6


A Framework for Decision Making

INTRODUCTION

Chapter 5 presented four alternatives available to Bayer: (1) continuing with the existing process, (2) adopting an alternative chemical process not involving MIC, (3) using an alternative process for MIC production that would consume MIC immediately and thus not require storage, and (4) reducing the volume of stored MIC and the risks of transporting MIC from one facility within the site to another by rearranging process equipment. Table 6.1 summarizes how these four alternatives compare along several key performance features, or “attributes”, some of which were captured in Table 5.3 and some of which are additional attributes that could have been considered an analysis of trade-offs. An “X” indicates that the given manufacturing approach is superior to the others along the given attribute. As is clear from this comparison, none of the alternatives is superior to all the others along every attribute. For example, the Institute process, while posing higher risks due to the volume of MIC stored, generates less wastewater than the original non-MIC process used to produce carbaryl, and also has many cost advantages

Given that no alternative clearly dominates the others, the question arises as to what decision-making framework could be used to identify the “best” choice. Benefit-cost analysis (BCA), as described in Chapter 5, assigns dollar values to the different attributes, based on their expected values, adds together all the benefits and costs for each alternative, then chooses the one with the highest net benefits. While Chapter 5 illustrated that valuing uncertain attributes can be difficult in many cases, BCA has been used extensively for many years by government agencies, and certain conventions have been adopted (such as using the value of a statistical life to evaluate fatality risks) to deal with some common attributes.



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6 A Framework for Decision Making INTRODUCTION Chapter 5 presented four alternatives available to Bayer: (1) continuing with the existing process, (2) adopting an alternative chemical process not involving MIC, (3) using an alternative process for MIC production that would consume MIC immediately and thus not require storage, and (4) reducing the volume of stored MIC and the risks of transporting MIC from one facility within the site to another by rearranging process equipment. Table 6.1 summarizes how these four alterna- tives compare along several key performance features, or “attributes”, some of which were captured in Table 5.3 and some of which are additional attributes that could have been considered an analysis of trade-offs. An “X” indicates that the given manufacturing approach is superior to the others along the given attribute. As is clear from this comparison, none of the alternatives is superior to all the others along every attribute. For example, the Institute process, while posing higher risks due to the volume of MIC stored, generates less wastewater than the original non- MIC process used to produce carbaryl, and also has many cost advantages Given that no alternative clearly dominates the others, the question arises as to what decision-making framework could be used to identify the “best” choice. Benefit-cost analysis (BCA), as described in Chapter 5, assigns dollar values to the different attributes, based on their expected values, adds together all the ben- efits and costs for each alternative, then chooses the one with the highest net benefits. While Chapter 5 illustrated that valuing uncertain attributes can be diffi - cult in many cases, BCA has been used extensively for many years by government agencies, and certain conventions have been adopted (such as using the value of a statistical life to evaluate fatality risks) to deal with some common attributes. 113

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114 USE AND STORAGE OF METHYL ISOCYANATE (MIC) AT BAYER CROPSCIENCE TABLE 6.1 Multiple Attribute Analysis of Alternative Carbamate Pesticide Production Processes (1)—Existing Institute Process (2)—Non-MIC Processes (3)—Process Producing Gaseous MIC Consumed Immediately (4)—Alternative MIC-based Process Arrangements, Reducing Onsite Storage Manufacturing Approach Potentially Important Attributes External/Regulatory Pressures (1) (2) (3) (4) Liability for harm to surrounding communities X X Community acceptance X ? Liability for worker injury due to MIC exposure X ? Liability for worker injury due to dust exposure X Wastewater disposal requirements X X Length of time for regulatory approval X Internal/Cost Pressures Product purity X X Cost and availability of chemical feedstocks X X Capital costs X Equipment O&M costs X Costs of safety equipment ? ? ? ? Process corrosivity X X Process yield X Availability of in-house expertise X X Previous experience with large-scale production X NOTE: “X” indicates that the given pesticide manufacturing approach performs better than the others along the given attribute. “?” indicates that information available to the committee was insufficient to evaluate the approach along the given attribute. The complex, multi-attribute decision-making Bayer and the legacy owners faced when modifying the MIC and carbamate-pesticide production processes is a challenge that will be familiar to many in the chemical industry. Incorporating risk considerations into these analyses can be particularly challenging as the final decision may affect individuals and organizations beyond the company itself, e.g., reducing or increasing requirements for local emergency responders. In fact, several experts in chemical engineering have suggested that the lack of effective methods for analyzing such trade-offs is a major barrier to the widespread use of inherently safer processes (ISP; Khan and Amyotte, 2004). With this in mind, the committee chose to present one possible approach for analyzing these and other trade-offs, while recognizing other methods are also possible.

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115 A FRAMEWORK FOR DECISION MAKING The alternative to BCA described in this chapter is multi-attribute utility theory (MAUT), one of a suite of methods—or decision aids—developed to assist with complex decision-making involving multiple stakeholders. MAU (described in more detail in the next section) keeps the attributes separate throughout the process and emphasizes the multi-dimensional nature of the decision process. The method recognizes explicitly that different people could assign different values to the different attributes—both in how strongly the person weights different attributes when making the final decision and possibly even in whether the attri - bute is considered to be positive or negative. By forcing the user to confront the multiple attributes and decide their relative importance, MAU may help clarify a difficult decision (or at least the difficult trade-offs involved). However, MAU is still a relatively unfamiliar process for most companies, which is a disadvantage when it comes to applying the method in real-world cases. MULTI-ATTRIBUTE UTILITY THEORY The need for trade-offs among conflicting objectives under uncertainty is pervasive in many decisions faced by businesses, government agencies, and other organizations. Decision scientists have conceived formal methods for balancing such trade-offs and have established mathematically that these methods yield the choice that maximizes the utility to the decision maker. Many approaches to multiple-criteria decision making are available from the fields of economics and decision analysis, including, for example, analytic hierarchy process (AHP) analysis and fuzzy set theory. Overviews of methods for multi-attribute or crite - ria decision analysis can be found in Belton and Stewart (2002), Figueira et al. (2005), and DeBrucker et al. (2012). There are strengths and weaknesses for each methodology, and these should be evaluated and understood by any user prior to application. MAU, one example of these analysis methods, assigns a numeric value (utility) representing a specific decision maker’s preferences to each of the (multi-attribute) outcomes of each choice under consideration. The discussion below illustrates how MAU could be employed in supporting decision making in the chemical manufacturing industry, while recognizing that the AHP, fuzzy set theory, and other decision-making frameworks also could be considered. MAU is not a new idea to the chemical community. In 1995, the Center for Chemical Process Safety (CCPS) published a book that suggested that MAU and other decision aids could be used to support process safety assessments (CCPS, 1995). However, these decision aids, although employed regularly in other busi - nesses, have yet to take hold in the chemical process industry. The CCPS’s obser- vation from its 1995 book remains true today: “Decision aids have been applied only to a very limited extent in risk decision problems in [the chemical process] industry.” The CCPS indicates key obstacles to adopting these tools include lack of familiarity with the tools among chemical process industry decision makers and fear that the methods are either too simple or too costly. Nonetheless, the

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116 USE AND STORAGE OF METHYL ISOCYANATE (MIC) AT BAYER CROPSCIENCE committee believes that MAU and/or other techniques from decision analysis could prove highly valuable for strengthening the integration of safety concerns into decision making in the chemical process industry. Use of these techniques could benefit not only those at risk due to safety breaches but the industries themselves, as the techniques can lead to the identification of profitable safety solutions that otherwise may have been overlooked. These tools could also assist in strengthening the relationship between companies and communities by provid- ing a framework for requesting and receiving input from external stakeholders. Such input may help identify overlooked concerns or areas where additional com- munication and outreach could be beneficial for maintaining a safe environment and a positive relationship with the external stakeholders. This section first provides an overview of MAU theory and its use for analyz- ing trade-offs in complex decision problems under uncertainty. Then, it describes some limitations of the inherent safety indexes currently being used. Finally, it suggests an approach for employing MAU concepts in ISP assessments focused on improving the choice of chemical manufacturing process. However, as noted previously, MAU theory is not the sole method of approaching these difficult questions, although it is hoped that the discussion here will demonstrate the potential utility of these types of decision aids. Decision Sciences and MAU Models: Background The field of decision sciences emerged from the axioms of rational choice first posed by mathematician John von Neumann and economist Oskar Morgenstern in 1947. Von Neumann and Morgenstern proved that if an agent (decision maker) has preferences with four specific characteristics (completeness, transitivity, con - tinuity, and independence), then there must exist a mathematical equation known as a utility function such that the decision makers’ preferences can be captured by maximizing the equation’s expected value (von Neumann and Morgenstern, 1947; French, 1986). The utility function is usually designated as U(x), where x=(x1, . . ., xn) is a vector representing how well a particular decision option satisfies each of n attributes important to the decision maker. Because the utility function includes multiple attributes, it provides a framework to consider trade- offs among those attributes (for example, among cost, risk, and performance). Since von Neumann and Morgenstern published the axioms of rational util- ity, decision scientists have developed systematic methods for characterizing utility functions (see, e.g., Keeney and Raiffa, 1976; French, 1986; Raiffa et al., 2002). Many different mathematical forms for utility functions have been con - ceived. Each functional form makes certain assumptions about independence of and/or interactions among the conflicting objectives. For example, the simplest type of utility function is linear in all the attributes:

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117 A FRAMEWORK FOR DECISION MAKING n U ( x1 ,…, x n ) = ∑ kiU i ( xi ) (6.1) i =1 In this function, the Ui(xi) represents individual utility functions for each of the decision maker’s n objectives, and the ki represents weights assigned to the differ- ent objectives. These weights reflect the value to the decision maker of an option that offers the best possible outcome along objective i, while setting all other objectives at their worst possible values. For this type of utility function to accu - rately characterize preferences, certain strict independence conditions must hold (for details, see Clemen and Reilly, 2001, and other texts on decision theory). In essence, the decision makers’ preferences for one attribute cannot change as levels of some other attribute change, even if the outcomes along attributes are uncertain. Another type of utility function, requiring weaker independence condi- tions, is the multiplicative function, expressed as follows: 1 + kU ( x1 ,…, x n ) = ∏ [kkiU i ( xi ) + 1] (6.2) Several textbooks explain in detail the methods for determining which func - tional form is appropriate for the decision situation at hand (e.g., Keeney and Raiffa, 1976; Bunn, 1984; Clemen and Reilly, 2001). Formal courses in decision analytic methods are offered in many business and engineering schools. In gen - eral, the methods involve the following steps: • Identifying the fundamental objectives of the decision makers and attri- butes that can be used to represent progress along each objective; • Eliciting individual utility functions for each attribute (the Ui(xi) in the above equations)—functions that may be linear, concave, or convex, depending on the decision makers’ risk tolerance; • Testing the attributes for independence to determine the appropriate func- tional form (e.g., linear, multiplicative, linear-multiplicative) for the MAU model; and • Eliciting the scaling constants (the k and ki in the above equations) for the multi-attribute function. The resulting MAU model will be specific to the decision makers upon whose values it is based, and as a practical matter, it may be difficult for the decision makers to identify their preferred attributes. Adding to the complexity, even if different interest groups can agree on the attributes that should be con- sidered in a decision, they may prefer different trade-offs among the attributes (e.g., willingness to trade cost savings for decreased health hazard) or have difficulty assigning values to the trade-offs, and they may exhibit different risk tolerances (willingness to gamble on outcomes with the potential for high payoff

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118 USE AND STORAGE OF METHYL ISOCYANATE (MIC) AT BAYER CROPSCIENCE but also high risks of loss). As a result, different utility functions (with different single-attribute utility functions and scaling constants) are needed to reflect the preferences of groups with different values. It is worth noting that some efforts to develop indexes for ISP assessments assume that there is a single correct pro - cess design from an inherent safety perspective. However, groups with disparate values may have differing utility functions that may lead to differences in the preferred manufacturing process. Although multiple MAU models may be needed to reflect different groups’ values, these models can be extremely useful in guiding negotiations among groups in conflict (Raiffa et al., 2002). As Clemen and Reilly (2001) point out, “Understanding trade-offs can be crucial for making progress in negotiation set - tings.” By making values explicit, MAU models can reveal similarities and differ- ences in the value structures of groups. As an example, MAU models can be used to identify trade-offs between risk reductions and cost reductions. Keeney and McDaniels (1992) show that a MAU function developed to inform strategic deci - sions of the British Columbia Hydro and Power Authority (BC Hydro) implies that the company decision makers value a hectare of wilderness lost at $2,500 (see Box 6.1). That is, at least $2.5 million in economic benefits would be needed to justify a company choice that would damage a thousand hectares of wilderness. Disagreements on certain features of a decision do not always result in different preferred alternatives. That is, a MAU process may reveal that groups in conflict might make similar choices in spite of their different values. When groups do differ in their preferred alternatives, a MAU model can highlight the main features of the decision about which groups disagree, and this may facilitate compromise. Raiffa et al. (2002) provides detailed guidance on the use of MAU models in negotiations among parties in conflict. Decision analytic methods are now widely employed in business and other applications, from fire department operations planning to nuclear power facility siting (Keeney and Raiffa, 1976; von Winterfeldt and Edwards, 1986; Keefer et al., 2004). The Decision Analysis Society, a specialty group within INFORMS (Institute for Operations Research and the Management Sciences), organizes regular conferences to exchange both academic and practical information on decision analysis and publishes a journal, entitled Decision Analysis. Keeney and Raiffa (1976) and von Winterfeldt and Edwards (1986), along with more recent issues of Decision Analysis, provide many more practical examples of applica- tions of MAU theory to decision making. The example provided in Box 6.2 shows that firms can “discover” new information about their preferences by using MAU—in this case, an alternative that was preferred in four out of five attributes turned out not to be the optimal choice, because of the high value the firm placed on the fifth attribute. Note that MAU provides a framework for making rational decisions; it does not necessarily describe how people actually make decisions. In fact, substantial research has indicated that individuals often do not act in accordance with the

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119 A FRAMEWORK FOR DECISION MAKING BOX 6.1 MAU Model Informs Strategic Decisions in the Hydropower Industry The British Columbia Hydro and Power Authority (BC Hydro) has used MAU to inform strategic decisions since the late 1980s, when the company commissioned Ralph Keeney and Tim McDaniels to support a comprehensive reassessment of the company’s planning processes. Keeney and McDaniels constructed a MAU model that BC Hydro then used to support decisions related to capital equipment upgrades, supply planning, and other corporate strategic issues. Keeney and McDaniels (1992) and Clemen and Reilly (2001) summarize how the MAU model was developed and the values revealed by the model. First, Keeney and McDaniels interviewed BC Hydro’s key decision makers to identify their objectives and sets of attributes for measuring progress toward those objectives. The result was the set of six funda­ mental objectives and 22 attributes for measuring performance shown in Table 6.2. Next, Keeney and McDaniels assessed a MAU function for combining all of these attributes into a summary measure of utility for the company. The result was a combination linear­multiplicative utility func­ tion (see Keeney and McDaniels, 1992). This function then was pro­ grammed into a spreadsheet to allow BC Hydro’s managers to assess the overall utility of various decision alternatives. Keeney and McDaniels used the resulting MAU function to illustrate the dollar value, from the company’s perspective, of various attributes. For example, they showed that the value of a hectare of wilderness, from the company’s perspective, is equivalent to $2,500, so that any process change that would cause such a loss but produce less than $2,500 in expected gain would not be worthwhile. Similarly, they showed that two power outages per year of 2 hours duration each to 20,000 large (com­ mercial and industrial) customers was equivalent to $83 million. Keeney and McDaniels observed that “if BC Hydro had opportunities to reduce expected outages of that nature at a cost less than $83 million, those opportunities would be good investments from the utility’s perspective.” After Keeney and McDaniels’ MAU decision support model was in place, BC Hydro’s Director of Strategic Planning commented, The structured set of objectives has influenced BC Hydro planning in many contexts. Two examples include our work to develop a decision framework for supply planning, and a case study of an investment to upgrade reliability. . . . Less obvious has been an evolution in how key senior planners view planning issues. The notion of a utility function over a range of objectives (rather than a single objective, like costs) is evident in many planning con­ texts. The specific trade­offs in the elicitation process are less important than the understanding that trade­offs are unavoidable in electricity utility decisions and that explicit, well­structured, informed trade­offs can be highly useful. (Keeney and McDaniels, 1992, p. 109, as quoted in Clemen and Reilly, 2001)

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120 USE AND STORAGE OF METHYL ISOCYANATE (MIC) AT BAYER CROPSCIENCE TABLE 6.2 Fundamental Objectives and Attributes for Measuring Progress along Objectives Described in Hydropower Case Study in Box 6.1 1. Maximize contribution to economic development 11. Minimize cost of electricity use (mills per kilowatt-hour in 1989 Canadian dollars) 12. Maximize funds transferred to government (annualized dividend payable) 13. Minimize economic implications of resource losses (cost of resource losses in 1989 Canadian dollars) 2. Act consistently with the public’s environmental values 21. About local environmental impacts 211. To flora (hectares of mature forest lost) 212. To fauna (hectares of wildlife habitat lost of Spatzizi Plateau quality) 213. To wildlife ecosystems hectares of wilderness lost of the Stikine Valley quality) 214. To limit recreational use (hectares of high quality recreational land lost) 215. To aesthetics (annual person-years viewing high voltage transmission lines in quality terrain) 22. About global impacts (generation capacity in megawatts that results in “fossil fuel” pollution) 3. Minimize detrimental health and safety impacts 31. To the public 311. Mortality (public person-years of life lost) 312. Morbidity (public person-years of disability equal in severity to that causing employee lost work time) 32. To employees 311. Mortality (employee person-years of life lost) 312. Morbidity (employee person-years of lost work time) 4. Promote equitable business arrangements 41. Equitable pricing to different customers (constructed scale, see text) 42. Equitable compensation for concentrated local impacts (number of individuals that feel they are inequitably treated) 5. Maximize quality of service 51. To small customers 511. Minimize outages (expected number of annual outages to a small customer annually) 512. Minimize duration of outages (average hours of outage per outage to small customers) 52. To large customers 511. Minimize outages (expected number of annual outages to a large customer annually) 512. Minimize duration of outages (average hours of outage per outage to large customers) 53. Improve new service (elapsed time until new service is installed) 54. Improve response to telephone inquiries (time until human answers the telephone) 6. Be recognized as public service oriented (constructed scale, see text) SOURCE: Keeney and McDaniels, 1992.

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121 A FRAMEWORK FOR DECISION MAKING BOX 6.2 MAU Informs Materials Choices in the Automotive Industry As an example of practical application of MAU to inform system de­ sign in industry, consider the example of choosing materials for automo­ bile frames and skins presented in Thurston (1990). Thurston designed a MAU model for use by a French automobile manufacturer working on long­range plans for its future vehicle fleets (those to be manufactured in the next 5­10 years). The company’s design engineers faced three options for vehicle frame­and­skin systems: 1. Traditional steel uni­body, 2. Internal steel frame with nonstructural external polymer composite skin, and 3. Steel and polymer composite frame with polymer composite skin. The design engineers were perplexed about which material choice to pursue, because although steel could be produced with the lowest operating cost, it did not perform as well along other attributes (including durability and flexibility) as the other choices. Thurston, in describing this case study, noted, “When decision makers are faced with several alterna­ tive systems, each system may be represented as a bundle of seemingly incommensurate attributes. The best choice is not always clear.” In this case, the automotive company, with support from decision analysts, used a MAU model to help decide which of the three vehicle frame­and­skin systems maximized the utility to the company, given the need to trade­off cost and performance attributes. Thurston interviewed the decision makers at the company (in this case, engineering materials design managers focused on long­term fleet planning) to determine which attributes were important in their decision. The decision makers identified five attributes: 1. Capital cost (billions of francs), 2. Operating cost (francs per vehicle), 3. Weight (kg), 4. Corrosion resistance (years of resistance to corrosion), and 5. Design flexibility (number of body styles possible per platform). With the company’s engineers, Thurston then measured the perfor­ mance of each of the three materials options along these five attributes. Table 6.3 shows the results. continued

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122 USE AND STORAGE OF METHYL ISOCYANATE (MIC) AT BAYER CROPSCIENCE BOX 6.2 Continued TABLE 6.3 Alternative Automotive Frame-and-Skin Systems and Their Performance Along Key Attributes Design Operating Flexibility Capital Cost (Number Cost (Francs of Bodies Corrosion (Billion per Weight per Resistance Design Francs) Vehicle) (Kilos) Platform) (Years) Steel Uni-body 3 30 500 1 5 Steel Frame; PC Skin 2 40 425 5 15 Steel and PC Frame; PC Skin 1.5 45 350 5 15 SOURCE: Reproduced from Thurston, 1990. Next, through in­person and electronic surveys of the decision makers, Thurston determined an appropriate form for the MAU function—in this case, multiplicative: n KU ( x ) + 1 = ∏ (Kk iU i ( x i ) + 1) i =1 Thurston then assessed the individual utility functions Ui(xi) for each of the five attributes, again through surveys of the decision makers, and the scaling constants ki for each utility function, as well as the overall scaling constant K. As an example, Figure 6.1 shows single­attribute utility functions for operating cost and flexibility in number of body types possible per platform. As shown, these single­attribute functions convert the levels of each option along each attribute to a value between 0 and 1 that reflects the decision makers’ tolerance of risk. For example, the concave shapes of these utility functions show that the decision maker is risk averse in preferences for capital costs and flexibility, gaining more from initial incremental cost savings and design flexibility than from subsequent increments. Figure 6.2 shows the resulting attribute scores and rankings of the three design alternatives. As shown, the steel frame with polymer com­ posite skin option has the highest utility. This result was counter­intuitive to the decision makers, since the polymer composite skin­and­frame option scored highest along four of the five attributes, including capital costs. However, the high variable costs of this latter system outweighed the benefits along the other four attributes. Thus if the decision makers

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123 A FRAMEWORK FOR DECISION MAKING FIGURE 6.1 Single-attribute utility functions for cost and flexibility for the auto industry case study described in Box 6.2. Note that the dots represent points on the utility function assessed through structured interviews with the company’s decision makers. SOURCE: Thurston, 1990. continued

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124 USE AND STORAGE OF METHYL ISOCYANATE (MIC) AT BAYER CROPSCIENCE BOX 6.2 Continued FIGURE 6.2 Attribute levels and final rankings of the alternative auto frame- and-skin designs discussed in Box 6.2. SOURCE: Thurston, 1990. had chosen the option that seemed intuitively to be the right choice, this decision would not have accurately reflected their true preferences. tenets of rational decision theory (Kahneman et al., 1982). Decision analysis, by providing a framework for the decision process, can help overcome the well- known human cognitive limitations that can lead to less-than-optimal decision- making (Clemen and Reilly, 2001). It is also important to recognize that, as with any modeling system, the quality of the data used in the analysis is critical, and care must be taken to ensure that the inputs collected from the various stakeholder communities—utility values for MAU—accurately represent their views. Limitations of Existing Inherent Safety Indexes Chemical engineers in the field of ISP design have conceived a number of summary indexes intended to capture the trade-offs in objectives embodied by

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125 A FRAMEWORK FOR DECISION MAKING different design options. Examples include the integrated inherent safety index (I2SI) and a set of European Union (EU) indexes known as the INSET Toolkit. (For descriptions of these methods, see Chapter 4 and Khan and Amyotte, 2004.) When considered from a formal decision analysis perspective, these indexes have a number of limitations, both theoretical and practical. The main theoreti - cal weakness is that the indexes were not designed to follow the von Neumann- Morgenstern model of rational choice, so there is no guarantee that the index value for a given manufacturing process will be able to reflect a given decision maker’s actual preferences and attitudes toward risk. Perhaps more importantly, these indexes—in contrast to a MAU approach—do not allow for the possibility of multiple decision makers with different preferences. As an example, consider the I2SI index (Khan and Amyotte, 2004). This index is intended to combine assessments of multiple inherent safety attributes into a single numeric value. The equation for computing this index is: ISI alt PHCI alt I 2SI = (6.3) HI base PHCI base where ISIalt, PHCIalt, HIbase, and PHCIbase are all subindexes computed as func- tions of various process design attributes. For each manufacturing process con- sidered, the ratio HIbase/PHCIbase is the same, so that when comparing multiple alternatives the above equation reduces to: 5 ∑ ISI } 2 Min{200, i ISI alt i =1 I 2SI = c × =c× (6.4) 10 PHCI alt ∑ PHCI j j =1 where c is a constant and the summation terms in the equation represent the attri - butes listed in Table 6.4. The values for the summation terms are determined from a combination of subjective judgments about the degree to which each process unit satisfies the principles of ISP. An index such as the I2SI provides a single calculation that cannot be adjusted to reflect the variation in preferences among attributes and willingness to tolerate risk that different constituencies may exhibit. For example, a company owner may be willing to tolerate a small risk of a spill that could have health effects in the community if the alternative involved a much higher risk of a fire that would seriously damage the facility, whereas members of the community may not accept such a trade-off, and employees of the firm (who place some value on keeping the facility intact in order to retain their jobs) may prefer something in between the owner and the community. In addition to putting different weights

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126 USE AND STORAGE OF METHYL ISOCYANATE (MIC) AT BAYER CROPSCIENCE TABLE 6.4 Attributes Included in the I2SI (Equation 6.4) Attribute Category : Adherence to principles of inherently safer design Representation in Attribute Description I2SI Equations Extent to which process minimizes use of hazardous materials ISI1 = ISIm Extent to which process substitutes safer materials for more hazardous ones ISI2 = ISIsu Extent to which process attenuates risks by operating under safer conditions ISI3 = ISIa (e.g., room temperature and pressure) Extent to which process simplifies manufacturing (e.g., by avoidance of ISI4 = ISIsi multiproduct or multiunit operations or congested pipe or unit settings) Extent to which process limits potential negative consequences of out-of- ISI5 = ISIl normal operations (e.g., by unit segregation) Attribute Category : Need for add-on processes to control hazards Representation in Attribute Description I2SI Equations Pressure control required PHCI1 = PHCIp Temperature control required PHCI2 = PHCIt Flow control required PHCI3 = PHCIf Level control required PHCI4 = PHCIl Concentration control necessary PHCI5 = PHCIc Inert venting necessary PHCI6 = PHCIiv Blast wall needed PHCI7 = PHCIbw Fire resistance wall needed PHCI8 = PHCIfr Sprinkler system necessary PHCI9 = PHCIs Forced dilution needed PHCI10 = PHCId on different attributes, a MAU model can reflect differences in risk tolerances in the form of the utility function: linear functions represent risk neutrality; concave functions represent a preference for gambling on high risks that have potentially high payoffs; and convex functions represent risk aversion (for details, see Cle - men and Reilly, 2001). The existing indexes are the proverbial black box: input a set of numbers based on the process being evaluated, and the index produces a single value for each alternative, which is then used to rank the different alternatives and identify the optimal decision. All of the trade-offs, uncertainties, and risk tolerances are hidden from view because they are implicitly assumed in the underlying calcula - tions, rather than explicitly chosen parameters. Because these indexes implicitly assume one value structure, the effects of alternative value structures on the pref - erence ordering of the alternatives cannot be assessed. Indeed, because the trade- offs in the index are completely opaque (to the analysts as well as to others), it is unlikely that companies will be able to use such indexes to build trust within the communities in which their facilities are located. A more transparent approach,

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127 A FRAMEWORK FOR DECISION MAKING that is, one that makes the trade-offs in attributes and risk tolerances explicit, is needed if the outcomes of ISP assessments are to be widely embraced. EMPLOYING MAU MODELS IN ISP ASSESSMENTS The existing inherent safety indexes could serve as a starting point for building MAU functions to inform process design choices, with inherent safety in mind. For ISP design, fundamental objectives have been described as elimi - nation, minimization, substitution, moderation, and simplification. The indexes provide useful starting points for constructing MAU functions in that they identify a number of the attributes by which progress along these fundamental objectives could be measured. For example, the attributes in the I2SI include costs associated with any damage that might occur due to a safety breach and costs associated with a process control option under consideration. Other attributes that could be included might be the number of fatalities that could occur in an accident and the potential loss of community goodwill (which can lead to additional costs for the company in its future decision-making). CCPS (1996), mentioned at the beginning of this chapter, provides an illustration of the use of MAU to choose a process control device for a hypothetical chemical distillation column. EMPLOYING MAU MODELS AT BAYER CROPSCIENCE MAU and ISP decision-making tools could have been used to inform manu- facturing process design choices at numerous points in the history of the Institute pesticide plant, starting with the introduction of MIC to the site in 1978. As noted in Chapter 5, changes to the production process made at the Institute facility were generally considered in response to business conditions or external pressures, without explicit consideration of ISP principles. Several of these decision points could have provided opportunities to introduce MAU approaches, which would have recognized the multi-attribute nature of the decision and the differences in preferences between the firm and the community, perhaps resulting in a decision- making process that would have been more acceptable to the community. That, in turn, might have allowed production to continue at the plant in some form. Chapter 5 mentions the 1984 Bhopal accident and the 2008 methomyl acci- dent as significant opportunities for broadening the approach to decision making at Institute. Other opportunities arose when the plant changed ownership. Choices that better accounted for the multiple costs and benefits involved—including the costs associated with risks imposed on the community—could have prevented the types of accident risks that persisted until the use of MIC ceased at the site. Without such a multi-attribute decision framework in place, the decisions at the Institute site seem to have focused on production costs and the business risk of interrupting the flow of product to the market, which resulted in the decision to

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128 USE AND STORAGE OF METHYL ISOCYANATE (MIC) AT BAYER CROPSCIENCE continue with the same basic production process with some modifications rather than adopting an entirely new approach. This bias in favor of an existing production process is not surprising and may even reflect the optimal decision, especially from the company’s point of view. “Steel in the ground” is a powerful motivator. It avoids the up-front capital costs of a new process, along with any uncertainties about how well the process will operate or what its operating cost will be. Critically and objectively reviewing a process that has been in operation for many years can be difficult for those work - ing at a chemical production facility. For example, from a practical standpoint, it is likely that, over time, a given process will have been modified from its initial design. Minor changes in procedures, modifications to existing equipment, and the effect of age and maintenance on the system must be taken into account dur- ing the trade-off analysis. This may be challenging if, due to staffing changes or insufficient documentation, the modifications from the initial design have not been recorded. In addition, alternative processes may always seem “hypothetical,” and concerns about risks expressed by community members may be ascribed to their lack of understanding of the process and its many layers of safety protec - tions. These factors make it difficult to apply MAU (and ISP) analyses to exist - ing plants in ways that can really identify promising alternatives to the existing process. Implementing a structured, multi-attribute decision process such as MAU analysis may be easier when designing a new production process or an entirely new facility. In such cases, no incumbent process has an advantage in terms of capital costs or production uncertainties, since all the alternative processes are new and hence hypothetical. In addition, the company may be involved in negoti- ations with regulators—who, in turn, are considering the concerns of community groups—in order to obtain an operating permit, so the need to address concerns held by those outside the facility will be more salient. During these initial inter- actions between the facility and its neighbors, a MAU analysis could serve to educate community members about the trade-offs between risks and economic considerations of importance to the company, while informing the company of the community’s concerns about different types of production risks. However, conducting a MAU analysis for the MIC issue would not neces- sarily have resulted in different decisions by the companies owning the Institute plant. Especially using the company’s own valuation on the multiple attributes of the alternatives, continuing with the existing MIC process might well have seemed optimal. The advantage that would be gained from using multi-criteria analysis is that the company would have explicitly identified the most important attributes of the alternative processes and assigned a valuation to those attributes. Using MAU could also have provided a calculation into which the preferences of others (e.g., community members) could easily be incorporated.

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129 A FRAMEWORK FOR DECISION MAKING CONCLUSIONS No one carbamate pesticide production process dominates all the others along every attribute that owners of the Institute site considered or could have consid- ered. However, given the necessary trade-offs, a decision-analysis approach, such as MAU, could have helped the various plant owners consider all aspects of their choices. A decision analysis approach might also have facilitated the communi- cation process between the company and the surrounding community, allowing the two groups to compare their preferences regarding the decision attributes and their potentially differing tolerances and attitudes toward risk. Although the CCPS has advocated for the development of such an approach since 1995, it has not become institutionalized in the chemical process industry. A new decision- making framework, incorporating some of the work done to develop exist- ing ISP indexes but also allowing explicit consideration of differences in decision makers’ preferences across multiple attributes, could assist in the incorporation of ISP considerations into decision making in the chemical manufacturing industry and communication of those considerations to a concerned public. Design decisions cannot be strictly objective with regards to ISP as these choices will always require trade-offs among attributes and varying levels of risk; different individuals or constituencies may have different value systems and thus make different trade-offs. However, a new decision framework could support incorporation of the attributes in existing indexes, while adhering, and drawing benefit from, the mathematics of multi-attribute or -criteria decision analysis. The committee recommends that the Chemical Safety Board or other appropriate entity convene a working group to chart a plan for incorporat- ing decision theory frameworks into ISP assessments. The working group should include experts in chemical engineering, ISP design, decision sciences, negotiations, and other relevant disciplines. The working group should iden - tify obstacles to employing methods from the decision sciences in process safety assessments. It should identify options for tailoring these methods to the chemical process industry and incentives that would encourage their use. REFERENCES Belton, V.,and T. S. Stewart. 2002. Multiple Criteria Decision Analysis: An Integrated Approach. Norwell: Klumber Academic Publishers. Bunn, D. W. 1984. Applied Decision Analysis. New York: McGraw-Hill. CCPS (Center for Chemical Process Safety). 1995. Tools for Making Acute Risk Decisions with Chemical Process Safety Applications. New York: American Institute of Chemical Engineers. Clemen, R. T., and T. Reilly. 2001. Making Hard Decisions with Decision Tools. Belmont, CA: Duxbury Press. DeBrucker, K., Macharis, C., and A. Verbeke. 2012. Multi-criteria analysis and the resolution of sustainable development dilemmas: a stakeholder management approach. European Jounral of Operational Research. Available online.

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130 USE AND STORAGE OF METHYL ISOCYANATE (MIC) AT BAYER CROPSCIENCE Figueira, J., S. Greco, and M. Ehrgott. 2005. Multiple Criteria Decision Analysis: State of the Art Surveys. New York: Springer Science and Business Media, Inc. French, S. 1986. Decision Theory: An Introduction to the Mathematics of Rationality. Chichester: Ellis Horwood. Kahneman, D., P. Slovic, and A. Tversky. 1982. Judgment Uner Uncertainty: Heuristics and Biases. Cambridge, UK: Cambridge University Press. Keefer, D. L., C. W. Kirkwood, and J. L. Corner. 2004. Perspective on decision analysis applications, 1990-2001. Decision Anal. 1(1):4-22. Keeney, R., and T. McDaniels. 1992. Value-focused thinking about strategic decisions at BC. Hydro. Interfaces 22(6):94-109. Keeney, R., and H. Raiffa. 1976. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. New York: Wiley. Khan, F. I., and P. R. Amyotte. 2004. Integrated Inherent Safety Index (I2SI): A tool for inherent safety evaluation. Process Saf. Prog. 23(2):136-148. Raiffa, H., J. Richardson, and D. Metcalfe. 2002. Negotiation Analysis: The Science and Art of Col- laborative Decision Making. Boston: Belknap Harvard. Thurston, D. L. 1990. Multiattribute utility analysis in design management. IEEE Trans. Eng. Man- age. 37(4):296-301. von Neumann, J., and O. Morgenstern. 1947. Theory of Games and Economic Behavior, 2nd Ed. Princeton, NJ: Princeton University Press. von Winterfeldt, D., and W. Edwards. 1986. Decision Analysis and Behavioral Research. Cambridge, UK: Cambridge University Press.