision making. Decisions were reached mainly through consensus and the criteria used by the committee members to evaluate a proposal were not explicitly known and documented. In the previous work, a hybrid intelligent decision support system is developed for supporting managers in making timely and optimal MT investment decision. The intelligent system adopts Fuzzy ARTMAP (FAM) neural network modelling techniques for retrieving the historical evaluation results. FAM is employed to guide the retrieval process of the adapted case (i.e. the MT investment project prioritisation and evaluation) in the repository. For a full description of the hybrid intelligent system decision mechanism, please refer to authors’ former work (Tan, et al., 2006). However, the intelligent system approach has a major drawback in decision visibility, i.e., the pattern learning mechanisms and the retrieval process are ‘‘blackbox’’ mechanisms. The decision makers of Pharma cannot know why this decision is made, what factors they are considering, and what trade-offs has been taken. As a result, the management team was not confident that the resources had been optimised, as the decision process is lack of transparency and traceability. Thus, the marginal analysis approach is proposed to direct the justification of MT investment decision making. The visibility of decision making is improved by providing a clear justification pathway in which each criteria trade-offs are clearly illustrated to the committee members. The following example illustrates how the marginal analysis approach can be applied at the MT Concept Phase for Company Pharma. Table 7 represents 10 candidate MT projects with their
K.H. Tan et al. / Int. J. Production Economics 131 (2011) 15–21
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Alt 1
Alt 2
Alt 3
Alt 4
Flexibility
45
4%
11%
-4%
15%
Compatibility
23
-30%
30%
0%
0%
Cost
67
-10%
-3%
2%
2%
Vendor
45
11%
6%
-4%
15%
Reject
Accept
Reject
Accept
Alt 5
Fig. 3. Marginal analysis decision path.
with respect to the ratio of change between two pairs of performance criteria.
Table 7 Set of candidate MT projects. MT projects
Priority
Risk
1 2 3 4 5 6 7 8 9 10
63.4 64.3 72.9 54.9 69.4 40.4 55.2 40.1 31.7 40.6
44.8 52.8 59.2 34.4 44.8 44.8 30.4 12.0 22.4 67.2
respective Priority and Risk scores. The priority and risk scores are determined in the evaluation module, which employs the FAM techniques to access the criteria performance of each potential MT project. The marginal analysis approach involves a series of incremental priority and risk calculations, which are explained as following:
Incremental priority and incremental risk—determining the
incremental priority and risk allows for a relative measure of how each performance measure changes with respect to each alternative. Given that the absolute measures of priority and risk have no real meaning (unlike cost or profit that have a monetary unit)—an incremental approach provides a more robust basis for conducting the analysis Difference between incremental priority and incremental risk— calculating the difference between the incremental priority and incremental risk provides an aggregate measure of how multiple measures of goodness combine (in our example we assume that they are weighted equally, but if they were not then the difference could be weighted accordingly). For example, a positive incremental change in priority is desirable and a negative incremental change in risk is desirable, hence when both priority and risk have a relatively equivalent positive incremental change—then the net effect is that there is not an overall incremental benefit for the challenger alternative. Incremental priority/Incremental risk—this ratio provides additional insight into the magnitude of the overall incremental changes, as such it is an auxiliary measure and not crucial to a marginal analysis. It is also only effective when considering pairs of performance criteria. Its role is to provide a relative measure of the marginal differences between two alternatives
Table 7 presents marginal analysis results based on justifying additional priority with respect to a changing risk profile and the resulting selection decision. In this case Project 9 has the lowest Priority score ¼31.7 and on this basis alone it is rejected. The marginal analysis then proceeds by comparing the next highest Priority score ( ¼40.1 for Project 8, with an associated Risk score ¼ 12.0). The marginal change in Priority score is 26.5% ¼(40.1–31.7)/ 31.7, with a corresponding marginal change in Risk score ¼ 46.4%. This results in a marginal difference of 73%¼26.5– ( 46.4) and a ratio of Incremental Priority to Incremental Risk of 57% ¼26.5/ 46.4. Both a positive marginal difference and a negative Inc Priority/Incr Risk ratio reveals an improvement compared with Project 9. However, based on Project 8 having a low overall Priority score it is classified as a ‘‘maybe’’ project. Fig. 4 illustrates the resulting decision path with the Incremental Priority, Incremental Risk, and Differences between Incremental Priority and Incremental Risk given. Table 8 provides the sequential marginal analysis calculations and resulting actions for Company Pharma. As the MT analysis continues into another MA evaluation, a more detailed marginal analysis is conducted and a corresponding decision path is generated (Fig. 5). In this case the projects ranked as ‘‘Maybe’’ and ‘‘Yes’’ are further analyzed sequentially starting from the lowest overall Priority score and considering the highest individual Priority criteria first. As the more detailed analysis proceeds projects will drop out of contention based on their marginal contributions. As shown in Table 9, the Pharma managers identified the sequence of conducting marginal analysis. The sequence is based on the importance of the criterion for selecting the MT investment. In each marginal analysis, (i) the incremental criterion performance (obtained from the intelligent system in previous work), (ii) the difference between incremental priority and incremental criterion, and (iii) the ratio of incremental priority and incremental criterion are calculated. In this case, there are eleven marginal analysis planed to be conducted, but not all the marginal analysis will be gone through since the final solution usually is obtained before all the marginal analysis are processed. The results indicate that the proposed marginal analysis approach could create a powerful decision path that helps Pharma managers to gain insight into MT evaluation and selection process. It is easy to apply, and the transparency of the decision process helps to address one of the major limitations of existing
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Differences between Incremental Priority and Incremental Risk 7
+12
5
4
-32
9
-27
1
8 +73
-16
+84
Accept
+23 3
2
Consider
-273
Reject 6 -49 10
Solid line = Inc Priority Dashed line = Inc Risk
x
Fig. 4. Illustrative decision path for technology selection—risk marginal analysis.
Table 8 Marginal analysis on priority and risk for 10 candidate MT projects. MT projects
Priority
Risk
Incr. priority (%)
Incr. risk (%)
Difference between Incr. priority – Incr. risk (%)
Incr. priority/Incr. risk (%)
9 8 6 10 4 7 1 2 5 3
31.7 40.1 40.4 40.6 54.9 55.2 63.4 64.3 69.4 72.9
22.4 12.0 44.8 67.2 34.4 30.4 44.8 52.8 44.8 59.2
0 26.5 0.7 0.6 35.1 0.5 15.0 1.4 7.9 5.1
0 46.4 273.3 50.0 48.8 11.6 47.4 17.9 15.2 32.1
73 273 49 84 12 32 16 23 27
57 0.3 1 72 5 32 8 52 16
Action
Reject Maybe Reject Reject Yes Yes Maybe Maybe Yes Maybe
Selection Criteria Risk Financial
Incremental Changes in Criteria
Strategy
Accepted Projects
HSE
P4 +0.1
P4
P7
P7
P1
P5
P1
P5
P5
P3
Supply Quality
+0.0 -0.2
Importance
P8
Deployment
P2 Rejected Projects
Novelty IP Customer Fig. 5. Illustrative decision path for technology selection—second phase.
selection model i.e. not transparent (Loch and Kavadias, 2002). The visibility of the decision process allows Pharma managers to trace back how various different decisions were explored to determine which project provides the greatest marginal benefit.
Nevertheless, the marginal analysis approach does require the decision maker to provide for some quantification of the benefits/ costs associated with an alternative, this does require more time and effort. The marginal analysis approach does not prescribe
K.H. Tan et al. / Int. J. Production Economics 131 (2011) 15–21
Table 9 Sequence of conducting marginal analysis in pharma case study. Sequence of conducting marginal analysis
Criterion
1 2 3 4 5 6 7 8 9 10 11
Risk Financial Strategy Health/Safety (HSE) Supply Quality Importance Deployment Novelty Intellectual property (IP) Customer
a solution (which some might consider a limitation), but rather it provides rationale for why a decision is made.
5. Discussions and summary The above case study has demonstrated the possibility of using a marginal analysis approach to support managers in the difficult task of evaluating and selecting manufacturing technology. The marginal analysis guided approach develops a decision path as a means to support MT evaluation. A marginal analysis is used to evaluate alternatives with respect to all decision criteria. Then a decision path is developed that captures the logic behind the variety of decisions made over the course of a technology adoption process. The decision path provides the reasoning from a marginal analysis perspective as to why a specific aspect of the technology was selected, thereby, providing the corporate memory necessary to ensure success in the future. A marginal analysis approach is a systematic approach for transforming opaque decision problems into transparent problems by a sequence of transparent paths. In other words, it offers the possibility to managers of replacing confusion by clear insight into justification. The decision path provides new clarity to the analysis process, allowing visualisations that are both easily understandable and traceable. The path helps in understanding and permits managers to assure both effectiveness and efficiency in evaluation and justification. The power of the marginal analysis transforms opaque decision situation into transparent ones. Thus, it helps to increase transparency in the early stage MT evaluation process and ensure managers the quality of thinking behind the evaluation was consistent. Although encouraging initial results have been achieved, the proposed approach needs to be further validated. It is planned that continued application of the approach in a
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