Supply Chain Uncertainty and Environmental Management
Read this article, which examines the impact of supply chain uncertainty on environmental management spending in manufacturing. Focus on the sections of Supply Chain Uncertainty and Linking Supply Chain Uncertainty to Environmental Management. What is your definition of uncertainty in supply chain management?
Empirical Analysis
Bivariate correlations are presented in Table 3. There is a significant correlation between the environmental practices scale and the percentage of the capital budget devoted to environmental projects. This correlation suggests that somehow these two variables are representing a similar concept. The two scales measuring supply chain uncertainty correlated at 42% indicating potential for collinearity if they are both introduced in the regression model. The data was analyzed using hierarchical regressions with demand and supply uncertainty entered separately in the models.
Table 3 Correlations^{a,b}
Mean | s.d. | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1. Environmental practices | 4.0 | 1.3 | ||||||||||
2. % of capital budget | 2.3 | 1.5 | .339* | |||||||||
3. Pollution prevention | 51.5 | 28.5 | −.016 | .120 | ||||||||
4. Pollution control | 26.1 | 24.0 | −.063 | .147^{†} | −.596* | |||||||
5. Management systems | 22.4 | 24.0 | .082 | −.005 | −.593* | −.293* | ||||||
6. Demand uncertainty | 2.8 | 1.1 | −.181* | .041 | −.083 | .186* | −.086 | |||||
7. Supply uncertainty | 2.4 | 0.8 | −.092 | .011 | −.149^{†} | .157^{†} | .020 | .420* | ||||
8. ISO 14001 certification | 0.2 | 0.4 | .196* | .204* | −.321* | −.040 | .438* | −.081 | −.101 | |||
9. Plant size | 4.8 | 1.0 | .155^{†} | .103 | −.145 | −.008 | .181* | −.166* | −.075 | .314* | ||
10. Company size | 6.0 | 1.8 | .018 | .102 | −.231* | .003 | .277* | −.051 | −.007 | .380* | .536* | |
11. Province | 0.7 | 0.4 | −.142^{†} | −.198* | .157^{†} | −.016 | −.132^{†} | .173* | .105 | −.174* | −.039 | −.101 |
- ^{a}Pearson correlation except for "ISO 14001 certification" and "Province" for which a Spearman correlation was computed (because of the binary nature of these two variables)
- ^{b}* p-value < .01; ^{†} p-value < .05
Environmental Spending
The results of the regressions pertaining to environmental spending are presented in Table 4. Weak support for hypothesis H1 was found as demand uncertainty was negatively linked to the level of environmental practices (Model 1; p-value < .05). However, supply uncertainty was not impacting the level of environmental practices (Model 1; p-value > .10). The proportion of the capital budget devoted to environmental projects was neither affected by demand uncertainty nor by supply uncertainty. Not surprising, the plants that were ISO 14001 certified had higher level of environmental practices (Model 1; p-value < .01); however, it was not linked to the proportion of capital budget related to environmental projects.
Table 4 Regressions: Environmental Management^{a,b}
Environmental practices | % of capital budget to environment | |||||
---|---|---|---|---|---|---|
Model 1.1 | Model 1.2 | Model 1.3 | Model 2.1 | Model 2.2 | Model 2.3 | |
Plant size^{c} | .134^{†} | .118 | .133^{†} | .047 | .059 | .049 |
Company size^{c} | −.133^{†} | −.141^{†} | −.131^{†} | .037 | .041 | .036 |
Province^{d} | −.083 | −.058 | −.081 | −.170* | −.188** | −.172* |
ISO 14001^{e} | .180* | .181* | .177* | .086 | .085 | .088 |
Demand uncertainty | −.148* | .098 | ||||
Supply uncertainty | −.037 | .031 | ||||
R-square | .056** | .077** | .057* | .056* | .065* | .057* |
F-statistics | 3.433 | 3.835 | 2.805 | 3.337 | 3.116** | 2.706 |
∆ R-square | .021* | .001 | .009 | .008 |
- ^{a}Standardized betas reported. Number of observations: 237 for environmental practices and 231 for capital budget
- ^{b}** = p-value < .01; * = p-value < .05; ^{†} = p-value < .10
- ^{c}The company and plant size were computed by taking the natural logarithmic transformation of the number of employees
- ^{d}This is a dummy variable where 0 is Ontario and 1 is Quebec
- ^{e}This is a dummy variable where 0 is a not ISO 14001 certified plant and 1 is a certified plant
Pollution Prevention and Pollution Control
Because the level of resources devoted to environmental management can have an influence on sustainable product innovation and that kind of innovation is associated with pollution prevention, three blocks of variables were used in each of the hierarchical regression model. First, the control variables were entered followed by three environmental management variables: ISO 14001 certification, percent of the capital budget devoted to environmental projects, and the proportion of infrastructural resources devoted to environmental management (i.e., management systems). Finally, either demand or supply uncertainty was entered in the model. This approach allowed to assess the additional variance explained in the dependent variable from supply chain uncertainty.
Strong support for hypothesis H2 was found (Table 5). Supply chain uncertainty was negatively linked to pollution prevention (Model 3.3, p-value < .01 and Model 3.4, p-value < .01) and positively linked to pollution control (Model 4.3, p-value < .01 and Model 4.4, p-value < .01). In fact, when put together in the regression (not reported in Table 5), demand uncertainty and supply uncertainty contributed significantly to the variance explained (i.e., the change in R-square from the introduction of the two variables). The increase in the R-square when both uncertainty variables were included was 2.9% (p-value < .01) for pollution prevention and 4.1% (p-value < .01) for pollution control. Therefore, an increasing level of supply chain uncertainty is associated with a shift of environmental spending from pollution prevention to pollution control.
Table 5 Regressions: Pollution Prevention and Pollution Control^{a,b}
Pollution Prevention | Pollution Control | |||||||
---|---|---|---|---|---|---|---|---|
Model 3.1 | Model 3.2 | Model 3.3 | Model 3.4 | Model 4.1 | Model 4.2 | Model 4.3 | Model 4.4 | |
Plant size^{c} | .022 | .025 | .011 | .021 | −.020 | −.029 | −.013 | −.024 |
Company size^{c} | −.233** | −.055 | −.060 | −.054 | .016 | .066 | .071 | .065 |
Province^{d} | .143* | .037 | .067 | .046 | −.036 | −.044 | −.079 | −.054 |
ISO 14001^{e} | −.076 | −.078 | −.089 | .091 | .092 | .106 | ||
% capital budget | −.087 | −.071 | −.082 | .103 | .084 | .097 | ||
Management systems | −.541** | −.543** | −.527** | −.346** | −.345** | −.364** | ||
Demand uncertainty | −.148** | .176** | ||||||
Supply uncertainty | −.142** | .169** | ||||||
R-square | .075** | .372** | .392** | .173** | .002 | .114** | .143** | .142** |
F- Statistics | 5.678 | 20.502 | 19.084 | 19.032 | 0.112 | 4.467 | 4.942 | 4.905 |
∆ R-square | .297** | .021** | .020** | .113** | .029** | .028** |
- ^{a}Standardized betas reported. Number of observations: 215
- ^{b}** = p-value < .01; * = p-value < .05; ^{†} = p-value < .10
- ^{c}The company and plant size were computed by taking the natural logarithmic transformation of the number of employees
- ^{d}This is a dummy variable where 0 is Ontario and 1 is Quebec
- ^{e}This is a dummy variable where 0 is a plant that is not ISO 14001 certified and 1 is a certified plant