Descriptive Analysis

The conditions of Pakistan, Germany, and Turkey are discussed in chapter 5 with the perspective of international garments supply chain. Vulnerability profile of Pakistan is relatively higher as compare to Germany and Turkey. The research question assumes that supply chain process carried out in country with volatile conditions will experience frequent disruption. The frequency of resorting to adaptive capability is expected to be high for the garments supply chain firms involved in supply chain processes in Pakistan than Turkey and Germany respec- tively due to comparatively high volatile conditions. Further it is assumed that resilience is expected to be low for supply chain firms operation in Pakistan as compared to Turkey and Germany given to the differences in conditions. Supply chain global resilience depends upon the resilience of supply chain processes like

manufacturing and transportation. Supply chain risk costs are determined by sup- ply chain global resilience. In order to analyze the data for these assumptions, means of variables are compared for different groups of data. Compare means, in SPSS Version 20, is used to analyze the data for group differences regarding the variables of adaptive capability, disruption vulnerability and supply chain pro- cesses resilience, supply chain global resilience, and supply chain risk costs. The results are presented in Annexure B. Supply chain stage of manufacturing is compared for differences in group data of Pakistan and Turkey where garments manufacturing is carried out in location with unique conditions. Supply chain transportation stage is compared for differences for group data of Pakistan, Tur- key, and Germany with specific conditions as transportation is carried across these countries.

6.5.1.1 Manufacturing Adaptive Capability Descriptives

Adaptive capability, for manufacturing stage, has four indicators. The first indi- cator measures the frequency of using alternate raw material source by the firms in different locations. Firms usually resort to this adaptive measure in case inabil- ity of regular supplier to maintain the flow of raw material. The comparative means of using alternate raw material sources for garments manufacturing firms in Pakistan and Turkey are 3.29 and 2.98 respective as reported in Annexure B. The frequency of using alternate raw material source is higher for manufacturing firms in Pakistan is higher as compared to Turkey. Similarly the indicator meas- uring frequent usage of alternate production methods has means of 3.44 and 3.10, the indicator measuring usage of alternate utility sources has means of 3.43 and 3.13 and the indicator measuring the adaptive measure of using lead time buffer has means of 3.69 and 3.40 for data groups Pakistan and Turkey respectively. All four indicators of manufacturing adaptive capability have variable means higher for Pakistan as compared to Turkey presented in Annexure B.

The differences among the groups for indicators of manufacturing adaptive capa- bility are not statistically significant as shown in Annexure C. This suggests that conditions for manufacturing are not very different in Pakistan and Turkey. Sup-

ply chain entities, in these countries, resort to adapt to capability with a similar pattern.

6.5.1.2 Manufacturing Disruption Vulnerability Descriptives

The first assumption is that supply chain process disruption vulnerabilities are frequent in location with volatile conditions. Disruption vulnerability for manu- facturing stage is measured through four indicators of procurement delays, work- er shortages, machine stoppages, and utility breakdown. Manufacturing firms in Pakistan and Turkey encounter procurement delays with comparative means of 2.32 and 2.05. Workers shortages occur with mean of 2.11 and 2.00 in firms in Pakistan and Turkey respectively. Machine closures happens with a mean of 2.02 and 1.70. Utilities breakdowns are more frequent for Pakistan as compared to Turkey with means of 2.57 and 2.50 as presented in Annexure B .

Statically, the differences among the data groups for manufacturing disruption vulnerability are not significant reported in Annexure C. This suggests that con- ditions for garments manufacturing in Pakistan and Turkey are either not very different or the firms take adaptive measures more frequently that reduces the frequency of disruption vulnerability. The latter is assessed in the following sec- tion.

6.5.1.3 Transportation Adaptive Capability Descriptives

Transportation adaptive capability has three indicator variables including alter- nate cargo service, alternate shipping method and lead time buffer. The variables measures the frequency of resorting to these alternatives in Pakistan, Turkey, and Germany with different conditions. The comparative means for alternate ship- ping services are 3.64, 3.08, and 2.57, for alternate shipping method are 3.45, 3.05, and 2.75; and for extra time transportation are 3.80, 2.88, and 2.96 respec- tively for Pakistan, Turkey, and Germany as shown in Annexure B.

The group differences for all the indicator variables of adaptive capability are statistically significant as shown in Annexure C. This suggests that conditions for transportation process are different for Pakistan, Turkey, and Germany. The con- dition in Pakistan requires the firms to resort to frequent adaptive measures as

compared to Turkey and Germany. Frequent use of adaptive capability is sup- posed to be the reason that Pakistan and Turkey shows not significantly different disruption vulnerability, as mentioned earlier.

6.5.1.4 Transportation Disruption Vulnerability Descriptives

Similarly, transportation disruption vulnerability is measured by four indicator variables namely shipping service delay, road haulage delay, shipment pro- cessing delay, and shipping delays including arrival/ departure or during travel- ing. The comparative means for data group of Pakistan, Turkey, and Germany are 2.27, 2.53, and 2.00 for shipping service delay, 2.58, 2.50, and 2.39 for road delays, 2.07, 2.30, and 2.07 for processing delays, and 1.98, 2.20, and 1.75 for shipping line delays as presented in Annexure B. The frequency of transportation disruption vulnerability is higher for Pakistan and Turkey as compared to Ger- many.

The data group differences are not statistically significant Annexure C. This sug- gests that transportation process is either having low disruption vulnerability or transportation disruptions are frequently responded by measures that reduces the occurrence of disruptions.

6.5.1.5 Manufacturing Resilience Descriptives

The third assumption is that supply stage carried out in a volatile conditions is expected to have low resilience. Supply chain process resilience for manufactur- ing and transportation have two indicators each measuring the aspects of resili- ence in terms of quality and quantity objective of the processes. Manufacturing process resilience is measured through negative keyed indicators of frequency of rejects and production working under capacity. The negative-keyed indicators are reversed those measuring the frequency of meeting quality and quantity objec- tives. The comparative means for meeting manufacturing quality objective are 3.98 and 4.05 and for meeting manufacturing quantity objective are 4.01, and 4.05 respectively for data groups Pakistan and Turkey (Annexure B).

The resilience indicators for Pakistan are comparatively lower than Turkey how- ever these differences are statistically not significant as presented in Annexure C.

As suggests earlier, frequent use of adaptive capability by firms in Pakistan and Turkey reduces the frequency of disruption and therefore the data group shows similar level of resilience.

6.5.1.6 Transportation Resilience Descriptives

As mentioned above, the third assumption is that supply stage carried out in a volatile conditions is expected to have low resilience. The transportation stage resilience measures the frequency of meeting transportation quality and quantity objectives. The comparative means for these indicator variables are 4.18, 3.78, and 4.14 for meeting transportation quality objective and 4.07, 3.58, and 4.11 for meeting transportation quantity objective for Pakistan, Turkey, and Germany data groups respectively as presented in Annexure B.

The indicators of transportation resilience is almost the same for the data groups and therefore the group differences are not significant (Annexure C). This sug- gests that adaptive capability is effectively used to maintain the process of trans- portation across countries with unique conditions.

6.5.1.7 Supply Chain Global Resilience Descriptives

The fourth assumption was that supply chain processes carried in location with volatile conditions are expected to have low supply chain global resilience. The indicators for supply chain global resilience are the frequency of meeting manu- facturing and transportation schedule objectives. The comparative means for meeting manufacturing schedule objective are 3.87 and 4.03 for data group of Pakistan and Turkey. The comparative means for meeting transportation sched- ule objective are 4.19, 3.78, and 4.18 for data group Pakistan, Turkey, and Ger- many respectively reported in Annexure B.

The differences among the groups for supply chain global resilience are not sig- nificant as reported in Annexure C. This suggests that adaptive capability is used where needed and thus contribute to resilience of supply chain process and ulti- mately to the overall supply chain global resilience. The supply chain global re- silience is exhibiting similar pattern across countries with different conditions.

6.5.1.8 Supply Chain Risk Costs Descriptives

The fifth assumption made was that supply chain processes carried in locations with volatile conditions are expected to have high risks cost. The indicator varia- bles for measuring the construct of supply chain risk costs are the frequency of excess cost incurred during manufacturing and transportation processes. The comparative means for manufacturing excess costs are 3.91 and 3.95 for data groups Pakistan and Turkey and for transportation excess costs are 4.01, 3.88, and 3.93 for data groups Pakistan, Turkey, and Germany as reported in Annexure B.

The difference among groups are not significant as presented in Annexure C. The use of adaptive capability contributing to resilience of supply chain processes and supply chain global resilience influences the excess costs and therefore show a similar pattern across countries with different conditions.

To summarize the group comparison, adaptive capability has a high frequency in case of manufacturing stage carried in Pakistan and Turkey. The occurrence of disruptive vulnerability during manufacturing and transportation stages is ex- pected to be high for Pakistan data group compared to Turkey and Germany re- spectively. The results shows difference but none of these is significant. The ex- planation is that in case of high adaptive capability, the occurrence of disruption vulnerability is expected to show similar pattern for locations with different con- ditions. The assumption suggests that there is a direct causal relationship between adaptive capability and disruption vulnerability. Similarly, the use of adaptive capability used during transportation stage is not statistically significant, however the comparative means are higher for Pakistan data group than for Turkey. In case of transportation stage, comparative means are different for group data and use of transportation adaptive capability is higher for Pakistan than Turkey and Germany. The group differences are also statistically significant. The contextual explanation to this is the fact that the location conditions of Pakistan are more volatile as compared to Turkey, the gap widens between Pakistan and Germany as discussed in chapter 5. The assumption 2 is true for countries with significant- ly different conditions. As conditions are highly volatile for Pakistan, the use of

adaptive capability is higher for Pakistan. This suggest that negative relationship between disruption vulnerability and adaptive capability. The frequency of dis- ruptions is assumed to be low in the case of frequent use of adaptive capability. The group difference for the indicators of supply chain processes resilience, sup- ply chain global resilience, and supply chain risk costs are not significantly dif- ferent for manufacturing stage carried out in Pakistan and Turkey and transporta- tion stage spread over Pakistan, Turkey, and Germany. This is contrary to as- sumptions 3, 4, and 5 suggesting that there is an intervening variable that nega- tively affects the negative relationship between disruption vulnerability and resil- ience to an extent that resilience of the firms located in different countries with unique conditions are not significantly different. This suggests that there is mod- eration effect that changes the negative effect of disruption vulnerability on these constructs. Test for mediation is carried out in the preceding section that suggests partial mediation. The assumptions supports the proposed direct effect, mediation effect, and moderation effect examined in the preceding section.

6.6 Summary

This chapter is concerned with analysis outer and inner models. Structural equa- tion modeling is used as a state of the art method for statistical analysis of supply chain resilience model. There is detailed discussion on the nature of outer model. The indicators of supply chain resilience model have been identified as formative indicators. Partial least square structural equation modeling is selected for model estimation. The outer model is evaluated against the measures of indicators weights and loading, their significance, collinearity, and suppressor effect. The evaluation of outer model suggests that all the indicators are to be kept in the model during further analysis.

The causal analysis evaluates the hypotheses suggested in the inner model. The model proposes direct effects between constructs, mediation effect, and modera- tion effect. The evaluation measured used are coefficient of determination, sig- nificance of path coefficients, predictive relevance and heterogeneity. The measures are considered for all the relationships and the hypothesis are found to

be empirically supported by the data across different location. The assumption for causal relationships are discussed in detail and it was suggested that in the presence of adaptive capability, the frequency of disruption vulnerability is re- duced. Adaptive capability is frequently used in locations with comparatively high vulnerability profile.