Modelling and Simulation in Engineering

Modelling and Simulation in Engineering / 2021 / Article

Research Article | Open Access

Volume 2021 |Article ID 5232814 | https://doi.org/10.1155/2021/5232814

Gladys Bonilla-Enriquez, Patricia Cano-Olivos, Li-Qun Peng, Weihua Gan, Jose-Luis Martinez-Flores, Diana Sanchez-Partida, "Modelling Sustainable Development Aspects within Inventory Supply Strategies", Modelling and Simulation in Engineering, vol. 2021, Article ID 5232814, 11 pages, 2021. https://doi.org/10.1155/2021/5232814

Modelling Sustainable Development Aspects within Inventory Supply Strategies

Academic Editor: Luis Carlos Rabelo
Received25 Oct 2019
Revised06 Feb 2021
Accepted02 Mar 2021
Published18 Mar 2021

Abstract

Nowadays, inventory management is a tool that must be extended to cover all aspects of the supply chain (SC). One of these aspects is Sustainable Development (SD) which emphasizes the balance between economic well-being, natural resources, and society. As inventory involves the use of natural and economic resources, the integration of SD criteria is important for a more efficient and sustainable SC. In this work, the most important SD variables associated with inventory management were identified. These variables were integrated as cost elements within a nondeterministic inventory control model to include SD criteria within inventory supply strategies. Through the assessment of the proposed integrated model, it was determined that, although SD practices involve additional investments, specific practices such as reuse/recycling and government incentives can increase revenue and profits. This is important for the development of government and business strategies to perform sustainable practices.

1. Introduction

Sustainable Development (SD) has its beginnings in the 1980s when the United Nations (UN) requested an investigation about natural resources and their situation in the world in the face of the evident deterioration of the environment and natural resources. This investigation led to the report entitled “Our Common Future” [1] where the term “Sustainable Development” was defined for the first time [2]. SD integrates the concern of the resource capacity of natural systems with the social, political, and economic challenges of humanity [3].

The UN and the OECD (Organization for Economic Cooperation and Development) have been working with member countries to make appropriate recommendations to reduce the impact of inefficient economic practices on the sustainable aspects of the environment and society. Within this context, the manufacture and service industries are two of the main sectors whose practices impact on the economic, environmental, and social aspects.

If unattended, the impacts on these aspects are expected to worsen as more people would require products and services, compromising the availability of environmental resources for production of raw materials. Thus, finding new solutions to achieve sustainable consumption and production is of main interest for companies (however, this requires an understanding of the environmental and social effects of products and services).

The distribution of materials and products throughout the SC is one of the major contributors to emissions of greenhouse compounds with high logistics costs [4]. Within this context, inventory management is the component of the SC which involves operations that impact on economic, environmental, and social aspects as it involves production and distribution operations that generate significant pollution (e.g., CO2 emissions and product waste) and economic loss due to inefficient management practices [57].

Hence, the importance of this work consists of the development of an inventory model to integrate SD criteria within the SC to reduce economic loss and negative impacts on SD. For the development of the model, the multicriteria analysis was performed to identify the most significant factors of the environmental, economic, and social aspects that impact on the SD of inventory supply management.

The advances of this work are presented as follows: in Section 2, a review on SD and sustainable inventory models is presented; then, in Section 3, the analysis to determine the SD factors associated to inventory is described. Section 4 presents the development of the integrated inventory control model with the cost variables representing these SD factors. A test instance developed to assess the outcomes of the inventory model is presented in Section 5 with a discussion regarding its results. Finally, Section 6 presents our conclusions and future work.

2. Sustainable Inventory Models

As discussed in [8], the formulation of sustainable tools for SC requires a multidisciplinary approach. Thus, the development of an inventory model with SD criteria involves multidisciplinary complexity [2] with different policies or strategies to reduce contaminants and economic loss [6]. In this review, deterministic and nondeterministic inventory models that have addressed sustainable aspects were analyzed. Table 1 presents a detailed analysis regarding the most recent models which have included some sustainable variables within their formulations.


WorkDescriptionVariable associated to sustainability

[5]EOQ model with the vehicle’s cost of CO2 emissionsEconomic lot size
CO2 emissions

[9]EOQ model with the cost of CO2 emissions associated to logistics and warehousing operationsEconomic lot size
Water footprint
CO2 emissions
Cap-and-trade incentives

[10]Multiobjective EOQ model with the cost of CO2 emissionsEconomic lot size
Water footprint
CO2 emissions
Environmental and social criteria

[11]EOQ model with facility location that integrates CO2 restrictions on multiple business unitsEconomic lot size
CO2 emissions and taxes
Cap-and-trade incentives

[12]EOQ and EPQ models with green costs associated to warehousing and productionEconomic lot size
CO2 emissions and taxes
Cap-and-trade incentives

[13]EOQ model with carbon footprint and transportation costsEconomic lot size
CO2 emissions
[14]EOQ model with sustainability considerationsEconomic lot size
CO2 emissions
Carbon tax
Carbon offsets and social criteria

Bonney and Jaber [5] addressed the importance of analyzing the relation of inventory to the environment and whether if it is possible to create environmentally responsible inventory planning systems. Their results suggested that ordering items in larger quantities (less frequent orders) in contrast to the traditional economic order quantity (EOQ) model can lead to reducing transportation costs and consequent CO2 emissions. Furthermore, their results implied that a cost-benefit analysis can be performed by a joint cost function between the company’s benefits and the inventory costs.

Hua et al. [9] addressed the trade in carbon emissions as an effective mechanism to reduce them. This was proposed by investigating how companies manage carbon footprints in inventory management under the carbon emission trading mechanism. They derived the optimal order quantity and analytically and numerically examined the impacts of carbon trade, carbon price, and carbon cap on order decisions, carbon emissions, and total cost.

Bouchery et al. [10] developed a sustainable EOQ model. Their results were used to provide some insights about the effectiveness of different regulatory policies to control carbon emissions. They also used an interactive procedure which allowed the decision-maker to quickly identify the best option among these solutions. The proposed interactive procedure led to a new combination of multicriteria decision analysis techniques.

Benjaafar et al. [11] used the EOQ and News Vendor models to study the extent to which carbon reduction requirements can be addressed by operational adjustments, as an alternative (or a supplement) to costly investments in carbon-reducing technologies. They also used these models to (a) investigate the impact of collaboration among companies within the same SC on their costs and carbon emissions and (b) to study the incentives that companies might have in seeking such cooperation.

Tao et al. [12] researched the joint optimal decisions on lot size in a coordinated SC between a retailer and a manufacturer under the carbon tax and cap-and-trade mechanisms. The comprehensive cost-based models were proposed to capture the influence of two carbon regulatory schemes on business decisions in a coordinated two-stage SC.

Battini et al. [13] linked sustainability aspects to the raw material lot size, from the beginning of the order purchase to its delivery at the buyer’s plant. Thus, the environmental impact of transportation and inventory was incorporated into the EOQ model. The approach was applied to represent data from industrial problems to assess the impact of sustainability considerations on purchasing decisions when compared to traditional approaches.

Arslan and Turkay [14] discussed on sustainable aspects for the standard EOQ model with a single item at a single location with no backlogging, constant lead times, and an unlimited supply. Also, they discussed on relaxations to consider multiple items at multiple locations with planned backorders, variable lead times, finite production rates, quantity discounts, imperfect quality, and resource constraints such as warehouse space.

As discussed, while companies have made efforts to increase profits by looking for the economic factor, research has provided insights regarding the importance of the environmental and social factors for this goal, and currency markets are moving in that direction [2, 15]. The works reviewed in Table 1 have demonstrated that the integration of these factors within the inventory control techniques can improve on achieving sustainability without conflicting with the economic aspect of inventory management.

In this context, the proposed research contributes with an integrated inventory supply model to address a more comprehensive integration of the economic, environmental and social factors. In contrast to the works reviewed in Table 1, where up to three variables were analyzed, the present work analyzes six variables associated with SD factors. These were identified and modelled as cost elements for their integration within an inventory control technique for uncertain demand, which is a common feature in nowadays markets. The advances of this model are described in the following sections.

3. Determination of Variables Associated to SD Factors in Inventory Management

The determination of variables associated with the SD factors can be considered a multicriteria task. This is because each factor is integrated by diverse decisions, costs, and resources that affect the sustainability of the SC. Also, depending on the context, qualitative and quantitative assessment of the importance of each factor may lead to different conclusions. In the example, in [16] it was mentioned that economic factors should be the dominant ones in inventory management. On the other hand, in [14] it was considered that environmental and social factors should be considered due to the current environmental situation.

For the present work, we extended the analyses reported in [16, 17] on metrics to measure SC performance and evaluation of sustainable supply chain indicators. The work reported in [16] concluded that the most important SC metrics related to sustainability were those presented in Table 2.


SocialEconomicEnvironmental

Health and safety
(1) Number of accidents (employees)
(2) Work conditions
(3) Number of accidents (nonemployees)
Quality
(1) On-time delivery
(2) Customer satisfaction
(3) Order fill rate
(4) Product/service availability
Emissions
(1) Level of CO2 emissions
(2) Level of CO2 emission from transport processes
(3) Level of CO2 emission from infrastructure

Noise
(1) Noise volume
(2) Time of noise emission
(3) Noise emission in urban areas
Efficiency
(1) Distribution costs
(2) Total costs
(3) Transport costs
(4) Loading capacity utilization
Natural resources utilization
(1) Energy use
(2) Water consumption
(3) Energy consumption/revenue

Employees
(1) Employee skills
(2) Employee satisfaction
(3) Percent of labor cost spent on training
Responsiveness
(1) Stock-outs
(2) Product lateness
(3) Lead time
(4) Forecast accuracy
Waste and recycling
(1) Level of waste
(2) Level of products recycled
(3) Level of products reused

To provide a more general model, we performed a focus group discussion with different professionals in the manufacturing and logistics fields for the assessment of these metrics on inventory management. In this way, the metrics presented in Table 2 were extended to those presented in Table 3.


SocialEconomicEnvironmental

Health and safety
(1) Number of accidents (employees)
(2) Number of accidents (nonemployees)
(3) Number of accidents associated to company’s vehicles
(4) Wireless electronics conditions
(5) Toxic and hazardous emissions
Quality
(1) On-time delivery
(2) Customer satisfaction
(3) Order fill rate
(4) Product quality
(5) Supplier performance
Emissions
(1) Level of CO2 emissions
(2) Level of CO2 emission from transport processes
(3) Level of CO2 emission from infrastructure
(4) Carbon footprint—ordering
(5) Carbon footprint—holding

Waste
(1) Water pollution
(2) Air pollution
(3) Solid waste
Efficiency
(1) Information management costs
(2) Distribution costs
(3) Inventory costs
(4) Transport costs
(5) Loading capacity utilization
Natural resources utilization
(1) Energy use
(2) Water consumption
(3) Energy consumption/revenue
(4) Fossil fuel consumption
(5) Energy efficiency per ton kilometer

Employees
(1) Hours efficiently worked (energy optimization)
(2) Clean workplace
(3) Good use of work tools
(4) Classification of waste in work areas
(5) Disposal of work waste
Responsiveness
(1) Stock-outs
(2) Lead time
(3) Forecast accuracy
(4) New product—time to market
(5) New product—time to first date
Waste and recycling
(1) Level of waste
(2) Level of products recycled
(3) Level of products reused
(4) Level of landfill waste
(5) Level of biodegradable materials used

With this information, we proceeded to determine the most important variables or metrics between each other based on a multicriteria analysis. For the selection of the analysis tool, we studied the work reported in [17] where the AHP technique led to determine that environmental and social factors could contribute more to the sustainability of the Indian automotive industry.

3.1. AHP Analysis

For our analysis, the goal of the AHP is defined as the identification of the most important alternatives to be modelled as cost variables within the proposed inventory model. For this, the SD factors are set as the criteria at level 1 of the AHP structure. Then, the aspects of each factor are considered as the subcriteria for level 2. Finally, the alternatives from which the variables will be identified and modelled are set at level 3 of the AHP structure. Figure 1 presents the description of the AHP model with the associated abbreviations.

Figure 2 presents the weights (i.e., importance) estimated for the criteria, subcriteria and alternatives defined by the AHP structure of Figure 1. These weights were estimated based on the feedback of professionals in the manufacturing and logistics fields and the results reported in [16]. The details of the professionals’ profiles are presented in Table 4.


Field

SCXXXXXXXX
IMXXXXXXXX
SDXXXX

With this data, the AHP weights associated with the interactions between all criteria, subcriteria, and alternatives were computed. These are presented in Table 5.


GoalConsistency ratioCriteria/factorsAlternatives
L1L2L3

Definition of SD criteria that impact inventory through the SC0.0516Eco 0.5936Eco_Q 0.2234A_Eco_Q1 0.0075A_Eco_Q2 0.0269A_Eco_Q3 0.0375A_Eco_Q4 0.0568A_Eco_Q5 0.0947
Eco_E 0.0887A_Eco_E1 0.0026A_Eco_E2 0.0045A_Eco_E3 0.0159A_Eco_E4 0.0213A_Eco_E5 0.0443
Eco_R 0.2815A_Eco_R1 0.0366A_Eco_R2 0.0895A_Eco_R3 0.1114A_Eco_R4 0.0245A_Eco_R5 0.0165
0.0176Env 0.2493Env_E 0.0523A_Env_E1 0.0024A_Env_E2 0.0035A_Env_E3 0.0133A_Env_E4 0.0267A_Env_E5 0.0065
Env_N 0.0599A_Env_N1 0.0028A_Env_N2 0.0089A_Env_N3 0.0288A_Env_N4 0.0036A_Env_N5 0.0158
Env_W 0.1371A_Env_W1 0.0076A_Env_W2 0.0069A_Env_W3 0.0368A_Env_W4 0.0496A_Env_W5 0.0362
0.0036Soc 0.1571Soc_H 0.0172A_Soc_H1 0.0010A_Soc_H2 0.0046A_Soc_H3 0.0009A_Soc_H4 0.0045A_Soc_H5 0.0062
Soc_W 0.0485A_Soc_W1 0.0298A_Soc_W2 0.0057A_Soc_W3 0.0130
Soc_E 0.0913A_Soc_E1 0.0061A_Soc_E2 0.0028A_Soc_E3 0.0212A_Soc_E4 0.0157A_Soc_E5 0.0455

From Table 5 it is obtained that the economic criterion (Eco) is the most significant with . The environment criterion (Env) is the second most significant with , and the social criterion (Soc) is the least significant with . For each criterion, also the most significant subcriterion is determined. For example, for Eco, the subcriterion responsiveness (Eco_R) is the most significant with . Finally, for each subcriterion, the most significant alternative is obtained (marked in bold). By following the previous example, for Eco_R, the most significant alternative is lead time (A_Eco_R3) with (see abbreviations presented in Figure 1).

Table 6 presents the interpretation of the results of the AHP analysis of Table 5. Note that these results will support the definition of variables that will be modelled as cost elements for the proposed inventory control model with SD criteria.


GoalCriteria/factorsFinal results (most significant alternative)
L1L2

Definition of SD criteria that impact inventory through the SCEconomic 0.5936Quality 0.2234Customer satisfaction 0.0947
Efficiency 0.0887Inventory costs 0.0443
Responsiveness 0.2815Lead time 0.1114
Environmental 0.2493Emissions 0.0523Level of CO2 emission from transport processes 0.0267
Natural resources 0.0599Energy consumption/revenue 0.0288
Waste and recycling 0.1371Level of products reused 0.0496
Social 0.1571Health and safety 0.0172Toxic and hazardous emissions 0.0062
Waste 0.0485Water pollution 0.0298
Employees 0.0913Disposal of work waste 0.0455

As presented, there are nine variables (alternatives) associated with the sustainability of inventories through all SD factors (criteria). To equally address the variables through the SD factors, the two most significant variables were selected from each criterion. These are summarized in Table 7.


EconomicQuality
Lead time
EnvironmentalLevel of CO2 emission from transport processes
Level of products reused
SocialWater pollution
Disposal of work waste

4. Development of the SD Inventory Model

Figure 3 presents the general structure of the SC which consists of three main entities: raw material suppliers, end-product manufacturers, and end-product retailers or clients. Here, the final entities determine the global requirements (demand) of end-products to be produced and transported through the SC. These requirements are to be periodically covered by the delivery of lots of size which is the basis of the economic aspect of inventory control management. As presented, the availability of depends of different aspects of the SC which are related to the SD variables identified in Table 7.

Thus, the integration of each SD variable within the inventory model considering the relationships and dependencies identified in Figure 3 is performed as follows: (i)Quality involves producing products with the minimum defects and the features required by the customer. Within SD criteria, achieving the highest quality supports the reduction of unnecessary waste and reconditioning processes. As the rejection of a lot is based on the individual detection of defects, the quality cost is considered as an investment to be associated with units(ii)Lead time is associated to prompt delivery of products or raw material. Inefficient delivery is associated with rejection rates of lots, unnecessary additional transportation costs, and CO2 emissions. In this regard, failure to comply with the lead time can be considered as a penalty cost to be associated to lots(iii)CO2 emissions are associated with transportation. If lot sizes are not adequately estimated, unnecessary additional transportation may take place which would produce CO2 emissions. Thus, the emission cost CE is considered as a cost associated with the transportation of lots(iv)The level of products that are reused is an important sustainability aspect. This practice consists of using an item for other purposes, either similarly to the original purpose or to different ones. This is different from recycling because it does not involve reconditioning or breaking down into raw materials. Thus, it can lead to save time, money, energy, and other resources within the company [18, 19]. Depending of the effort or additional steps for reuse (i.e., change of packaging/labelling and washing), this can include a small cost with an important return value. In this case, it is considered as an incentive CPR which is dependent of a percentage of a lot(v)Water pollution is an aspect which is commonly omitted in the practice, and it can take place in any stage of the production process (i.e., cleaning and maintenance). The water pollution cost is considered as a shared-cost associated with producing a unit of product(vi)Disposal of work waste is also an aspect that is not considered in practice. This requires additional investment for green practices associated with proper disposal of units which, if the quality is not absolute, is dependent on a percentage of the lot size. Thus, is considered as a cost associated with the lot size

In inventory management tools, there are three main costs: holding costs, ordering costs, and safety stock costs [20]. An inventory control policy must determine a balance between these costs to reduce the impact on the SC.

One of the most widely used models for inventory control under uncertainty is the continuous review model or (, ) model [21], where defines the optimal lot size and the reorder point which depends on the lead time and average demand [22].

In general terms, the (, ) model considers the following constants and variables: is the order cost per lot; is the holding cost per unit of product; is the purchase cost per unit of product; is the stock-out cost per unit of product; is the cumulative demand for a planning horizon, and is the average daily, weekly, or monthly demand; LT is the lead time; and are the mean and standard deviation of the demand during the lead time; is the standard loss function; is the expected shortage of units of products per cycle. The economic lot quantity and the reorder point then are estimated as presented in Figure 4 [23].

Within the determination of the lot size, is equivalent to as it is associated with the units of products not delivered per inventory cycle. Another cost to be performed each time a lot is ordered is the transportation cost. While this can be considered within , the CO2 emission costs are not frequently considered. In [23], a cost metric, based on the transportation distance and CO2 emissions generated per kilometer, was determined as follows: where is the average CO2 emission per kilometer in grams, dist is the total traveled distance between the supplier and the warehouse, and is a CO2 emission tax per gram.

About costs associated with units of products, the quality cost can be added to as an investment to keep products in conforming conditions. Also, the cost of water pollution can be added to the holding cost as a shared cost between the supplier and the retailer. Figure 5 presents the adapted (, ) model with these four SD cost variables:

The last two cost variables, and are considered to be dependent on the lot size. Thus, these are integrated into the total cost formulation of the (, ) model as described below: where (a)Total order cost (b)Total holding cost of cycle inventory (c)Total holding cost of safety stock (d)Total shortage cost (e)Total incentive for reuse of products (f)Total disposal cost of waste

5. Assessment of the Model

The textile industry is one of the most important manufacturing industries. However, it is also one of the industries that have a more negative impact on the environment and social welfare. In this case, the proposed model can be used to reduce the costs associated with these impacts.

Let us consider the inventory production and distribution of cotton t-shirts of 250 grams. Based on the feedback obtained from two retailers, the annual demand for this product was estimated as  units with a delivery cost of 100 USD/lot ().

For this product, the associated cost elements of the integrated model were estimated as follows (the same methodology can be performed for different products): (i)Quality is assured by the implementation of diverse processes and personnel. According to [24], the salary of a quality engineer is approximately 700 USD per month. In practice, approximately 20% of the products are sampled for quality control. This leads to approximately per month. Considering that sampling represents approximately 30% of the activities performed by the quality engineer, the unit cost of quality is estimated as (ii)The unit cost , which considers raw material and production costs, averages 3.0 USD/unit [25].(iii)Holding cost is minimal as t-shirts do not require specific warehousing conditions. It is estimated as 0.05 USD/unit(iv)Nowadays, some countries have a tax policy to regulate the contamination of water caused by textile manufacturing [26]. For this case, a t-shirt requires approximately 2,700 liters of water or 2.7 cubic meters [27]. If the task averages 0.20 USD/m3, is approximated as (v)It is expected that manufacturers perform the appropriate measures to dispose of waste. Collecting and disposing of a batch of combined waste approximately costs 400 USD/ton [28]. In this case, a cost of 80 USD is considered for (vi)Reused products can be considered as refurbished or substitution products. In practice, this accounts for approximately 5% of a lot. Thus, (vii)For a stock-out unit of product, a cost is considered due to loss and additional penalties. This leads to define (viii)To estimate , it is important to determine the transportation route from the (supplier) manufacturer to the seller (retailer). Figure 6 presents an example of the route with a length of 375 km. Based on the work reported in [29], for a standard vehicle with a cargo capacity between 1.305 tons and 1.740 tons, an emission of 225 gCO2/km is generated if diesel is consumed. This leads to an estimate total of 85.0 kgCO2 for the trip. In practice, an emission tax is established to try to reduce CO2 emissions. In this case, a reference of 0.0020 USD per gCO2/km, this results in

Table 8 presents the overview of the previously defined cost variables together with the additional variables for the (, ) model. As the lead time is defined in days, a reference of daily demand is considered for and . Also, as uncertain demand is considered, a coefficient of variability of 25.0% is assumed.


40000Units0.05USD per unit
Days360100.00USD per lot
112Units42.19USD per lot
28Units0.32USD per unit
LT10Days0.54USD per unit
1120Units80.00USD per lot
89Units
3.00USD per unit
4.50USD per unit

On the other hand, Table 9 presents the results of the iterative process for the estimation of and . As presented, convergence is achieved on the 3rd iteration. With this result, where  units and  units, . If no costs associated with SD criteria are considered, the following results are obtained: and . In such cases, does not change significantly; however, increases by a factor of 3.55. This is expected because if SD criteria are to be considered, more care must be taken to establish the economic lot.



35360.98212.1013070.00650.582.59
35680.98202.1013070.00650.582.62
35680.98202.10R313070.00650.582.62
35680.98202.1013070.00650.582.62
35680.98202.1013070.00650.582.62
35680.98202.1013070.00650.582.62
35680.98202.1013070.00650.582.62

Table 10 presents the total cost analysis for both scenarios. As presented, even though the SD model has more costs due to waste disposal and order costs with CO2 emissions and quality assurance, one significant income may come from investment in product reuse. This practice can represent a higher incentive which can compensate for the other SD costs. This is an improvement on the standard case where product reuse is not performed.


(, ) SD model(, ) model

=1121.08315.53
=89.20316.93
=9.3812.00
=29.361.36
=2000.00
=896.86
Annual total inventory cost =145.88645.82

6. Conclusions and Future Work

An important aspect to perform SD practices is the economic effort needed for their implementation. As discussed, there are specific SD factors associated with inventory control that must be carefully managed in order to maintain economic benefit.

For this purpose, six SD cost variables were identified and modelled within the (, ) model for assessment of their impact and outcomes of their implementation. As discussed by other works, implementation of SD practices can increase the costs of the company significantly. This was observed in the analysis presented in Table 10. Particularly, those associated with lots (i.e., emission cost due to transportation and waste management) represent the highest costs. However, the opportunity of product reuse can lead to significant economic benefits which can compensate these costs. This can also lead to important advantages over standard practices where SD criteria are not considered.

Even though these results lead to define specific practices to obtain economic benefits from SD factors, additional work must be performed to extend on the analysis and identification of other SD criteria. In example, stored inventory can lead to emission of contaminants which can affect the workers’ health. Thus, this should be considered within the lot ordering process. Also, considering other inventory models can lead to improve the applicability in other industries.

Data Availability

The data used for the present work is described in the manuscript. Where applicable, other sources have been referenced.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

The Article Processing Charge (APC) was funded by Universidad Popular Autonoma del Estado de Puebla A.C.

References

  1. W. Visser and G. H. Brundtland, Our Common Future: Report of the World Commission on Environment and Development, Greenleaf Publishing in association with GSE Research, 1987.
  2. L. E. Rincón, M. J. Valencia, V. Hernández, L. G. Matallana, and C. A. Cardona, “Optimization of the Colombian biodiesel supply chain from oil palm crop based on techno-economical and environmental criteria,” Energy Economics, vol. 47, pp. 154–167, 2015. View at: Publisher Site | Google Scholar
  3. L. Kahle and E. Gurel-Atay, Communicating Sustainability for the Green Economy, M. E. Sharpe, New York, 2014.
  4. EPA, United States Environmental Protection Agency, 2019, https://www.epa.gov/history.
  5. M. Bonney and M. Jaber, “Environmentally responsible inventory models: Non-classical models for a non- classical era,” International Journal Production Economics, vol. 133, no. 1, pp. 43–53, 2011. View at: Publisher Site | Google Scholar
  6. Y. Bouchery, A. Ghaffari, and Z. Jemai, Key performance indicators for sustainable distribution supply chains: set building methodology and application, Ecole Centrale Paris, Cahiers de Recherche 2010-08, Laboratoire Génie Industriel., 2010.
  7. W.-S. Chang, Y.-S. Tsai, J.-Y. Wang, H.-L. Chen, W.-H. Yang, and C.-C. Lee, “Sex hormones and oxidative stress mediated phthalate-induced effects in prostatic enlargement,” Environment International, vol. 126, pp. 184–192, 2019. View at: Publisher Site | Google Scholar
  8. J. Linton, R. Klassen, and V. Jayaraman, “Sustainable supply chains: an introduction,” Journal of Operations Management, vol. 25, no. 6, pp. 1075–1082, 2007. View at: Publisher Site | Google Scholar
  9. G. Hua, T. Cheng, and S. Wang, “Managing carbon footprints in inventory management,” International Journal of Production Economics, vol. 132, no. 2, pp. 178–185, 2011. View at: Publisher Site | Google Scholar
  10. Y. Bouchery, A. Ghaffari, Z. Jemai, and Y. Dallery, “Including sustainability criteria into inventory models,” European Journal of Operational Research, vol. 222, no. 2, pp. 229–240, 2012. View at: Publisher Site | Google Scholar
  11. S. Benjaafar, Y. Li, and M. Daskin, “Carbon footprint and the management of supply chains: insights from simple models,” IEEE Transactions on Automation Science and Engineering, vol. 10, pp. 99–116, 2012. View at: Publisher Site | Google Scholar
  12. Z. Tao, A. Guiffrida, and O. Offodile, “Carbon emission modeling in a two stage supply chain,” American Journal of Management, vol. 17, no. 1, pp. 82–92, 2017. View at: Google Scholar
  13. D. Battini, A. Persona, and F. Sgarbossa, “A sustainable EOQ model: theoretical formulation and applications,” International Journal of Production Economics, vol. 149, no. 145-153, pp. 145–153, 2014. View at: Publisher Site | Google Scholar
  14. M. Arslan and M. Turkay, “EOQ revisited with sustainability considerations,” Foundations of Computing and Decision Sciences, vol. 38, no. 4, pp. 223–249, 2013. View at: Publisher Site | Google Scholar
  15. R. Contreras, R. Aguilar, and O. Cuauhtemoc, “Desarrollo sostenible (semblanza histórica),” Revista del Centro de Investigación, vol. 10, no. 37, pp. 101–121, 2012. View at: Google Scholar
  16. W. Piotrowicz and R. Cuthbertson, “Performance measurement and metrics in supply chains: an exploratory study,” International Journal of Productivity and Performance Management, vol. 64, no. 8, pp. 1068–1091, 2015. View at: Publisher Site | Google Scholar
  17. D. Kumar and C. P. Garg, “Evaluating sustainable supply chain indicators using fuzzy AHP,” Benchmarking: An International Journal, vol. 24, no. 6, pp. 1742–1766, 2017. View at: Publisher Site | Google Scholar
  18. M. Galbreth, T. Boyaci, and V. Verter, “Product reuse in innovative industries,” Production and Operations Management, vol. 22, no. 4, pp. 1011–1033, 2013. View at: Publisher Site | Google Scholar
  19. H. Yu and W. Solvang, “A general reverse logistics network design model for product reuse and recycling with environmental considerations,” The International Journal of Advanced Manufacturing Technology, vol. 87, no. 9-12, pp. 2693–2711, 2016. View at: Publisher Site | Google Scholar
  20. J. Korponai, A. Tóth, and B. Illés, “Effect of the safety stock on the probability of occurrence of the stock shortage,” Procedia Engineering, vol. 182, pp. 335–341, 2017. View at: Publisher Site | Google Scholar
  21. M. Hariga, “A continuous review (Q,R) model with owned and rented storage facilities,” in International Conference on Computers & Industrial Engineering, Troyes, France, 2009. View at: Publisher Site | Google Scholar
  22. P. Keskinocak, “Supply chain models: manufacturing & warehousing ISyE 3104 - lot size / reorder level (Q, R) models,” 2013, https://www2.isye.gatech.edu/~pinar/teaching/isye3104-fall2013/inventory-stochasticdemand-partiii.pdf. View at: Google Scholar
  23. S. O. Caballero-Morales, J. L. Martínez-Flores, D. Sánchez-Partida, and P. Cano-Olivos, “Impact of CO2 Emissions on Inventory Replenishment under Uncertain Demand,” in 2018 SCALE Latin American Conference, Boston, Massachusetts, 2017. View at: Google Scholar
  24. Indeed, “Indeed,” Salaries of Quality Engineers in Mexico, 2018, https://www.indeed.com.mx/salaries/Ingeniero/a-de-calidad-Salaries. View at: Google Scholar
  25. Modaes, “How much does it cost to produce a t-shirt? (in Spanish),” 2018, https://www.modaes.es/back-stage/cuanto-vale-hacer-una-camiseta.html. View at: Google Scholar
  26. Z. Wu, X. Guo, C. Lv, H. Wang, and D. di, “Study on the quantification method of water pollution ecological compensation standard based on emergy theory,” Ecological Indicators, vol. 92, pp. 189–194, 2018. View at: Publisher Site | Google Scholar
  27. S. Benson, “It takes 2. 720 liters of water to make just one T-shirt,” Refinery, vol. 29, 2018. View at: Google Scholar
  28. M. Ishikawa, “Optimum cost sharing of sorted waste collection between households and local authority considering consumer inconvenience: rational basis of shared responsibility,” Environmental Economics and Policy Studies, vol. 4, no. 4, pp. 235–251, 2002. View at: Google Scholar
  29. S. O. Caballero-Morales and J. L. Martínez-Flores, “A methodology for integration of CO2 emissions on the single-facility location problem,” in Proceedings of the 2017 International Symposium on Industrial Engineering and Operations Management (IEOM), Bristol, UK, 2017. View at: Google Scholar

Copyright © 2021 Gladys Bonilla-Enriquez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views146
Downloads165
Citations

Related articles