Academia.eduAcademia.edu

Figure 8 – uploaded by Nigerian Journal of Engineering Science Research (NIJESR)

See full PDFdownloadDownload figure
Fig. 7: Effect of Weight Selection on the Objective Function Values xxx et al., (2020). Supply Chain Management in a Manufacturing Industry. Nigeria Journal of Engineering Science Research (NIJESR). 3(1), pp. 48-62

Figure 7 Effect of Weight Selection on the Objective Function Values xxx et al., (2020). Supply Chain Management in a Manufacturing Industry. Nigeria Journal of Engineering Science Research (NIJESR). 3(1), pp. 48-62

Related Figures (15)

Supply Chain Management in a Manufacturing Industry “1Eli, T.M., 2Akene, A., and 3Ibhadode, O.
Supply Chain Management in a Manufacturing Industry “1Eli, T.M., 2Akene, A., and 3Ibhadode, O.
Fig. 1 Materials/information flow in SCM (Kittipong et al., 2013) xxx et al., (2020). Supply Chain Management in a Manufacturing Industry. Nigeria Journal of Engineering Science Research (NIJESR). 3(1), pp. 48-62
Fig. 1 Materials/information flow in SCM (Kittipong et al., 2013) xxx et al., (2020). Supply Chain Management in a Manufacturing Industry. Nigeria Journal of Engineering Science Research (NIJESR). 3(1), pp. 48-62
Fig. 2 Supply Chain Network in a Manufacturing Industry (Large and Thomsen, 2011 In this research work, the SCM model consists of suppliers who provide raw materials to the manufacturing plants, warehouses/ distribution centres and customer zones/retailers. Finished goods are shipped to various distribution centres using a particular mode of transportation, and finally to customers. Fig. 2 shows the network and interconnections of the components in the SCM process.
Fig. 2 Supply Chain Network in a Manufacturing Industry (Large and Thomsen, 2011 In this research work, the SCM model consists of suppliers who provide raw materials to the manufacturing plants, warehouses/ distribution centres and customer zones/retailers. Finished goods are shipped to various distribution centres using a particular mode of transportation, and finally to customers. Fig. 2 shows the network and interconnections of the components in the SCM process.
The sustainability aspect of SCM ensures that the overall performance of the system is improved by considering all three dimensions together. This simply means that the environmental and social factors should be considered, plus the traditional economic dimension. Cetinkaya et al. (2011) employed a three-dimensional metrics system to describe the sustainability. That is, social, economic, and environmental dimensions, and each of the dimensions is broken down into three other sub dimensions (Fig. 3). They stated that an improvement in one of the aspects, without negatively affecting the other dimensions, will result in a positive and sustainable supply chain.
The sustainability aspect of SCM ensures that the overall performance of the system is improved by considering all three dimensions together. This simply means that the environmental and social factors should be considered, plus the traditional economic dimension. Cetinkaya et al. (2011) employed a three-dimensional metrics system to describe the sustainability. That is, social, economic, and environmental dimensions, and each of the dimensions is broken down into three other sub dimensions (Fig. 3). They stated that an improvement in one of the aspects, without negatively affecting the other dimensions, will result in a positive and sustainable supply chain.
Table 1. ISO Measure of Social Impacts in Supply Chain Design Decisions (Pishvaee et al., 2012) 2.3 Mathematical Models The following sets and parameters are used for the formulation of the mathematical model. Sets /indices: a A a Aa I LS ool) A) My A El ae A sustainable process ensures at all times that the system thrives in all the challenges it faces. It is worth noting that measuring all the social factors in a single process of decision-making will be extremely difficult. Pishvaee et al. (2012) developed some standards to plan and implement corporate social responsibility in organizations, but a standard framework developed by the ISO termed the “International Guidance Standard on Social Responsibility-ISO 26000" (Montoya-Torres, 2015) is adopted. The classifications which are relevant to SCM are presented in Table 1. The safety factor (number of lost days caused by injuries at work) is considered by reducing the incidents/ accident rates.
Table 1. ISO Measure of Social Impacts in Supply Chain Design Decisions (Pishvaee et al., 2012) 2.3 Mathematical Models The following sets and parameters are used for the formulation of the mathematical model. Sets /indices: a A a Aa I LS ool) A) My A El ae A sustainable process ensures at all times that the system thrives in all the challenges it faces. It is worth noting that measuring all the social factors in a single process of decision-making will be extremely difficult. Pishvaee et al. (2012) developed some standards to plan and implement corporate social responsibility in organizations, but a standard framework developed by the ISO termed the “International Guidance Standard on Social Responsibility-ISO 26000" (Montoya-Torres, 2015) is adopted. The classifications which are relevant to SCM are presented in Table 1. The safety factor (number of lost days caused by injuries at work) is considered by reducing the incidents/ accident rates.
Table 2. Production Costs, Fixed Costs for Plants and Distribution Centres
Table 2. Production Costs, Fixed Costs for Plants and Distribution Centres
Table 3. Cost of Transportation between Nodes (Naira/ unit)
Table 3. Cost of Transportation between Nodes (Naira/ unit)
Table 4. Carbon Dioxide Emissions from Trucks Shipping Raw Materials and Products (Tonnes of Carbon Dioxide) RESULTS AND DISCUSSION
Table 4. Carbon Dioxide Emissions from Trucks Shipping Raw Materials and Products (Tonnes of Carbon Dioxide) RESULTS AND DISCUSSION
Table 5. Weights for the Objective Functions using the AHP Also, optimal values of quantity of products produced in different time periods and quantity of raw materials supplied to meet the Just-In-Time approach are shown in Table 6, while the optimal quantity of products shipped to distribution centres to meet demand from customer zones is given in Table 7. The optimal values of these decision variables optimized the model with the objective functions values given in Table 8.
Table 5. Weights for the Objective Functions using the AHP Also, optimal values of quantity of products produced in different time periods and quantity of raw materials supplied to meet the Just-In-Time approach are shown in Table 6, while the optimal quantity of products shipped to distribution centres to meet demand from customer zones is given in Table 7. The optimal values of these decision variables optimized the model with the objective functions values given in Table 8.
Optimal raw material (Raw) quantity supplied to plants in each time period, X1:s(Naira/ Unit) Table 6. Optimal Solutions of Decision Variables in each Time Period
Optimal raw material (Raw) quantity supplied to plants in each time period, X1:s(Naira/ Unit) Table 6. Optimal Solutions of Decision Variables in each Time Period
[able 7. Optimal Quantity of Products Shipped from Plants to DC in each Time Period (X3 rat) Table 8. Objective Function Values
[able 7. Optimal Quantity of Products Shipped from Plants to DC in each Time Period (X3 rat) Table 8. Objective Function Values
The results obtained by running the numerical data through the model explain its effectiveness and feasibility. Using the preliminary analysis, the fixed demand quantities for each of the customer zones lead to the amount of raw materials (Table 6) supplied to each plant to meet these demands, considering the processes and the capacities of the production units. The conflicting characteristic of the multi-objective model comes into play also. The objective function value when the cost minimization model was run alone gave N221,462,800, but when run with the conflicting environmental and social models, the optimal solution was 232,615,300. This is acceptable for reasons that a considerable price has to be paid to operate a green and sustainable SCN in which the environment is not polluted. The emissions from supply trucks and production plants from the entire supply chain as shown by the optimal value of the second objective gives 22.343 tonnes of CO2, with the transportation activities resulting in 7.47 tonnes of CO2 emissions. This is equivalent to 1.44E-04 tonnes of CO2 per km (estimated total distance travelled in the SCN is 52, 000 km). Converting this further, we got 144 grams of CO2 per km and by international standard; the accepted CO2 emission for 2017 is pegged at 175g of COz per km (ICCT, 2014). This result shows that the activities of the supply chain do not violate international environmental regulations as regards CO 2 emissions from trucks involved. Besides, the sensitivity of the solution can be analysed by varying these parameters in the model in order to evaluate their effects on the optimal solution. The ranges within which the solutions are acceptable were calculated by the optimizer and are presented for selected model parameters. The effect of changes in demand on the objective function cost is
The results obtained by running the numerical data through the model explain its effectiveness and feasibility. Using the preliminary analysis, the fixed demand quantities for each of the customer zones lead to the amount of raw materials (Table 6) supplied to each plant to meet these demands, considering the processes and the capacities of the production units. The conflicting characteristic of the multi-objective model comes into play also. The objective function value when the cost minimization model was run alone gave N221,462,800, but when run with the conflicting environmental and social models, the optimal solution was 232,615,300. This is acceptable for reasons that a considerable price has to be paid to operate a green and sustainable SCN in which the environment is not polluted. The emissions from supply trucks and production plants from the entire supply chain as shown by the optimal value of the second objective gives 22.343 tonnes of CO2, with the transportation activities resulting in 7.47 tonnes of CO2 emissions. This is equivalent to 1.44E-04 tonnes of CO2 per km (estimated total distance travelled in the SCN is 52, 000 km). Converting this further, we got 144 grams of CO2 per km and by international standard; the accepted CO2 emission for 2017 is pegged at 175g of COz per km (ICCT, 2014). This result shows that the activities of the supply chain do not violate international environmental regulations as regards CO 2 emissions from trucks involved. Besides, the sensitivity of the solution can be analysed by varying these parameters in the model in order to evaluate their effects on the optimal solution. The ranges within which the solutions are acceptable were calculated by the optimizer and are presented for selected model parameters. The effect of changes in demand on the objective function cost is
The allowable variations in the costs of shipping raw materials to plants and products to customer zones are shown in Fig. 5. Expectedly, each increase in transportation cost leads to a corresponding increase in the total cost of the supply chain. This can be explained in terms of the costs incurred as a result of increases in logistics management charges, and the additional costs incurred in managing a sustainable supply chain. The supply chain trucks used for supplies must emit only minimal amounts of CO2 as approved by international bodies, and this can be achieved by investing in clean trucks, which is reflected in the final costs of the supply chain. In Fig. 6, the unit holding cost makes the economic objective function to increase slightly with each increase in its value. This is due to the fact that cost is incurred in holding up products in distribution centres and warehouses. Also, at an increasing capacity, production costs tend to reduce slightly as a result of bulk purchases, mass production, and constant modes of production processes, hence the effect on the total cost of the supply chain. The weights on the objective functions largely affect the resulting values. Different weights were calculated as a result of altering the preferences of the decision makers and are plotted in Fig. 7. It can be seen that for every weight combination, an increase in the weight of an objective function leads to a corresponding increase in that objective function value.
The allowable variations in the costs of shipping raw materials to plants and products to customer zones are shown in Fig. 5. Expectedly, each increase in transportation cost leads to a corresponding increase in the total cost of the supply chain. This can be explained in terms of the costs incurred as a result of increases in logistics management charges, and the additional costs incurred in managing a sustainable supply chain. The supply chain trucks used for supplies must emit only minimal amounts of CO2 as approved by international bodies, and this can be achieved by investing in clean trucks, which is reflected in the final costs of the supply chain. In Fig. 6, the unit holding cost makes the economic objective function to increase slightly with each increase in its value. This is due to the fact that cost is incurred in holding up products in distribution centres and warehouses. Also, at an increasing capacity, production costs tend to reduce slightly as a result of bulk purchases, mass production, and constant modes of production processes, hence the effect on the total cost of the supply chain. The weights on the objective functions largely affect the resulting values. Different weights were calculated as a result of altering the preferences of the decision makers and are plotted in Fig. 7. It can be seen that for every weight combination, an increase in the weight of an objective function leads to a corresponding increase in that objective function value.
xxx et al., (2020). Supply Chain Management in a Manufacturing Industry. Nigeria Journal of Engineering Science Research (NIJESR). 3(1), pp. 48-62
xxx et al., (2020). Supply Chain Management in a Manufacturing Industry. Nigeria Journal of Engineering Science Research (NIJESR). 3(1), pp. 48-62
Fig. 6: Sensitivities of Holding Cost and Plant Capacity on Total Cost
Fig. 6: Sensitivities of Holding Cost and Plant Capacity on Total Cost
Related topics:
PRODUCTION ENGINEERINGDesign for Manufacturing and Assembly
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved