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Dynamic Simulation-Based Assessment of Supply Chain Sustainability
CHAPTER
Dynamic SimulationBased Assessment of Supply Chain Sustainability
15
Arief Adhitya*, Iskandar Halim*, Rajagopalan Srinivasanx, 1 *Institute of Chemical and Engineering Sciences, Jurong Island, Singapore x Indian Institute of Technology Gandhinagar, Ahmedabad, Gujarat, India 1 Corresponding author: E-mail: [email protected]
15.1 INTRODUCTION Sustainability has become a key agenda for every industry in the face of mounting environmental challenges, shortages of natural resources, growing awareness of social responsibility, and the need to remain profitable. The chemical process industry, in particular, has a strong emphasis on sustainability due to the nature of its business. The industry involves extraction of raw materials such as crude oil, gas, and minerals, processes which are highly energy intensive, and handling of large volume of toxic, flammable, and hazardous chemicals. The push for sustainability has pressurized the chemical process industry to consider the sustainability implications of its supply chain operation. The important role of supply chain management for sustainability has been highlighted in surveys conducted by Deloitte (2010) and McKinsey (2010). While sustainability is commonly viewed as having three pillars (also known as the triple bottom line)d economic, environmental, and social aspects (Bonini, 2011), the economic and environmental aspects have received more attention than the social aspect. The integration of environmental thinking and supply chain management is termed as green supply chain management (GSCM) and aims to help companies improve their environmental performance along the supply chain. In the literature, the scope of GSCM is wide and ranges from green purchasing to product design, material sourcing and selection, benign manufacturing, delivery of the final product to the customers as well as end-of-life management of the product after its useful life (Srivasta, 2007). In the process systems engineering literature, GSCM has received much attention, mostly in the area of supply chain planning and design. A common approach is to formulate a mathematical optimization model for maximum economic benefits and minimum environmental impacts. Decisions include selection of appropriate Computer Aided Chemical Engineering, Volume 36. ISSN 1570-7946. http://dx.doi.org/10.1016/B978-0-444-63472-6.00015-X © 2015 Elsevier B.V. All rights reserved.
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raw materials, suppliers, technologies, or transportation routes (Hugo and Pistikopoulos, 2005; Bojarski et al., 2009; Guille´n-Gosa´lbez and Grossmann, 2010; Mele et al., 2011). Economic performance can be measured through indicators such as profit or customer satisfaction. Environmental indicators include metrics such as waste reduction algorithm (Cabezas et al., 1999) and life cycle assessment (LCA)-based metrics such as CML 2001 (Guine´e, 2002). While most of the publications focus on planning and design, less attention is given to operational aspects and dynamics of the supply chain operation. Supply chain is the network of suppliers, manufacturing plants, warehouses, and distribution channels organized to acquire raw materials, convert them to finished products, and distribute these products to customers. Supply chain decisions such as sourcing of raw materials, intermediates, and packaging; plant sites selection; production, storage, and transportation policies have significant impact on sustainability performance. Supply chain operation is complex as it involves numerous interacting entities with various roles and constraints. This results in complex dynamics, which in turn could lead to unforeseen domino effects. Information delay, limited visibility, and the presence of various uncertainties further complicate supply chain decision-making. Consequently, a decision taken by an entity may be optimal for its own objective, but its impact on the overall supply chain performance, both economic and environmental, may not be immediately obvious. An integrated analysis of the impacts of decisions on the overall system is required. These motivate the use of supply chain simulation models, which could capture the behavior of the entities, their interactions, the resulting dynamics, and evaluate the overall performance of the supply chain. Dynamic simulation offers a good way to uncover direct and indirect effects of supply chain decisions on sustainability performance. In this work, we present a dynamic supply chain simulation model integrated with indicators from LCA for assessment of supply chain sustainability. We first describe a typical supply chain operation in Section 15.2, followed by dynamic simulation model of the supply chain operation in Section 15.3. Section 15.4 presents sustainability assessment case studies from two different supply chains, i.e., diaper and detergent. Finally, Section 15.5 gives some concluding remarks.
15.2 SUPPLY CHAIN OPERATION AND SUSTAINABILITY A typical supply chain consists of suppliers, manufacturer, distributors, and retailers/ customers (Figure 15.1). The manufacturer and distributor belong to a focal enterprise and have several departments performing various supply chain functions while interacting with one another. These departments are sales, operations, procurement, and storage. The sales department deals with customers and accepts their orders. In some cases, the sales department also makes demand forecast. This actual/forecast demand information is communicated to the operations department, who manages production. Based on this demand information and also inventory information
15.2 Supply Chain Operation and Sustainability
Resources from Environment Raw materials
Transport
Manufacturer
Distributor
Transport
Retailer/Customer
Employment
OperaƟons
Societal Benefits
Procurement
Transport
Electricity
Storage
Water emission
Social Impacts
Sales
Air emission
Water
Health and safety
Raw material supplier
Fuel
Packaging and solid waste
Burden to Environment
FIGURE 15.1 Typical supply chain with its environmental and social impacts.
from the storage department, the operations department decides how much product to make. The operations department and the storage department communicate with the procurement department for procurement of the required raw materials. The storage department manages both raw materials and product inventory. There may also be a logistics department that manages raw materials and product transportation. Each of these departments follows certain policies in performing its functions. The sales department may have a policy to decide which customer orders to accept, a policy to decide pricing, and a policy for demand forecasting. The operations department may follow a certain scheduling policy to decide production rates and schedule. The procurement department has a procurement policy, e.g., procurement at a regular interval or after reaching a certain inventory level. The storage department may have a storage policy to manage the different raw materials and product inventory. Thus, each of these departments is an independent and autonomous entity making its own decisions in the supply chain operation while interacting with one another. At the supply chain level, the manufacturer procures raw materials from different suppliers, manufactures the products, and sends them to the distributor. The distributor typically operates in a push-mode; it keeps a certain level of inventory to fulfill
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retailer/customer orders. Different procurement policies for replenishment of products can be adopted by the distributor. For example, under the fixed interval policy, procurement is done at regular intervals to bring its inventory back to a certain topup level. The distributor places orders to the manufacturer, which may operate in push-mode (products manufactured based on demand forecast) or pull-mode (products manufactured upon receiving the distributor’s orders). Policies and decisions made by each of the above entities will affect the sustainability performance of the overall supply chain. Sustainability of supply chain operation can be assessed through its economic, environmental, and social impacts, as shown in Figure 15.1. Economic sustainability is related to costs and profitability. The environment is impacted as supply chain operation consumes resources from the environment (raw materials, fuel, water, and electricity) and releases a burden to the environment (air emission, water emission, packaging, and solid waste). Social impacts include employees’ health and safety, employment, and other societal benefits. These impacts can be modeled and measured through various indicators. Modeling of the supply chain operation for sustainability assessment is described in the next section.
15.3 DYNAMIC SIMULATION MODEL OF SUPPLY CHAIN OPERATION A supply chain consists of networks of entities at different levels and can be seen as a sociotechnical system in which the physical and the social networks of actors involved in its operation collectively form an interconnected complex system. The actors determine the operation of the physical network, and the physical network affects the behavior of the actors. Decision-making in the supply chain is distributed across the various actors, each with its own goals and tasks. For example, the operations department makes products and the storage department manages inventory. These actors communicate with one another to get the information necessary to perform their own tasks or to call for certain actions. Agent-based modeling is a suitable approach to model complex systems comprising of interacting autonomous agents (Macal and North, 2005). In this approach, a system is described by defining the actors (agents) and the interactions between them. The system behavior then emerges from the behavior of the model components and their interactions. Instead of taking a top-down view in modeling, the model is hence constructed from a bottom-up perspective. Because of this, agentbased modeling is considered a natural approach for systems involving distributed and decentralized decision-making. Each actor in the supply chain operation is modeled as an agent with its own activities and interactions with other agents, as summarized in Table 15.1. Policies are implemented to guide the agents’ decision-making in performing their activities. In this work, we do not explicitly model the logistics department and transport; material transport is implemented through time delay between dispatch and arrival at destination.
15.3 Dynamic Simulation Model of Supply Chain Operation
Table 15.1 Agent Activities and Interactions in Supply Chain Operation Agent
Activity
Interaction with
Retailer/Customer
Place Order
Distributor (Sales)
Distributor
Receive order Accept order Assign order Process order
Retailer/Customer Retailer/Customer Distributor (operations) Distributor (sales), distributor (storage) Distributor (operations)
Sales
Operations Storage
Procurement
Manufacturer
Sales
Operations
Storage
Procurement
Raw material supplier
Release product for delivery Manage product inventory Buy product
Receive order Accept order Assign order Request raw materials Make product Store products Release raw materials for production Manage raw material and product inventory Buy raw materials
Supply raw materials
Distributor (procurement) Distributor (operations), distributor (storage), manufacturer (sales) Distributor (procurement) Distributor (procurement) Manufacturer (operations) Manufacturer (storage) Manufacturer (sales) Manufacturer (storage) Manufacturer (operations) Manufacturer (procurement) Manufacturer (operations), manufacturer (storage), raw material supplier Manufacturer (procurement)
The agent-based model can be implemented in an agent-based modeling platform such as the Repast simulation toolkit in Java (North et al., 2006), or a mathematical modeling platform such as MATLAB/Simulink (MathWorks, 2010). van Dam et al. (2009) compared the two approaches and identified their strengths in the context of refinery supply chain modeling. Physical elements of the refinery are modeled explicitly in the mathematical platform but not in the agent-based platform. On the other hand, the agent-based platform explicitly has a flexible structure, allowing new agents and connections between agents to be added in extensions to the model. Also, in the agent-based platform it is possible to directly reuse the same ontology and source code for modeling a different supply chain. In general, the efforts required to make changes in the model for various scenarios, ranging from operational to tactical and strategic levels, are similar in the two platforms. In terms of explaining the model and the model results, the agent-based platform
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offers a natural representation of the decision-making processes and interactions between the entities while the mathematical platform has an edge for explaining the technical process. The supply chain model has been implemented in both the agent-based platform using Repast (Behdani et al., 2010) and the mathematical platform using MATLAB/ Simulink (Adhitya and Srinivasan, 2010). In this work, we use the latter and integrate indicators from LCA into the model to enable sustainability assessment. The model uses a discrete-time representation; one day is divided into a predefined number of time ticks t. Raw material arrival at the manufacturer is modeled as: RAr ðtÞ ¼ RSr ðt LTr Þ
(15.1)
where RAr(t) is the amount of raw material r arriving at the manufacturer at time t, RSr(t) is the amount of raw material r ordered from the supplier, and LTr is the lead time between order and arrival of the raw material r. Processing in the manufacturer is modeled through the recipe Rcpr, which specifies the amount of raw material r required to make one unit of product. RUr ðtÞ ¼ Rcpr CJ amt
(15.2)
where RUr(t) is the amount of raw material r used for production at time t and CJamt is the amount of product to be made in job CJ. The material balance on raw material inventory at the manufacturer is given by: IRr ðt þ 1Þ ¼ IRr ðtÞ þ RAr ðtÞ RUr ðtÞ
(15.3)
where IRr(t) is the inventory of raw material r at time t. All activities and policies of the agents as listed in Table 15.1 have been modeled. For the complete model equations, the reader is referred to Adhitya et al. (2011). Economic performance can be measured through indicators such as manufacturer profit and distributor profit. Environmental and social performance can be measured through indicators from LCA and social/societal LCA, respectively. LCA is a widely used technique for measuring the environmental (or social for social/societal LCA) consequences assignable to a product or service. Starting from raw material extraction to production process, transportation, point of use, and final disposal, LCA charts the course of all of the consumption of natural resources and release of pollutants into air, water, and soil. However, LCA impacts are derived from a product-centric perspective without considering the effects of supply chain dynamics such as different logistics options, inventories, distribution network, and ordering policy. For example, LCA calculations are typically based on a static, average truckload level and number of trips, whereas in reality these two variables of supply chain operation are dynamic. In this work, we take a supply chain-centric perspective by considering the various supply chain decisions through dynamic simulation. By integrating LCA indicators into the simulation, we can perform sustainability assessment of the supply chain operation, as illustrated through case studies in the next section.
15.4 Case Studies
15.4 CASE STUDIES We present two different case studiesddiaper supply chain (Adhitya et al., 2011) and detergent supply chain (Wang et al., 2011). In the former, only economic and environmental aspects are considered while in the latter social impacts are also included.
15.4.1 DIAPER SUPPLY CHAIN CASE STUDY Modern disposable diapers come in a variety of styles, but their basic raw materials are essentially the same: fluff pulp, superabsorbent polymer (SAP), plastic components such as low density polyethylene and polypropylene (PP), adhesives, and elastics (EDANA, 2005). In this case study, the environmental indicator values are derived from the LCA study of disposable diapers done by the UK Environmental Agency (2005). Eleven environmental indicators are used, namely abiotic resource depletion (ARD), global warming potential (GWP), ozone layer depletion, photochemical oxidation, acidification, eutrophication (EUT), human toxicity (HT), fresh water aquatic ecotoxicity (FWAE), terrestrial ecotoxicity (TE), water usage (WU), and energy consumption (EC). These 11 indicators are evaluated at each stage of the supply chain, namely raw materials, raw material transportation, manufacturing, packaging, product transportation from manufacturer to distributor, distributor operation, and product transportation from distributor to customer. Two economic indicators are used, namely manufacturer’s profit and distributor’s profit. Table 15.2 summarizes the economic and environmental indicators used in the diaper supply chain simulation model. Table 15.2 Indicators Used in the Diaper Case Study Aspect Economic Environmental
Indicators PPRO DPRO ARD GWP ODP PO ACD EUT HT FWAE TE WU EC
Manufacturer’s profit ($) Distributor’s profit ($) Abiotic resource depletion (g Sb eq) Global warming potential (g CO2 eq) Ozone layer depletion (g CFC-11 eq) Photochemical oxidation (g C2H2) Acidification (g SO2 eq) Eutrophication (g PO4 eq) Human toxicity (g 1,4-DB eq) Fresh water aquatic ecotoxicity (g 1,4-DB eq) Terrestrial ecotoxicity (g 1,4-DB eq) Water use (kg H2O) Energy consumption (MJ)
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Two scenarios are considered here. The first involves changes in the product composition; the second looks at the ordering policy in the supply chain. The aim is to assess the sustainability impacts (economic and environmental) of different decisions along the supply chain. The simulation horizon is 60 days. The cumulative profits and environmental impacts over the simulation horizon are compared in the different scenarios
15.4.1.1 Scenario 1: changing the diaper composition The first scenario analyzes the impact of changes to the diaper composition. The mass composition of a typical diaper from years 1987, 1995, and 2005 are shown in Table 15.3. Supply chain operation with the same demand is simulated for the three different diaper compositions and the results are shown in Figure 15.2. There is a significant reduction in WU for the 2005 case compared to the 1995 and 1987 cases. This is due to the reduction in the use of fluff pulp from 1987 to 2005 (14.57 g vs 32.83 and 54.94 g). However, the figure highlights an increase in the GWP, ARD, and TE for the 2005 case from the 1995 and 1987 cases. This is caused mainly by the increase in the amount of SAP in the 2005 case (13.65 g vs 4.46 and 0.74 g), and to a lesser extent the increase in the amount of PP (7.22 g vs 3.97 and 4.36 g). The new composition also leads to an increase in the manufacturer’s profit since it requires less raw materials (41.95 g vs 49 and 67 g). Although the older compositions use less SAP, they use much more fluff pulp. The results also reveal that the environmental impacts from raw material transportation are smaller in the 2005 case than the 1995 and 1987 cases, because less raw materials are needed to be transported, resulting in fewer trips.
15.4.1.2 Scenario 2: order batching The second scenario studies the effect of different ordering policies by the distributor. More frequent ordering in smaller batches means more transportation trips and consequently higher transportation impact and cost. Different order batching by the distributor is simulated through three different distributor procurement intervals: 2, 5, and 10 days. The results are shown in Figure 15.3. Table 15.3 Diaper Compositions Component
1987 (g)
1995 (g)
2005 (g)
Fluff pulp Superabsorber (SAP) Polypropylene (PP) Polyethylene (PE) Adhesive Elastic Others Total
54.94 0.74 4.36 4.29 1.34 0.20 1.14 67.01
32.83 4.46 3.97 3.33 1.42 0.20 2.79 49.00
14.57 13.65 7.22 2.69 1.76 0.21 1.85 41.95
Adapted from EDANA (2005)
15.4 Case Studies
ACD*7E+05 3E+04* DPRO
2
1.8
ARD *8E+05
1.6 1.4 1.2
5.5E+04* PPRO
EC*2E+06
1 0.8 0.6 0.4
6E+06* WU
EUT *5E+04
0.2 0
3E+05* TE
FWAE *5E+05
2E+04* PO
GWP *8E+07 2005 5E+05* ODP
HT *7E+06
1995 1987
FIGURE 15.2 Economic and environmental indicators for different diaper compositions in the diaper case study (Scenario 1). ACD, acidification; ARD, abiotic resource depletion; EC, energy consumption; EUT, eutrophication; FWAE, fresh water aquatic ecotoxicity; GWP, global warming potential; HT, human toxicity; ODP, ozone layer depletion; PO, photochemical oxidation; TE, terrestrial ecotoxicity; WU, water usage; PPRO, manufacturer’s profit; DPRO, distributor’s profit.
In general, less frequent ordering (i.e., higher procurement interval) results in lower environmental impacts due to reduced number of transportation trips. The larger batching of order results in longer lead time and the average inventory kept by the distributor is lower (see Figure 15.4). Consequently, the distributor’s profit is increased due to lower inventory cost. On the other hand, the average raw material inventory kept by the manufacturer is increased when the distributor order is less frequent, resulting in higher inventory cost and lower manufacturer’s profit. Less frequent ordering means reduced inventory and this might have an impact on order fulfillment. This, along with the trade-off between the distributor’s and manufacturer’s profits, could be studied further to find the optimal supply chain policies for both distributor and manufacturer.
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4.39E+04*DPRO
8.02E+05*ACD 1
ARD *9.72E+05
0.9 4.35E+04* PPRO
0.8
EC *2.40E+06
0.7 6.73E+06* WU
0.6
EUT *5.81E+04
0.5
3.64E+05* TE
FWAE *4.56E+05
2.29E+04* PO
GWP *9.32E+07 Interval Days = 2
4.29E+05* ODP
HT *7.77E+06
Interval Days = 5 Interval Days = 10
FIGURE 15.3 Economic and environmental indicators for different distributor procurement intervals in the diaper case study (Scenario 2). ACD, acidification; ARD, abiotic resource depletion; EC, energy consumption; EUT, eutrophication; FWAE, fresh water aquatic ecotoxicity; GWP, global warming potential; HT, human toxicity; ODP, ozone layer depletion; PO, photochemical oxidation; TE, terrestrial ecotoxicity; WU, water usage; PPRO, manufacturer’s profit; DPRO, distributor’s profit.
15.4.2 DETERGENT SUPPLY CHAIN CASE STUDY In the detergent supply chain case study, we include social indicators in addition to the economic and environmental indicators. Extension of LCA to include the social aspects of products along their life cycle is termed as societal LCA (SLCA). The social implications of a product can be expressed through qualitative, semiquantitative, or quantitative indicators (UNEP, 2009). Qualitative indicators provide information on a particular issue, for example, measures taken by an enterprise to manage employee stress. Semiquantitative indicators categorize qualitative indicators into a yes/no form or a scale-based scoring system, for example, presence of a stress management program (yes/no) or the rating of such stress management program (bad ¼ 0, mediocre ¼ 1, good ¼ 2). Quantitative indicators can be used if the issues at stake are quantifiable.
15.4 Case Studies
100
Interval = 10
Interval = 5
Interval = 2
90
Inventory (sku)
80 70 60 50 40 30 20 10 0 0
10
20
30
40
50
60
Day
FIGURE 15.4 Distributor inventory for different distributor procurement intervals in the diaper case study (Scenario 2).
Hunkeler (2006) proposed a quantitative SLCA indicator that is based on the number of worker hours to fulfill basic societal needs. The inventories of different unit processes in the product life cycle are associated with the workers’ employment incomes and then correlated with the various life necessities they can provide. This is done by dividing the total employment hours by characterization factors that specify the number of labor hours needed to fulfill each unit of societal needs. For example, Table 15.4 shows the societal characterization factors for two countries (France and Germany) and four societal needs (housing, health care, education, and other basic needs). An average worker needs to work for 20,000 h to purchase housing in Germany. Using the life cycle inventory data, we can calculate the total employment hours associated with the supply chain operation. If the total number of hours required for making a detergent ingredient X in Germany is 7 h, then based on Table 15.4, we can infer that this contributes to 0.00035 housing units, 0.0117 health care units, 0.0233 education units, and 0.14 units of other basic needs. Table 15.4 Wages and Societal Characterization Factors Societal Needs (Work hour/unit) Country
Housing
Health Care
Education
Other Basic Needs
France Germany
25,000 20,000
400 600
375 300
67.5 50
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In this case study, the detergent manufacturer is located in France and the raw materials come from suppliers in seven countries, namely Germany, Russia, France, Canada, South Africa, The Netherlands, and Morocco, by trucks and cargo ships. The societal characterization factors for these countries are taken from Hunkeler (2006). The product is a granular detergent based on the formulation described in Saouter and van Hoof (2002) and sold to local distributors in France. The environmental indicator values are derived from Dewaele et al. (2006). The simulation horizon is 180 days. Two scenarios are presented in this case study: changing detergent formulation and supplier selection policy.
15.4.2.1 Scenario 1: changing the detergent formulation The first scenario compares the sustainability of traditional detergent production containing a phosphate ingredient (sodium tripolyphosphate or STPP) and modern detergent without STPP. Until the 1980s, STPP was a commonly used ingredient in laundry detergent formulation. Since then, due to environmental concerns, phosphates were banned in Europe and many US states and STPP was phased out and replaced with zeolites, citrates, and other builders. These ingredients are more environmentally friendly but also more costly. Supply chain operation with the same demand is simulated for the two different detergent formulations and the results are shown in Figures 15.5 and 15.6. Figure 15.5 shows that the phosphate-free detergent has a lower profit because the replacement ingredients for STPP are more costly. It also has a lower social performance than the STPP detergent because fewer employment hours are required for the phosphate-free detergent production. Figure 15.6 shows that the phosphate-free detergent has better environmental performance in all impact categories with significant reductions in EUT, HT, FWAE, solid waste, and GWP.
Profit 10 9 8 7 6 5 4 3 2 1 0 Phosphate-free STPP
Social Indicators 14 × 106 12 10
Housing Healthcare Education Others
8 6 4 2 0 Phosphate-free
STPP
FIGURE 15.5 Profit and social indicators for two detergent formulations (Scenario 1). STPP, sodium tripolyphosphate.
15.4 Case Studies
Photochemical Oxidation Acidification
Ozone Layer Depletion Global Warming Potential
STPP Energy Consumption
Eutrophication
Human Toxicity
Water Use
Fresh Water Aquatic Ecotoxicity
Solid Waste Phosphate-free
FIGURE 15.6 Environmental indicators for two detergent formulations (Scenario 1). STPP, sodium tripolyphosphate.
15.4.2.2 Scenario 2: supplier selection policy The second scenario compares two supplier selection policies: favor nearest suppliers versus no preference with regards to supplier distance. Supply chain operation is simulated for the two different policies and the results are shown in Figures 15.7 and 15.8. Figure 15.7 shows that nearer suppliers favored results in a significantly higher profit because timely arrival of raw materials allows more orders to be fulfilled. More orders completed also means more employment and therefore higher social performance. However, as shown in Figure 15.8, environmental performance of the nearer suppliers favored policy is worse. This can be attributed to the same reason; more orders completed means more raw materials used, more manufacturing, packaging, and transport impacts.
Profit 10 9 8 7 6 5 4 3 2 1 0 No Preference Nearer Suppliers
Social Indicators 14 × 106 12 10
Housing Healthcare Education Others
8 6 4 2 0 No Preference
Nearer Suppliers
FIGURE 15.7 Profit and social indicators for two supplier selection policies in the detergent supply chain (Scenario 2).
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Photochemical Oxidation Acidification
Ozone Layer Depletion Global Warming Potential
Nearer Suppliers Eutrophication
Human Toxicity Fresh Water Aquatic Ecotoxicity
Energy Consumption Water Use Solid Waste No Preference
FIGURE 15.8 Environmental indicators for two supplier selection policies in the detergent supply chain (Scenario 2).
15.5 CONCLUDING REMARKS This paper presents a dynamic simulation-based framework for assessment of supply chain sustainability. Supply chain operation involves numerous interacting entities and thus complex dynamics, which will affect the supply chain sustainability performance. LCA-based assessments are static and do not consider the dynamics in supply chain operation. Thus, there is a need for dynamic simulation integrated with LCA indicators to capture the impacts of these dynamics on the overall sustainability performance. Case studies involving changes in product composition, ordering policy, and supplier selection policy demonstrate how the proposed framework can be used for sustainability assessment while considering the dynamics involved in supply chain operation.
REFERENCES Adhitya, A., Srinivasan, R., 2010. Dynamic simulation and decision support for multisite specialty chemicals supply chain. Ind. Eng. Chem. Res. 49, 9917e9931. Adhitya, A., Halim, I., Srinivasan, R., 2011. Decision support for green supply chain operations by integrating dynamic simulation and LCA indicators: diaper case study. Environ. Sci. Technol. 45, 10178e10185. Behdani, B., Lukszo, Z., Adhitya, A., Srinivasan, R., 2010. Performance analysis of a multiplant specialty chemical manufacturing enterprise using an agent-based model. Comput. Chem. Eng. 34, 793e801. Bojarski, A.D., Laı´nez, J.M., Espun˜a, A., Puigjaner, L., 2009. Incorporating environmental impacts and regulations in a holistic supply chains modeling: an LCA approach. Comput. Chem. Eng. 33, 1747e1759. Bonini, S., 2011. McKinsey Global Survey Results: The Business of Sustainability. McKinsey & Company. Cabezas, H., Bare, J.C., Mallick, S.K., 1999. Pollution prevention with chemical process simulators: the generalized waste reduction (WAR) algorithm efull version. Comput. Chem. Eng. 23, 623e634.
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Arief Adhitya*, Iskandar Halim*, Rajagopalan Srinivasanx, 1 *Institute of Chemical and Engineering Sciences, Jurong Island, Singapore x Indian Institute of Technology Gandhinagar, Ahmedabad, Gujarat, India 1 Corresponding author: E-mail: [email protected]
15.1 INTRODUCTION Sustainability has become a key agenda for every industry in the face of mounting environmental challenges, shortages of natural resources, growing awareness of social responsibility, and the need to remain profitable. The chemical process industry, in particular, has a strong emphasis on sustainability due to the nature of its business. The industry involves extraction of raw materials such as crude oil, gas, and minerals, processes which are highly energy intensive, and handling of large volume of toxic, flammable, and hazardous chemicals. The push for sustainability has pressurized the chemical process industry to consider the sustainability implications of its supply chain operation. The important role of supply chain management for sustainability has been highlighted in surveys conducted by Deloitte (2010) and McKinsey (2010). While sustainability is commonly viewed as having three pillars (also known as the triple bottom line)d economic, environmental, and social aspects (Bonini, 2011), the economic and environmental aspects have received more attention than the social aspect. The integration of environmental thinking and supply chain management is termed as green supply chain management (GSCM) and aims to help companies improve their environmental performance along the supply chain. In the literature, the scope of GSCM is wide and ranges from green purchasing to product design, material sourcing and selection, benign manufacturing, delivery of the final product to the customers as well as end-of-life management of the product after its useful life (Srivasta, 2007). In the process systems engineering literature, GSCM has received much attention, mostly in the area of supply chain planning and design. A common approach is to formulate a mathematical optimization model for maximum economic benefits and minimum environmental impacts. Decisions include selection of appropriate Computer Aided Chemical Engineering, Volume 36. ISSN 1570-7946. http://dx.doi.org/10.1016/B978-0-444-63472-6.00015-X © 2015 Elsevier B.V. All rights reserved.
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raw materials, suppliers, technologies, or transportation routes (Hugo and Pistikopoulos, 2005; Bojarski et al., 2009; Guille´n-Gosa´lbez and Grossmann, 2010; Mele et al., 2011). Economic performance can be measured through indicators such as profit or customer satisfaction. Environmental indicators include metrics such as waste reduction algorithm (Cabezas et al., 1999) and life cycle assessment (LCA)-based metrics such as CML 2001 (Guine´e, 2002). While most of the publications focus on planning and design, less attention is given to operational aspects and dynamics of the supply chain operation. Supply chain is the network of suppliers, manufacturing plants, warehouses, and distribution channels organized to acquire raw materials, convert them to finished products, and distribute these products to customers. Supply chain decisions such as sourcing of raw materials, intermediates, and packaging; plant sites selection; production, storage, and transportation policies have significant impact on sustainability performance. Supply chain operation is complex as it involves numerous interacting entities with various roles and constraints. This results in complex dynamics, which in turn could lead to unforeseen domino effects. Information delay, limited visibility, and the presence of various uncertainties further complicate supply chain decision-making. Consequently, a decision taken by an entity may be optimal for its own objective, but its impact on the overall supply chain performance, both economic and environmental, may not be immediately obvious. An integrated analysis of the impacts of decisions on the overall system is required. These motivate the use of supply chain simulation models, which could capture the behavior of the entities, their interactions, the resulting dynamics, and evaluate the overall performance of the supply chain. Dynamic simulation offers a good way to uncover direct and indirect effects of supply chain decisions on sustainability performance. In this work, we present a dynamic supply chain simulation model integrated with indicators from LCA for assessment of supply chain sustainability. We first describe a typical supply chain operation in Section 15.2, followed by dynamic simulation model of the supply chain operation in Section 15.3. Section 15.4 presents sustainability assessment case studies from two different supply chains, i.e., diaper and detergent. Finally, Section 15.5 gives some concluding remarks.
15.2 SUPPLY CHAIN OPERATION AND SUSTAINABILITY A typical supply chain consists of suppliers, manufacturer, distributors, and retailers/ customers (Figure 15.1). The manufacturer and distributor belong to a focal enterprise and have several departments performing various supply chain functions while interacting with one another. These departments are sales, operations, procurement, and storage. The sales department deals with customers and accepts their orders. In some cases, the sales department also makes demand forecast. This actual/forecast demand information is communicated to the operations department, who manages production. Based on this demand information and also inventory information
15.2 Supply Chain Operation and Sustainability
Resources from Environment Raw materials
Transport
Manufacturer
Distributor
Transport
Retailer/Customer
Employment
OperaƟons
Societal Benefits
Procurement
Transport
Electricity
Storage
Water emission
Social Impacts
Sales
Air emission
Water
Health and safety
Raw material supplier
Fuel
Packaging and solid waste
Burden to Environment
FIGURE 15.1 Typical supply chain with its environmental and social impacts.
from the storage department, the operations department decides how much product to make. The operations department and the storage department communicate with the procurement department for procurement of the required raw materials. The storage department manages both raw materials and product inventory. There may also be a logistics department that manages raw materials and product transportation. Each of these departments follows certain policies in performing its functions. The sales department may have a policy to decide which customer orders to accept, a policy to decide pricing, and a policy for demand forecasting. The operations department may follow a certain scheduling policy to decide production rates and schedule. The procurement department has a procurement policy, e.g., procurement at a regular interval or after reaching a certain inventory level. The storage department may have a storage policy to manage the different raw materials and product inventory. Thus, each of these departments is an independent and autonomous entity making its own decisions in the supply chain operation while interacting with one another. At the supply chain level, the manufacturer procures raw materials from different suppliers, manufactures the products, and sends them to the distributor. The distributor typically operates in a push-mode; it keeps a certain level of inventory to fulfill
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retailer/customer orders. Different procurement policies for replenishment of products can be adopted by the distributor. For example, under the fixed interval policy, procurement is done at regular intervals to bring its inventory back to a certain topup level. The distributor places orders to the manufacturer, which may operate in push-mode (products manufactured based on demand forecast) or pull-mode (products manufactured upon receiving the distributor’s orders). Policies and decisions made by each of the above entities will affect the sustainability performance of the overall supply chain. Sustainability of supply chain operation can be assessed through its economic, environmental, and social impacts, as shown in Figure 15.1. Economic sustainability is related to costs and profitability. The environment is impacted as supply chain operation consumes resources from the environment (raw materials, fuel, water, and electricity) and releases a burden to the environment (air emission, water emission, packaging, and solid waste). Social impacts include employees’ health and safety, employment, and other societal benefits. These impacts can be modeled and measured through various indicators. Modeling of the supply chain operation for sustainability assessment is described in the next section.
15.3 DYNAMIC SIMULATION MODEL OF SUPPLY CHAIN OPERATION A supply chain consists of networks of entities at different levels and can be seen as a sociotechnical system in which the physical and the social networks of actors involved in its operation collectively form an interconnected complex system. The actors determine the operation of the physical network, and the physical network affects the behavior of the actors. Decision-making in the supply chain is distributed across the various actors, each with its own goals and tasks. For example, the operations department makes products and the storage department manages inventory. These actors communicate with one another to get the information necessary to perform their own tasks or to call for certain actions. Agent-based modeling is a suitable approach to model complex systems comprising of interacting autonomous agents (Macal and North, 2005). In this approach, a system is described by defining the actors (agents) and the interactions between them. The system behavior then emerges from the behavior of the model components and their interactions. Instead of taking a top-down view in modeling, the model is hence constructed from a bottom-up perspective. Because of this, agentbased modeling is considered a natural approach for systems involving distributed and decentralized decision-making. Each actor in the supply chain operation is modeled as an agent with its own activities and interactions with other agents, as summarized in Table 15.1. Policies are implemented to guide the agents’ decision-making in performing their activities. In this work, we do not explicitly model the logistics department and transport; material transport is implemented through time delay between dispatch and arrival at destination.
15.3 Dynamic Simulation Model of Supply Chain Operation
Table 15.1 Agent Activities and Interactions in Supply Chain Operation Agent
Activity
Interaction with
Retailer/Customer
Place Order
Distributor (Sales)
Distributor
Receive order Accept order Assign order Process order
Retailer/Customer Retailer/Customer Distributor (operations) Distributor (sales), distributor (storage) Distributor (operations)
Sales
Operations Storage
Procurement
Manufacturer
Sales
Operations
Storage
Procurement
Raw material supplier
Release product for delivery Manage product inventory Buy product
Receive order Accept order Assign order Request raw materials Make product Store products Release raw materials for production Manage raw material and product inventory Buy raw materials
Supply raw materials
Distributor (procurement) Distributor (operations), distributor (storage), manufacturer (sales) Distributor (procurement) Distributor (procurement) Manufacturer (operations) Manufacturer (storage) Manufacturer (sales) Manufacturer (storage) Manufacturer (operations) Manufacturer (procurement) Manufacturer (operations), manufacturer (storage), raw material supplier Manufacturer (procurement)
The agent-based model can be implemented in an agent-based modeling platform such as the Repast simulation toolkit in Java (North et al., 2006), or a mathematical modeling platform such as MATLAB/Simulink (MathWorks, 2010). van Dam et al. (2009) compared the two approaches and identified their strengths in the context of refinery supply chain modeling. Physical elements of the refinery are modeled explicitly in the mathematical platform but not in the agent-based platform. On the other hand, the agent-based platform explicitly has a flexible structure, allowing new agents and connections between agents to be added in extensions to the model. Also, in the agent-based platform it is possible to directly reuse the same ontology and source code for modeling a different supply chain. In general, the efforts required to make changes in the model for various scenarios, ranging from operational to tactical and strategic levels, are similar in the two platforms. In terms of explaining the model and the model results, the agent-based platform
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offers a natural representation of the decision-making processes and interactions between the entities while the mathematical platform has an edge for explaining the technical process. The supply chain model has been implemented in both the agent-based platform using Repast (Behdani et al., 2010) and the mathematical platform using MATLAB/ Simulink (Adhitya and Srinivasan, 2010). In this work, we use the latter and integrate indicators from LCA into the model to enable sustainability assessment. The model uses a discrete-time representation; one day is divided into a predefined number of time ticks t. Raw material arrival at the manufacturer is modeled as: RAr ðtÞ ¼ RSr ðt LTr Þ
(15.1)
where RAr(t) is the amount of raw material r arriving at the manufacturer at time t, RSr(t) is the amount of raw material r ordered from the supplier, and LTr is the lead time between order and arrival of the raw material r. Processing in the manufacturer is modeled through the recipe Rcpr, which specifies the amount of raw material r required to make one unit of product. RUr ðtÞ ¼ Rcpr CJ amt
(15.2)
where RUr(t) is the amount of raw material r used for production at time t and CJamt is the amount of product to be made in job CJ. The material balance on raw material inventory at the manufacturer is given by: IRr ðt þ 1Þ ¼ IRr ðtÞ þ RAr ðtÞ RUr ðtÞ
(15.3)
where IRr(t) is the inventory of raw material r at time t. All activities and policies of the agents as listed in Table 15.1 have been modeled. For the complete model equations, the reader is referred to Adhitya et al. (2011). Economic performance can be measured through indicators such as manufacturer profit and distributor profit. Environmental and social performance can be measured through indicators from LCA and social/societal LCA, respectively. LCA is a widely used technique for measuring the environmental (or social for social/societal LCA) consequences assignable to a product or service. Starting from raw material extraction to production process, transportation, point of use, and final disposal, LCA charts the course of all of the consumption of natural resources and release of pollutants into air, water, and soil. However, LCA impacts are derived from a product-centric perspective without considering the effects of supply chain dynamics such as different logistics options, inventories, distribution network, and ordering policy. For example, LCA calculations are typically based on a static, average truckload level and number of trips, whereas in reality these two variables of supply chain operation are dynamic. In this work, we take a supply chain-centric perspective by considering the various supply chain decisions through dynamic simulation. By integrating LCA indicators into the simulation, we can perform sustainability assessment of the supply chain operation, as illustrated through case studies in the next section.
15.4 Case Studies
15.4 CASE STUDIES We present two different case studiesddiaper supply chain (Adhitya et al., 2011) and detergent supply chain (Wang et al., 2011). In the former, only economic and environmental aspects are considered while in the latter social impacts are also included.
15.4.1 DIAPER SUPPLY CHAIN CASE STUDY Modern disposable diapers come in a variety of styles, but their basic raw materials are essentially the same: fluff pulp, superabsorbent polymer (SAP), plastic components such as low density polyethylene and polypropylene (PP), adhesives, and elastics (EDANA, 2005). In this case study, the environmental indicator values are derived from the LCA study of disposable diapers done by the UK Environmental Agency (2005). Eleven environmental indicators are used, namely abiotic resource depletion (ARD), global warming potential (GWP), ozone layer depletion, photochemical oxidation, acidification, eutrophication (EUT), human toxicity (HT), fresh water aquatic ecotoxicity (FWAE), terrestrial ecotoxicity (TE), water usage (WU), and energy consumption (EC). These 11 indicators are evaluated at each stage of the supply chain, namely raw materials, raw material transportation, manufacturing, packaging, product transportation from manufacturer to distributor, distributor operation, and product transportation from distributor to customer. Two economic indicators are used, namely manufacturer’s profit and distributor’s profit. Table 15.2 summarizes the economic and environmental indicators used in the diaper supply chain simulation model. Table 15.2 Indicators Used in the Diaper Case Study Aspect Economic Environmental
Indicators PPRO DPRO ARD GWP ODP PO ACD EUT HT FWAE TE WU EC
Manufacturer’s profit ($) Distributor’s profit ($) Abiotic resource depletion (g Sb eq) Global warming potential (g CO2 eq) Ozone layer depletion (g CFC-11 eq) Photochemical oxidation (g C2H2) Acidification (g SO2 eq) Eutrophication (g PO4 eq) Human toxicity (g 1,4-DB eq) Fresh water aquatic ecotoxicity (g 1,4-DB eq) Terrestrial ecotoxicity (g 1,4-DB eq) Water use (kg H2O) Energy consumption (MJ)
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Two scenarios are considered here. The first involves changes in the product composition; the second looks at the ordering policy in the supply chain. The aim is to assess the sustainability impacts (economic and environmental) of different decisions along the supply chain. The simulation horizon is 60 days. The cumulative profits and environmental impacts over the simulation horizon are compared in the different scenarios
15.4.1.1 Scenario 1: changing the diaper composition The first scenario analyzes the impact of changes to the diaper composition. The mass composition of a typical diaper from years 1987, 1995, and 2005 are shown in Table 15.3. Supply chain operation with the same demand is simulated for the three different diaper compositions and the results are shown in Figure 15.2. There is a significant reduction in WU for the 2005 case compared to the 1995 and 1987 cases. This is due to the reduction in the use of fluff pulp from 1987 to 2005 (14.57 g vs 32.83 and 54.94 g). However, the figure highlights an increase in the GWP, ARD, and TE for the 2005 case from the 1995 and 1987 cases. This is caused mainly by the increase in the amount of SAP in the 2005 case (13.65 g vs 4.46 and 0.74 g), and to a lesser extent the increase in the amount of PP (7.22 g vs 3.97 and 4.36 g). The new composition also leads to an increase in the manufacturer’s profit since it requires less raw materials (41.95 g vs 49 and 67 g). Although the older compositions use less SAP, they use much more fluff pulp. The results also reveal that the environmental impacts from raw material transportation are smaller in the 2005 case than the 1995 and 1987 cases, because less raw materials are needed to be transported, resulting in fewer trips.
15.4.1.2 Scenario 2: order batching The second scenario studies the effect of different ordering policies by the distributor. More frequent ordering in smaller batches means more transportation trips and consequently higher transportation impact and cost. Different order batching by the distributor is simulated through three different distributor procurement intervals: 2, 5, and 10 days. The results are shown in Figure 15.3. Table 15.3 Diaper Compositions Component
1987 (g)
1995 (g)
2005 (g)
Fluff pulp Superabsorber (SAP) Polypropylene (PP) Polyethylene (PE) Adhesive Elastic Others Total
54.94 0.74 4.36 4.29 1.34 0.20 1.14 67.01
32.83 4.46 3.97 3.33 1.42 0.20 2.79 49.00
14.57 13.65 7.22 2.69 1.76 0.21 1.85 41.95
Adapted from EDANA (2005)
15.4 Case Studies
ACD*7E+05 3E+04* DPRO
2
1.8
ARD *8E+05
1.6 1.4 1.2
5.5E+04* PPRO
EC*2E+06
1 0.8 0.6 0.4
6E+06* WU
EUT *5E+04
0.2 0
3E+05* TE
FWAE *5E+05
2E+04* PO
GWP *8E+07 2005 5E+05* ODP
HT *7E+06
1995 1987
FIGURE 15.2 Economic and environmental indicators for different diaper compositions in the diaper case study (Scenario 1). ACD, acidification; ARD, abiotic resource depletion; EC, energy consumption; EUT, eutrophication; FWAE, fresh water aquatic ecotoxicity; GWP, global warming potential; HT, human toxicity; ODP, ozone layer depletion; PO, photochemical oxidation; TE, terrestrial ecotoxicity; WU, water usage; PPRO, manufacturer’s profit; DPRO, distributor’s profit.
In general, less frequent ordering (i.e., higher procurement interval) results in lower environmental impacts due to reduced number of transportation trips. The larger batching of order results in longer lead time and the average inventory kept by the distributor is lower (see Figure 15.4). Consequently, the distributor’s profit is increased due to lower inventory cost. On the other hand, the average raw material inventory kept by the manufacturer is increased when the distributor order is less frequent, resulting in higher inventory cost and lower manufacturer’s profit. Less frequent ordering means reduced inventory and this might have an impact on order fulfillment. This, along with the trade-off between the distributor’s and manufacturer’s profits, could be studied further to find the optimal supply chain policies for both distributor and manufacturer.
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4.39E+04*DPRO
8.02E+05*ACD 1
ARD *9.72E+05
0.9 4.35E+04* PPRO
0.8
EC *2.40E+06
0.7 6.73E+06* WU
0.6
EUT *5.81E+04
0.5
3.64E+05* TE
FWAE *4.56E+05
2.29E+04* PO
GWP *9.32E+07 Interval Days = 2
4.29E+05* ODP
HT *7.77E+06
Interval Days = 5 Interval Days = 10
FIGURE 15.3 Economic and environmental indicators for different distributor procurement intervals in the diaper case study (Scenario 2). ACD, acidification; ARD, abiotic resource depletion; EC, energy consumption; EUT, eutrophication; FWAE, fresh water aquatic ecotoxicity; GWP, global warming potential; HT, human toxicity; ODP, ozone layer depletion; PO, photochemical oxidation; TE, terrestrial ecotoxicity; WU, water usage; PPRO, manufacturer’s profit; DPRO, distributor’s profit.
15.4.2 DETERGENT SUPPLY CHAIN CASE STUDY In the detergent supply chain case study, we include social indicators in addition to the economic and environmental indicators. Extension of LCA to include the social aspects of products along their life cycle is termed as societal LCA (SLCA). The social implications of a product can be expressed through qualitative, semiquantitative, or quantitative indicators (UNEP, 2009). Qualitative indicators provide information on a particular issue, for example, measures taken by an enterprise to manage employee stress. Semiquantitative indicators categorize qualitative indicators into a yes/no form or a scale-based scoring system, for example, presence of a stress management program (yes/no) or the rating of such stress management program (bad ¼ 0, mediocre ¼ 1, good ¼ 2). Quantitative indicators can be used if the issues at stake are quantifiable.
15.4 Case Studies
100
Interval = 10
Interval = 5
Interval = 2
90
Inventory (sku)
80 70 60 50 40 30 20 10 0 0
10
20
30
40
50
60
Day
FIGURE 15.4 Distributor inventory for different distributor procurement intervals in the diaper case study (Scenario 2).
Hunkeler (2006) proposed a quantitative SLCA indicator that is based on the number of worker hours to fulfill basic societal needs. The inventories of different unit processes in the product life cycle are associated with the workers’ employment incomes and then correlated with the various life necessities they can provide. This is done by dividing the total employment hours by characterization factors that specify the number of labor hours needed to fulfill each unit of societal needs. For example, Table 15.4 shows the societal characterization factors for two countries (France and Germany) and four societal needs (housing, health care, education, and other basic needs). An average worker needs to work for 20,000 h to purchase housing in Germany. Using the life cycle inventory data, we can calculate the total employment hours associated with the supply chain operation. If the total number of hours required for making a detergent ingredient X in Germany is 7 h, then based on Table 15.4, we can infer that this contributes to 0.00035 housing units, 0.0117 health care units, 0.0233 education units, and 0.14 units of other basic needs. Table 15.4 Wages and Societal Characterization Factors Societal Needs (Work hour/unit) Country
Housing
Health Care
Education
Other Basic Needs
France Germany
25,000 20,000
400 600
375 300
67.5 50
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In this case study, the detergent manufacturer is located in France and the raw materials come from suppliers in seven countries, namely Germany, Russia, France, Canada, South Africa, The Netherlands, and Morocco, by trucks and cargo ships. The societal characterization factors for these countries are taken from Hunkeler (2006). The product is a granular detergent based on the formulation described in Saouter and van Hoof (2002) and sold to local distributors in France. The environmental indicator values are derived from Dewaele et al. (2006). The simulation horizon is 180 days. Two scenarios are presented in this case study: changing detergent formulation and supplier selection policy.
15.4.2.1 Scenario 1: changing the detergent formulation The first scenario compares the sustainability of traditional detergent production containing a phosphate ingredient (sodium tripolyphosphate or STPP) and modern detergent without STPP. Until the 1980s, STPP was a commonly used ingredient in laundry detergent formulation. Since then, due to environmental concerns, phosphates were banned in Europe and many US states and STPP was phased out and replaced with zeolites, citrates, and other builders. These ingredients are more environmentally friendly but also more costly. Supply chain operation with the same demand is simulated for the two different detergent formulations and the results are shown in Figures 15.5 and 15.6. Figure 15.5 shows that the phosphate-free detergent has a lower profit because the replacement ingredients for STPP are more costly. It also has a lower social performance than the STPP detergent because fewer employment hours are required for the phosphate-free detergent production. Figure 15.6 shows that the phosphate-free detergent has better environmental performance in all impact categories with significant reductions in EUT, HT, FWAE, solid waste, and GWP.
Profit 10 9 8 7 6 5 4 3 2 1 0 Phosphate-free STPP
Social Indicators 14 × 106 12 10
Housing Healthcare Education Others
8 6 4 2 0 Phosphate-free
STPP
FIGURE 15.5 Profit and social indicators for two detergent formulations (Scenario 1). STPP, sodium tripolyphosphate.
15.4 Case Studies
Photochemical Oxidation Acidification
Ozone Layer Depletion Global Warming Potential
STPP Energy Consumption
Eutrophication
Human Toxicity
Water Use
Fresh Water Aquatic Ecotoxicity
Solid Waste Phosphate-free
FIGURE 15.6 Environmental indicators for two detergent formulations (Scenario 1). STPP, sodium tripolyphosphate.
15.4.2.2 Scenario 2: supplier selection policy The second scenario compares two supplier selection policies: favor nearest suppliers versus no preference with regards to supplier distance. Supply chain operation is simulated for the two different policies and the results are shown in Figures 15.7 and 15.8. Figure 15.7 shows that nearer suppliers favored results in a significantly higher profit because timely arrival of raw materials allows more orders to be fulfilled. More orders completed also means more employment and therefore higher social performance. However, as shown in Figure 15.8, environmental performance of the nearer suppliers favored policy is worse. This can be attributed to the same reason; more orders completed means more raw materials used, more manufacturing, packaging, and transport impacts.
Profit 10 9 8 7 6 5 4 3 2 1 0 No Preference Nearer Suppliers
Social Indicators 14 × 106 12 10
Housing Healthcare Education Others
8 6 4 2 0 No Preference
Nearer Suppliers
FIGURE 15.7 Profit and social indicators for two supplier selection policies in the detergent supply chain (Scenario 2).
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Photochemical Oxidation Acidification
Ozone Layer Depletion Global Warming Potential
Nearer Suppliers Eutrophication
Human Toxicity Fresh Water Aquatic Ecotoxicity
Energy Consumption Water Use Solid Waste No Preference
FIGURE 15.8 Environmental indicators for two supplier selection policies in the detergent supply chain (Scenario 2).
15.5 CONCLUDING REMARKS This paper presents a dynamic simulation-based framework for assessment of supply chain sustainability. Supply chain operation involves numerous interacting entities and thus complex dynamics, which will affect the supply chain sustainability performance. LCA-based assessments are static and do not consider the dynamics in supply chain operation. Thus, there is a need for dynamic simulation integrated with LCA indicators to capture the impacts of these dynamics on the overall sustainability performance. Case studies involving changes in product composition, ordering policy, and supplier selection policy demonstrate how the proposed framework can be used for sustainability assessment while considering the dynamics involved in supply chain operation.
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