ESTRATEGIA Y PLAN DE INTRODUCCIÓN DE MERCADO

One of the most valuable types of information that can be determined from a Bayesian network developed using GeNIe is a ranking of the explanatory variables based on their information value relative to the outcome variable. This can also be viewed as a measure of their influence on or the degree to which they reduce the uncertainty in the outcome variable. The concept of value of information is one method of handling or studying decision models. The explanatory variables were ranked according to their degree of influence on, or information value relative to, each category of dollar loss for each of the five networks. Thus, five sets of inferences are presented and summarized in the following sections. The five sets of ranking results for zero, small, and medium dollar loss are presented in through

(225 226, )

Table 62 Table 64, respectively. In summary, causing object was the leading diagnostic variable for dollar loss, regardless of the loss category, followed by the failure item-area. Thus, these two variables were found to be the most influential or informative variables relative to dollar loss. In Table 62, which presents the

ranking results specific to zero dollar loss, causing object was the leading variable in 3/5 networks and nearly tied as the leading variable in a fourth network (T5). For zero loss, failure item-area was clearly the second leading variable, based on the results from 3/5 networks.

Table 62: Ranking of ‘Zero’ Dollar Loss Parent Variables.

Zero Dollar Loss Category

Rank T1 Ranking Value T2 Ranking Value T3 Ranking Value T4 Ranking Value T5 Ranking Value 1 CO 0.0972 CO 0.0957 CO 0.0968 FIA 0.0966 FIA 0.0936 2 FIA 0.0939 FIA 0.0927 FIA 0.0956 CO 0.0951 CO 0.0935

3 L 0.0084 L 0.0080 L 0.0083 L 0.0096 L 0.0083 4 RQ 0.0062 RQ 0.0072 RQ 0.0064 RQ 0.0073 RQ 0.0067 5 M 0.0035 CA 0.0033 CA 0.0031 M 0.0036 CA 0.0030 6 CA 0.0029 M 0.0030 M 0.0030 CA 0.0031 M 0.0029 7 FM 0.0015 FM 0.0017 FM 0.0017 FM 0.0017 FM 0.0010 8 C 0.0006 C 0.0008 C 0.0009 C 0.0010 C 0.0008 9 SH 0.0003 SH 0.0001 SH 0.0003 SE 0.0004 SH 0.0003 10 SE 0.0002 SE 0.0001 SE 0.0002 SH 0.0002 SE 0.0003

For both small and medium dollar loss, which are shown in Table 63 and Table 64, causing object was clearly the leading variable, followed by failure item-area, both based on 5/5 networks.

Table 63: Ranking of ‘Small’ Dollar Loss Parent Variables.

Small Dollar Loss Category Rank T1 Ranking Value T2 Ranking Value T3 Ranking Value T4 Ranking Value T5 Ranking Value 1 CO 0.0186 CO 0.0183 CO 0.0192 CO 0.0173 CO 0.0179 2 FIA 0.0161 FIA 0.0158 FIA 0.0167 FIA 0.0164 FIA 0.0158 3 RQ 0.0051 RQ 0.0057 RQ 0.0054 RQ 0.0065 RQ 0.0057 4 CA 0.0030 CA 0.0030 CA 0.0027 CA 0.0028 CA 0.0031 5 FM 0.0021 FM 0.0020 FM 0.0020 FM 0.0023 FM 0.0026 6 SE 0.0021 SE 0.0014 SE 0.0016 SE 0.0020 SE 0.0017 7 SH 0.0007 SH 0.0005 SH 0.0005 SH 0.0008 SH 0.0005 8 M 0.0005 M 0.0003 M 0.0004 M 0.0004 M 0.0002 9 C 0.0001 C <0.0001 C 0.0001 C <0.0001 C 0.0001 10 L <0.0001 L <0.0001 L <0.0001 L <0.0001 L <0.0001

Table 64: Ranking of ‘Medium’ Dollar Loss Parent Variables.

Medium Dollar Loss Category Rank T1 Ranking Value T2 Ranking Value T3 Ranking Value T4 Ranking Value T5 Ranking Value 1 CO 0.0393 CO 0.0385 CO 0.0413 CO 0.0390 CO 0.0375 2 FIA 0.0307 FIA 0.0306 FIA 0.0330 FIA 0.0306 FIA 0.0314 3 CA 0.0186 CA 0.0175 CA 0.0195 CA 0.0190 CA 0.0186 4 FM 0.0172 FM 0.0172 FM 0.0176 FM 0.0182 FM 0.0171 5 L 0.0153 L 0.0151 L 0.0152 L 0.0142 L 0.0155 6 RQ 0.0049 RQ 0.0061 RQ 0.0050 RQ 0.0061 RQ 0.0062 7 SE 0.0045 SE 0.0050 SE 0.0046 SE 0.0053 SE 0.0043 8 SH 0.0029 SH 0.0028 C 0.0037 SH 0.0039 SH 0.0032 9 C 0.0028 C 0.0027 SH 0.0033 C 0.0028 C 0.0029 10 M 0.0013 M 0.0017 M 0.0014 M 0.0020 M 0.0018

Therefore, based on these results, causing object and item-area should be the top focuses of policy or operational change initiatives. In general, the container failure variables occupied upper positions in the rankings for dollar loss, indicating the value of this type of information. In addition, GeNIe can be used to determine desirable changes in an explanatory variable in order to best impact an outcome variable. For example, using GeNIe, it was determined that a reduction in incidents involving the floor and water/liquid as the causing object should be targeted, based on all five networks. This was determined based on changes in the probability distribution of dollar loss given the particular category of causing object. For example, using the first network (T1) to illustrate this method, setting the floor and water category of causing object as evidence led to an increase in the occurrence of both small and medium dollar loss and a decrease in zero loss relative to no evidence, as shown in Table 65. Thus, the floor and water causing object had an undesirable effect on dollar loss and should be the target of reduction efforts to best impact dollar loss. The “none” and “other” categories of causing object generally produced the opposite effect on dollar loss.

Table 65: Effects of Causing Object on Dollar Loss. (T1 network)

Causing Object Dollar Loss

ZERO SMALL MEDIUM

no evidence 0.167 0.718 0.116

Floor and water/

liquid 0.083 0.770 0.147 None 0.199 0.682 0.119 Other 0.364 0.607 0.029

This method of examining the probabilities to select the preferred alternative is based on the concept of stochastic dominance. Using stochastic dominance, the dominating alternative is the one more likely to lead to a particular outcome or consequence. It is in effect the better gamble. Stochastic dominance is a means to formally screen various alternatives and eliminate choices based on the pattern of the probabilities.(227) Based on the T1 network as shown in Table 65, the best option or policy for a reduction in dollar loss was the floor and water/liquid combination because it increased the probability for a medium loss the most relative to no evidence (from 0.116 to 0.147). In addition, it also increased the probability for a small loss from 0.718 to 0.770, while the other categories decreased this probability. Although this analysis for causing object was fairly straightforward, more than one policy change initiative may be desirable for a variable, depending on the category of dollar loss targeted for reduction. For, although the medium category has a higher dollar amount associated with it, it occurs less frequently than the small category (12% versus 72%, respectively). Thus, targeting the small category may impact more incidents.

A summary of recommended policy changes for each variable based on all five networks using this same type of analysis is given in Table 66. A few of the recommendations are based on “best of five” network outcomes where necessary. For example, this table shows that

incidents involving the floor and water category should be the focus of reduction efforts in order to best impact dollar loss. For failure item-area, there are two courses of action, depending on the category of dollar loss targeted. Specifically, if small loss is to be reduced, the best category to target is basic packaging material on the bottom of the container. However, to best impact medium dollar loss, either basic packaging material on the bottom or closures on top should be targeted. When evaluated from an overall perspective, the best category of failure item-area to target for reduction is basic packaging material on the bottom since it increases the probability of both a small and medium dollar loss and (undesirably) decreases the probability of zero loss the most. The five sets of probability distributions for dollar loss on which these recommended policy changes are based are provided in Table 81 through Table 85 in Appendix C.

Table 66: Recommended Policy Changes for Impacting Dollar Loss.

Explanatory

Variable Targeted Categories

Causing Object To best impact small and medium loss, target floor and water/liquid. _{Overall best choice is floor and water/liquid since it also decreases the probability of zero loss.}

Failure Item-Area

To best impact small loss, target basic package material on bottom.

To best impact medium loss, target closure on top or basic package material on bottom. Overall best choice is basic package material on bottom since it increases probability of small and medium loss and decreases probability of zero loss the most.

Contributing Action

To best impact small loss, target loose fitting or valve or other. To best impact medium loss, target improper loading and dropped.

Overall best choice is loose fitting or valve since it increases probability of small and medium loss and decreases probability of zero loss the most.

Failure Mode To best impact small loss, target other. _{To best impact medium loss, target punctured and crushed.}

Location To best impact medium loss, target suburban/ commercial/ eastern. _{There is limited information for small loss.}

Release Quantity To best impact small loss, target small release quantity. _{To best impact medium loss, target medium release quantity.}

Table 66 (continued).

Container Type

To best impact medium loss, target bottle. There is limited information for small loss. There is limited information provided by fiber box.

Overall best choice is bottle since it also decreases the probability of zero loss. Shift To best impact small loss, target twilight. _{To best impact medium loss, target day.}

Material Type To best impact small and medium loss, target corrosives. _{Overall best choice is corrosives since it also decreases the probability of zero loss.}