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Measuring Environmental Performance across a Green Supply Chain: A Managerial Overview of Environmental Indicators Purba H Rao

Executive Summary

KEY WORDS Integrated Green Supply Chain Environmantal Sustainability Reverse Logistics Energy Efficiency Resource Conservation National Capital Region The Philippines

The concept of green supply chains is now accepted in many corporate organizations in Asia. Since the early nineties, when industry became aware of the increasing relevance of sustainable development, many business enterprises in Asia have adopted environmental initiatives as an integral part of their business practices. In time these organizations came to realize that the environmental initiatives needed to encompass not only the organization’s own business practices but also the entire stretch of operations across the supply chain. In other words, they felt the need to include the employees, suppliers, customers, waste handlers, and other business partners in the greening process (Bacallan, 2000). Thus, an integrated supply chain approach was called for. Such an approach should be able to identify the environmental aspects at every stage, assess the environmental impacts associated with these aspects, prioritize them, and design action plans to mitigate their adverse effects on the environment if any. An integrated green supply chain approach would take into consideration the inbound logistics phase of the supply chain, the production or internal supply chain, the outbound logistics phase, and the reverse logistics phase (Rao & Holt, 2005; Sarkis, 1999; Seuring & Muller, 2007). For many organizations in Asia, the green supply chain approach has also emerged as a way to demonstrate their commitment to sustainability (Seuring et al., 2008). However, there is a continuous need to measure and monitor the extent to which environmental performance is actually achieved. To assess this performance, a system of indicators across the supply chain is proposed, which is computationally easy to implement at the industry level. To demonstrate that the system of environmental indicators does measure performance, an empirical approach is adopted to test whether the system correlates with the four constructs of environmental sustainability: resource conservation, energy efficiency, reduction of hazardous waste, and reduction of greenhouse gas emissions (Vachon & Mao, 2008). In order to check these linkages of environmental indicators to the constituents of environmental performance, four multiple regression models were run. In the first model, the dependent variable was resource conservation, the independent predictor variables being the 20 environmental indicators grouped under the four constructs: Inbound logistics, production or internal logistics, outbound logistics, and reverse logistics. In the second, third, and fourth models, the dependent variables were energy efficiency, reduction of hazardous waste, and minimization of emission of greenhouse gases, respectively. Upon running the regression models, the models for resource conservation, reduction of hazardous waste, and reduction of emission of greenhouse gases were found to be significant at 5 percent significance level while the model for energy efficiency was significant at 10 percent level.

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E

ver since the early nineties when industry became aware of the increasing relevance of sustainable development, many business enterprises in Asia have adopted environmental initiatives as an integral part of their business practices. In time, these organizations came to realize that the environmental initiatives needed to encompass not only the organization’s own business practices but also the entire stretch of operations across the supply chain – including employees, suppliers, customers, waste handlers, and other business partners in the greening process. (Bacallan, 2000). This called for an integrated supply chain approach that would identify the environmental aspects at every stage, assess the environmental impacts associated with these aspects, prioritize them, and design action plans to mitigate their adverse effects on the environment, if any. An integrated green supply chain approach would take into consideration inbound logistics production or internal supply chain, outbound logistics, and the reverse logistics phases

of the supply chain (Rao & Holt, 2005; Sarkis, 1999; Seuring & Muller, 2008) (Figure 1). For many organizations in Asia, the green supply chain approach has also emerged as a way to demonstrate their commitment to sustainability (Seuring et al., 2008).

GREEN SUPPLY CHAIN The different phases of the green supply chain encompass the following greening initiatives (Sarkis, 2005; Rao, 2008a).

Greening the Inbound logistics Inbound logistics is the first phase in the supply chain. Greening this phase encompasses green sourcing of materials, green purchasing process, and greening suppliers. Green sourcing is one of the most important environmental strategies for reducing pollution and waste at the source; it entails taking action right at the start of the sup-

Figure 1: An Integrated Green Supply Chain INBOUND LOGISTICS

PRODUCTION

OUTBOUND LOGISTICS

Raw and virgin materials

Vendor Supplier

New components and parts

Storage

Fabrication Assembly Processing

Recycled, reused materials and parts

REVERSE LOGISTICS Reusable, recyclable materials and components

Output Distribution Logistics Warehousing Packaging Transportation Waste Disposal Treatment IWEP*

Use

Disposal

* IWEP = Industrial Waste Exchange Programme (Rao, 2008)

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MEASURING ENVIRONMENTAL PERFORMANCE ACROSS A GREEN SUPPLY CHAIN...

ply chain to reduce the hazardous as well as non-hazardous waste generated at the later phases of the supply chain (Rao & Kondo, 2010; Rao, 2008a). Greening the inbound logistics endeavours to accomplish the prevention of pollution at the source by: • Trying to reduce the purchase of hazardous materials, virgin materials • Purchasing more amounts of recycled, recyclable, reused or reusable materials • Trying to reduce the purchased volume of such items which are difficult to dispose off • Encouraging suppliers to supply only environmentfriendly materials • Minimizing packaging unless it is unavoidable • Requiring suppliers to use more of bio-degradable and returnable packaging • Encouraging suppliers to incorporate environmental initiatives into their processes • Holding environmental awareness seminars for suppliers • Imparting environmental know-how and helping the suppliers establish green production in their operations. (Rao, 2008; Sarkis, 2005; Tsoulfas & Pappis, 2008).

Greening the Production Phase This phase, in a manufacturing set up, constitutes fabrication and assembly with closed loop manufacturing, demanufacturing, remanufacturing, recycling, source reduction of waste, pollution and air emissions, and using environment-friendly technology, achieved with a shift to cleaner production, customer focus, worker involvement, and supplier integration (Min & Galle, 1997; Rao, 2004). Greening production involves many innovative initiatives (Florida, 1996; Lewis, 2000; Sanchez & Perez, 2001; Rothenberg, Frits, & Maxwell, 2001). Some of these can be: • Improvement of processes to reduce generation of hazardous waste • Improvement of processes to comply with emission standards • Improvement of processes to comply with effluent standards • Use of clean/renewable energy in the production process • Taking environmental design into considerations • Improvement of processes to reduce solid and liquid VIKALPA • VOLUME 39 • NO 1 • JANUARY - MARCH 2014

waste as much as possible • Improvement of processes to reduce water use, material use • Improvement of processes to reduce air emissions • Use of cleaner technology process • Recycling of materials internal to the company A point to be noted here is that the greening of production applies very effectively in non-manufacturing and service set up too. There the production phase would refer to production of service, as in the hotel industry.

Greening the Outbound Phase While the inbound logistics phase requires an interface between the organization and suppliers/vendors and other business partners, the outbound logistics phase involves waste disposal management and all processes required to deliver the product or service to the customer in its final form. In this phase, an organization has to consider the marketing and selling aspects, transportation and delivery logistics, packaging concerns, and waste disposal possibilities (Guide & Wassenhove, 2001). In order to green this stage of the supply chain, the organization has to deal with green marketing and environment-friendly packaging, transportation, and waste management. Again, for each of these stages, the organization has to interact with service providers and business partners like the delivery people and distributors, suppliers who must supply environment-friendly packaging, transportation personnel who use environmentfriendly transport and clean fuel, and finally, the waste suppliers and contractors who should meet the high standards of environmental management (Rao & Holt, 2005; Rao, 2008a). In this phase, the company tries to mitigate the pollution that has been created upstream in the supply chain. At this point, the solutions that are of an end-of-the-pipe nature are considered as opposed to pollution prevention at the source. This phase attracts a lot of public attention and thus needs to be made as environment-friendly as possible (Seuring et al., 2008).

Reverse Logistics In this phase, the company deals with the collection and reprocessing of used products, return of materials, components and parts, and bringing them back to the supply chain.

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Of all the aspects of the green supply chain, the reverse logistics phase is the least implemented in Asia. However, some applications of this concept are leading the way. Amway, Thailand, for instance, delivers its personal and home care products in plastic bottles to customers who are given discounts for their purchases when they return the empty containers to the company salesmen, who pick them up at regular intervals. These empties are then re-manufactured into reusable plastic bags (Rao, 2008a; Fleischmann, 2001; Helms & Hervani, 2006). As another example of implementation of reverse logistics, one may consider how Philippine Recyclers, Inc. (PRI), remanufactures used lead batteries. Lead-acid batteries installed in motor vehicles contain sulfuric acid and lead, both of which are highly toxic. Lead compounds, when swallowed or inhaled, may cause poisoning, leading to sickness or even death (Rao, 2008). These batteries, used in vehicles, run out every three to four years and have to be replaced regularly. Disposing of them properly is extremely important because if left out in the open, the sulfuric acid in the batteries tends to spill, causing soil infertility or contamination of ground water. Further, the lead that could dry up into dust can be carried by the wind to combine with the air we breathe. To avoid these health and environmental problems, PRI recycles these batteries, recovering the lead and sulphuric acid, refining the lead and cleaning the acid, for use in the production of new batteries (Rao, 2008a). Since a company and its business partners are generally considered as one single system that delivers the product or service, any shortcomings regarding sustainability initiatives on the part of the business partners would eventually be regarded as the company’s failure to ensure sustainability. This makes it all the more important for state-of-the-art companies to integrate sustainability initiatives, such as those involving the prevention of pollution, in a streamlined manner along the entire supply chain. The increasing relevance of greening the supply chain in industry has sparked interest in this topic among academic researchers too (Seuring et al., 2008; Sigala, 2008; Sarkis, 1999; Srivastava, 2007). Initially, most of the research in this area focused on definitions, necessity, and importance of greening the supply chain, the specifications of its driving forces at various stages, and the challenges and obstacles faced during the process (Lamming

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& Hampson, 1996; Min & Galle, 1997; Sarkis, 1999). Subsequent research elaborated the concepts of green purchasing (Hamner, 2006; Min & Galle, 1997; Theyel, 2006), greening suppliers (Bala et al. 2008; Rao & Holt 2005), environmental manufacturing and operations (Florida, 1996; Florida & Davidson, 2001; Geffen & Rothenberg, 2000; Klassen & Whybark,1999; Koplin, Seuring, & Mesterharm, 2007; Preuss, 2006; Sarkis, 2001), environmental management systems (Von Ahsen, 2006), product life cycle analysis (Kaiser, Eagan, & Shaner, 2001), and product recovery and reverse logistics (Guide & Wassenhove, 2001b; Helms & Hervani, 2006). The literature now encompasses integrated environmental sustainability issues as well as social- and community-oriented ones (Seuring et. al. 2008; Kovacs, 2008; Seuring & Muller, 2008). Recent studies have examined the different phases of the supply chain, and have discussed innovative environmental initiatives which can be integrated into it, such as metrics and environmental indicators to measure and monitor environmental operations (Klassen & McLaughin, 1996; Sanches & Perez, 2001), closed loop supply chains, remanufacturing aspects, and also reverse supply chains (Guide & Wassenhove 2001b; Hines & John, 2001). In addition to studying the relevance of supply chains for individual business organizations, researchers have also focused on supply chain issues for SMEs (small and medium enterprises), public and non-government organizations, as well as educational institutions (Walker & Preuss, 2008; Bala, Munoz, Rieradevall, & Ysern, 2008). Vachon and Mao (2008) examined the synergies between supply chain issues and sustainable development at the country level. They measured supply chain strength based on the number of suppliers and customers in a particular country and related this factor to sustainable development in terms of environmental performance, economic performance, and social sustainability. Ciliberti, Pontrandolfo, and Scozzi (2008) analysed the sustainability practices adopted by SMEs, and the challenges that they faced in making their suppliers environmentally and socially responsible. Cote, et al (2008) also dealt with similar issues. Tsoulfas and Pappis (2008) aimed to develop a system of indicators to measure environmental performance for specific phases of the supply chain and to come up with different approaches and methodologies to evaluate and operationalize the use of


metrics into the supply chain. While Tsoulfas and Pappis (2008) focused on production and internal supply chains, Lai, et al (2008) studied packaging and transportation decisions. A wide range of theories and insights have been developed to integrate sustainability aspects into integrated supply chains. However, a performance assessment system to help the implementation and evaluation aspects of a green supply chain or a performance monitoring system which would encompass the entire supply chain has not been developed yet. The objective of this paper is to propose and validate a metric or an indicator system to measure environmental performance across the supply chain, which would capture environmental performance into an array of simple, easy-to-compute indicators. This would help organizations — both large and small — to implement, measure, and monitor the performance of the green supply chain, encompassing their entire supply chain operations. The indicator system should be robust enough to cover the entire network of the supply chain, involving not just a single company but also the supplier companies, distributors, waste handlers, customers, and other business partners, which constitute the different stages of the supply chain. Environmental indicators across the supply chain endeavour to consolidate extensive environmental data pertaining to operations of a company in terms of their environmental aspects and impacts. They also help in developing a comparatively small number of pointers from various environmental initiatives, which can then serve as benchmarking and monitoring tools. If they are computed and compared periodically, they help to detect if environmental performance is not as expected and can thereby serve as an early warning system. These indicators, when compared across different companies in the same industry, can find organizational inefficiencies that can be addressed subsequently.

LITERATURE REVIEW The importance of using indicators to measure organizational performance has long been recognized in the field of operations management. Melnyk, Stewart, and Swink (2004) emphasize that a performance measurement system or “metrics” (what we refer to as ‘environmental indicators’) provides the essential links between strategy, VIKALPA • VOLUME 39 • NO 1 • JANUARY - MARCH 2014

execution, and desired performance within an organization (Klassen & McLaughin, 1996; Tanzil, Gang, & Beloff, 2003). Though the development of metrics has proven to be a challenge, companies still have a great opportunity to use metrics to measure what they are doing, and to identify gaps between actual performance and the industry standards. In addition to measuring performance, environmental indicators could also help organizations to benchmark their operations to the industry standards and to the standards of a state-of-the-art company within the same industry vertical. Further, these indicators could be used as a system of communication tools for corporate environmental reporting to local regulatory bodies, or as part of the Global Reporting Initiative (GRI) used by many organizations worldwide.1 Some indicator systems examine the relationship between managerial practices such as Quality Improvement approaches and Business Performance (Evans, 2004). Other systems bring out a strategic fit between manufacturing decisions and other strategies (Tanzil, Gang, & Beloff, 2003), while certain other indicator systems explore different approaches to ensure a strategic fit among the operations strategy, the financial strategy, and the competitive business environment (Wheelwright, 1984).

ENVIRONMENTAL INDICATORS AND ENVIRONMENTAL PERFORMANCE In modern industry operations, environmental performance has been a key area of concern for managers due to various reasons such as regulatory compliance, customer satisfaction, cost effectiveness, corporate image, community pressure, and competitive advantage (Theyel, 2001). According to Klassen et al. (1996), environmental performance specifically measures how firms are able to reduce their environmental impact, by benchmarking them to industry standards. In their study, Klassen et al. (1996) investigated the relationship between constructs such as the environmental performance of a firm and its financial performance, and concluded that a significant relationship existed between the two constructs. They proposed that environmental performance, when given proper media coverage, often enhanced a firm’s valuation, thereby improving its financial performance.

1

See http://www.mallenbaker.net/csr/CSRfiles/GRI.html for details.

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In order to explore the linkage between the environmental initiatives of an organization and its actual environmental performance, it would be pertinent to measure the initiatives and the performance using indicators or any other kind of metrics, which effectively captures all the initiatives incorporated in the company’s operations. Walton, Handfield, and Melnyk (1998) employed a case study approach to explore the role of supply chain management, especially the role of purchasing, in environmental management. They took into consideration the responses of the managers from five furniture-manufacturing companies in order to evaluate the role of suppliers in the major environmental practices of their respective firms. The environmental practices of the companies were categorized into five supply chain practices: sourcing materials for Design for Environment (DFE), product design process, supplier process improvement, supplier evaluation, and inbound logistics process. To evaluate the impact of the supply chain on environmental performance, the authors set up a “meta-matrix” which summarized each of the practices, and evaluated the environmental scores for each company.2 Several studies have also focused on how an organization can consolidate large amounts of environmental information, and use them as indicators/metrics to respond to an environmental crisis (Scarborough & Moody, 1995; Turner & Stephenson, 1994). An area that requires further research is the issue of how companies can manage and process vast amounts of environmental information in order to ascertain the exact types of information that are perceived to be important by different managers in the supply chain network (Henriques & Sadirsky, 1999). Developing a corporate environmental metric would serve several purposes in the environmental activities of companies. In the first place, such a metric could be used for internal purposes in order to (1) benchmark the company’s environmental activities against a prescribed standard and execute a gap analysis between where the company is currently placed, and where it should be; (2) continuously monitor and control the items included in the metric to track their performance and check for deviations; and (3) use the feedback derived from the metric to carry out brainstorming exercises with the employees to 2

The “meta-matrix“ used by Walton et al. (1998) corresponds conceptually to the metric for environmental performance that is proposed in this paper.

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find out corrective measures and/or areas of improvement. In many of the research approaches, environmental indicators form a core component of sustainable development. Several studies have focused on identifying environmental indicators or sustainability metrics that will measure environmental performance over time, and will determine if the desired rate of progress is being accomplished. (Tanzil & Beloff, 2003; Marshall & Brown, 2003). Scharz, Beloff, and Beaver (2002) developed a metrics that integrated environmental and economic performance in the production process. This management strategy incorporated eco-efficiency, whereby controlling the same metrics that led to environmental performance also resulted in more efficient production processes, and better quality of goods and services. The indicators of sustainability in this system—called Bridges Sustainability Metrics—had five basic components, namely, material intensity, energy intensity, water consumption, toxic emissions, and pollutant emissions (Guide to Corporate Environmental Indicators, 1997). Tanzil and Beloff (2003) considered other possibilities, and documented actual applications in the development of Sustainability Metrics. They essentially followed the same 5-component structure of the metrics that was proposed by Scharz et al. (2002). Another framework for metrics that is used for corporate environmental indicators was developed by the federal ministry in Bonn. This framework, which complements the ISO 14001 as well as the EMAS standards, was employed in a study, where the metrics were implemented for SMEs in the Philippines (Rao, et al., 2006). Though neither the ISO nor the EMAS requires the development of indicators, this framework supports and develops its own standards for environmental indicators, which would then lead to the environmental performance evaluation system as followed under ISO 14031. According to this system, the environmental indicators can be divided into three main groups, depending on whether they address the company’s environmental impact (environmental performance indicators), the management’s environmental activities (environmental management indicators), or the external conditions of the environment (environmental condition indicators) (Guide to Corporate Environmental Indicators, 1997).


Relevance of Corporate Environmental Indicators

Supply Chain Perspective

The business case for corporate environmental indicators has been presented widely to assist managerial decision-making (Bacallan, 1998; Rao et al., 2006). Various authors including Melyk et al. (2004) discuss how environmental indicators or metrics could help in improving business operations, and in identifying market opportunities and areas for potential cost reduction. Their system of indicators are designed to detect unexpected upward or downward variances in water, energy, and raw materials consumption, and at the same time provide data for environmental reporting both inside and outside the organization. This would then facilitate external and internal communications as required by the ISO14001 system, as well as correlate significantly with business performance.

The proposed environmental indicators do not focus on the perspective of a single company but takes into consideration the entire supply chain: suppliers, distributors, waste handlers, customers, and other business partners impacting the indicators, in addition to the main company. The impact of including different business partners in the supply chain and their inter-relations can be observed by studying how they affect the indicators. For instance, the levels of inbound indicators would be significantly affected by supplier companies that supplied recycled materials, and reusable materials, as opposed to hazardous materials. Similarly, the levels of outbound indicators would be impacted if the waste-handling companies treated the waste water that is generated, the distributor companies used cleaner energy in transportation, the waste contractors picked up hazardous waste, and so on. The indicator EI1 measures the proportion of recycled material (provided by the supplier companies) to the total input. The indicator EI20 measures the percentage of output recovered from the customers/customer companies, which would be significantly impacted by the cooperation of the customers in allowing the output to be recovered from them after use (Rao, 2008).

Since environmental indicators proposed in research do not consider integrated supply chain for an organization, and there is no comprehensive framework yet for environmental indicators across the entire supply chain, it is the objective of this paper to develop such indicators across the supply chain, and also to investigate if the set of indicators does lead to actual environmental performance, measured in terms of conservation of resources, energy efficiency, reduction of hazardous waste generation, and reduction of greenhouse gases (Asian Development Bank Publication, 2007).

DEVELOPING ENVIRONMENTAL INDICATORS Methodology This paper proposes a system of environmental indicators, encompassing the entire supply chain with the objective of greening it, which would be easy to compute by large as well as small companies. The proposed system would be validated empirically based on company data. Following the frameworks used by Sarkis (1999) and Ebinger, Goldbach, and Schneidewind (2006) for green supply chains, environmental indicators are proposed for each phase of the supply chain. These indicators are grouped together under four constructs, each relating to a specific phase of the supply chain: (1) inbound logistics; (2) production and internal supply chain; (3) outbound supply chain; and (4) reverse logistics (See Exhibit 1, in Appendix).


The objective of this paper is to develop environmental indicators for measuring and monitoring environmental performance across the supply chain. The conceptual framework for this study is schematically represented in Figure 2. Environmental performance is defined in terms of tenets that are widely accepted in the extant literature. The guidelines/postulates for environmentally responsible products as proposed by the Asian Development Bank (2007) and accepted by the industry, have been followed. These postulates concern the conservation of resources, energy efficiency, and the reduction of hazardous waste generation (solid, liquid, and gas) and greenhouse gas emissions.

Hypotheses This study validates the four hypotheses listed below, and checks whether the green supply chain indicators are significantly correlated to the performance of the green supply chain. H1: The environment indicators would have a significant impact on the conservation of resources.

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Figure 2: Conceptual Framework

Environment Indicators

Environmental Sustainability Factors

Indicators for inbound logisics

Indicators for production/ internal logistics

Environmental Sustainability: Energy efficiency, Conservation, Reduction of hazardous waste, Reduction of green house gas emission

Indicators for outbound logistics

Indicators for reverse logistics

H2: The environment indicators would have a significant impact on energy efficiency. H3: The environment indicators would have a significant impact on the reduction of hazardous waste generation (solid, liquid, and gas). H4: The environment indicators would have a significant impact on the reduction of greenhouse gas emissions.

The survey instrument was a research questionnaire which requested the companies to carry out a rough computation with regard to the 20 environment indicators listed in Exhibit 1, and also with regard to the self-assessed achievements of the companies on the four environmental sustainability aspects, namely, resource conservation, energy efficiency, hazardous waste reduction, and reduction of greenhouse gas emissions.

Data Collection

The questionnaire was mailed to the chief pollution officers or pollution control officers in all of the companies in the NCR region. This was then followed up with a telephonic request to mail back the questionnaire.

In order to explore the significance of the linkages between the green supply chain performance indicators and the achievement of the four components of environmental sustainability, a survey-based study, based on the responses from companies operating in the National Capital Region (NCR) around Metro Manila, in the Philippines, was proposed. Since the nature and characteristics of supply chains are heterogeneous and vary across different industry verticals, it was decided to confine the research to one industry only. There is a large population of companies in the semi-conductor industry in the NCR region, with over 900 companies in all, varying substantially in size. Hence, it was decided to use the population of all of these companies as the master database from where a significant sample could be drawn.

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The four factors of achievements of environmental sustainability were measured by questions such as those listed in Exhibit 2. These questions were rated on the 4point Likert scale with ratings as: Strongly agree Agree Somewhat agree Disagree

= = = =

4 3 2 1

Sampling Frame The sampling frame for the research comprised all the companies in the NCR region which fell under the semiconductor and electronics industry verticals. In 2008, there were more than 900 companies under this vertical,


some of them large world-class companies, and some medium and small ones – 72 percent of these were foreign and 28 percent were national companies. Together, they accounted for 70 percent of the total exports, and provided about 400,000 jobs.

whose value ranges between 0 and 1.0, with higher values indicating greater reliability among the indicators. Cronbach’s alpha or reliability estimate can be formulated as:

The manufacturing process of this industry is complex and sophisticated, and the companies have extended supply chains comprising inbound, production and outbound logistics, but not much of reverse logistics, although some remanufacturing takes place. This is because, in the Philippines, products are not collected back after use from the customers, or remanufactured in large volumes, especially by medium and small companies.

where,

Since the smaller companies in this industry are not wellorganized in terms of their supply chains, this study was confined to the medium and large companies, which numbered about 570. The survey questionnaire was mailed to these 570 companies in National Capital Region near Metro Manila. The obtained sample had 124 responses — 31 from large companies (with asset size over 15 million pesos), and 93 from medium companies (with asset size between 3 million and 15 million pesos). The sample size was not as large as would have been preferred. But it was a random sample, and the sample size gave rise to a margin of error of 8 percent, which is acceptable in most industrial research.

DATA ANALYSIS Reliability Analysis The first step of the data analysis was to check the reliability of the indicators under each of the four constructs in the sense that the indicators under the associated construct should correlate significantly with one another. As is well known, reliability implies that the set of indicators under a construct are consistent in their measurements. In other words, reliability is the degree to which a set of two or more indicators measure one common theme associated with the construct. If a construct is highly reliable, it implies that the indicators under it are highly correlated, indicating that they all measure the same latent construct. A commonly used measure of reliability for a set of two or more indicators under a construct is Cronbach’s alpha, VIKALPA • VOLUME 39 • NO 1 • JANUARY - MARCH 2014

[(K/(K-1)] × [1- {(∑Xii ) / (∑Xii+ ∑Xij ) }] i ≠ j; Xii and Xij are elements in the correlation matrix of the indicators under a construct K is the number of indicators within a construct ∑Xii indicates that the elements in the diagonal of the correlation matrix are to be added together ∑Xii + ∑Xij indicates that all the elements in the correlation matrix are to be added together. Using the above formula, the Cronbach’s alpha estimates were computed for each construct. The corresponding Cronbach’s alpha for each construct is as shown in Table 1. Table 1: Cronbach’s Alpha for the Four Constructs Construct

Cronbach’s Alpha

Inbound Indicators

0.8232

Production/Internal Indicators

0.7543

Outbound Indicators Reverse Logistics Indicators

0.7839 Not applicable (only one variable)

Since the Cronbach’s alpha levels were of acceptable standards (> 0.7), it was concluded that the four constructs and the associated indicator variables could be used as composite variables. Next, the four hypotheses that were discussed earlier were validated by running multiple regression analysis using the four constructs as independent variables. The composite variable for each construct was the average percentage rating of the variables (indicators) under each construct. While computing this average, care was taken to consider the sign of the indicators because certain indicators are better if small and others are better if large. Four regression models were considered. In the first, the dependent variable was the environmental sustainability factor of resource conservation. In the second regression model, the dependent variable was energy efficiency, while in the third, it was hazardous waste reduction, and in the fourth, the dependent variable was reduction of greenhouse gas emissions.

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Multiple Regression Analysis Multiple regression analysis was conducted to see which independent/predictor variables significantly correlated with a chosen dependent variable. Four different regression models were used to validate each of the four hypotheses. The dependent variables for each of them are presented in Table 2; the independent variables were the same for all four runs. Table 2: Dependent and Independent Variables for Regression Analysis Dependent Variable

Independent Variables for all Regression Runs

Regression 1

Resource conservation

Inbound indicators

Regression 2

Energy efficiency

Production indicators

Regression 3

Reduction of hazardous waste generation

Outbound indicators

Regression 4

Reduction of generation of greenhouse gases

two independent variables which emerged as significant were inbound logistics (p-value = 0.027) and reverse logistics (p-value = 0.0253). In the construct for the inbound logistics phase, the indicators that were included pertained to the proportion of recycled and recyclable material, reusable material, etc. The higher these proportions are, the higher will be the saving in the material used, leading directly to resource conservation. The reverse logistics phase also has a significant impact on resource conservation, perhaps because in this phase, the products are picked up after the customer’s use, usually broken apart, remanufactured, and put back in the supply chain (Rao, 2008a; Fleischmann, 2001). Amway uses this method to cut down on the sourcing of plastic, and thus save on resources as well as cost.

Reverse logistics indicators

Regression Model for Energy Efficiency

The results of the four regression runs are summarized in Table 3 which gives R-square, F-significance, β-coefficients, and p-values for the four regression runs. If the pvalue < 0.05, it would imply that the associated predictor variable would have significant impact on the dependent variable.

RESULTS Regression Model for Resource Conservation This regression model was run using resource conservation as the dependent variable, and the four constructs pertaining to each phase of the supply chain as independent variables. The R-square obtained was 0.528, with the associated Fsignificance at 0.032. Since this level is less than 0.05, the model is significant at 5 percent level of significance. Based on the p-values for the independent variables, the

This regression was run with energy efficiency as the dependent variable, and the four constructs pertaining to inbound logistics, production, outbound logistics, and reverse logistics as the independent variables. The Rsquare was 0.328, with an associated F-significance at 0.098. Since this level is more than 0.05, the model was considered not significant at 5 percent level, but significant at 10 percent level. Since the data obtained did not lead to a very significant model, we did not explore the significance of the different constructs on the dependent variable was not explored any further.

Regression Model for Hazardous Waste Reduction In this regression model, the R-square was low at 0.359. However, since the associated F-significance was 0.048, the model is significant at 5 percent level, with the constructs for the inbound phase, the production phase, and the reverse logistics phase having significant partial cor-

Table 3: Summary of Results for Four Runs of Regression Analysis

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Predictor Variables

Regression 1

Regression 2

Regression 3

Regression 4

Inbound indicators

0.35 (0.027)

Production indicators

1.254 (0.089)

1.52 (0.048)

0.63 (0.021)

-0.094 (0.85)

0.834 (0.037)

1.284 (0.035)

Outbound indicators

2.38 (0.061)

1.276 (0.0276)

1.276 (0.384)

-0.782 (0.58)

0.94 (0.042)

Reverse logistics R-square

1.483 (0.0253)

0.83 (0.28)

1.452 (0.016)

0.32 (0.52)

0.528

0.321

0.359

0.529

Significance of F-value

0.032

0.098

0.048

0.0327


relations on the dependent variable. The construct for the inbound phase measures the extent and use of hazardous materials which enter the supply chain in this phase. Usually, the input of hazardous materials leads to the production of a hazardous waste stream. Thus, if the sourcing of hazardous materials is less, the production of hazardous waste will be greatly reduced. The production phase includes the indicator which measures the extent of recycling in the hazardous waste stream. Thus, more the recycling (as measured by the indicator), the greater will be the reduction in the hazardous waste. For this model, the reverse logistics phase also has a significant partial correlation on the dependent variable. This result is predictable because reverse logistics usually prevents the hazardous component in the product from flowing out and contaminating land and ground water (after the customer’s use). Continuing with the earlier example of car batteries, if the used batteries are collected back from the customers, and remanufactured in the reverse logistics phase, the impact of hazardous waste would be significantly reduced (Rao, 2008a).

Regression Model for Greenhouse Gases Reduction This model is statistically significant at the 5 percent level, with the production phase and the outbound phase having significant partial correlations with the dependent variable. In the production phase, there is usually high use of nonrenewable energy, which should give way to the use of renewable energy. When this transformation from nonrenewable to renewable energy resources occurs, the indicator in the production phase measures it. This leads to reduction in the emission of greenhouse gases since these gases are primarily emitted from non-renewable energy sources. The outbound phase of the supply chain encompasses the transportation of finished products to the distributors and sales outlets. This is often carried out by fleets of vehicles that use traditional fuels, which emit greenhouse gases in large amounts. If the use of vehicles running on traditional fuels were replaced — even partially — by electric, CNG (natural gas), and other emission-free vehicles, it would lead to a significant reduction in the emission of greenhouse gases (Carmody & Ritchie, 2007; Arroyo, 2003). Such an effort is measured by the indicaVIKALPA • VOLUME 39 • NO 1 • JANUARY - MARCH 2014

tors in this phase. These results indicate that of the four hypotheses proposed earlier, H1, H3, and H4 were validated at the 5 percent level of significance, whereas H2 could only be validated at the 10 percent level of significance. In the next section, some pointers are discussed for the use of green supply chain indicators in monitoring the environmental sustainability performance of a company’s operations. Since the indicators were developed primarily to measure and monitor performance, a brief description of the implementation aspects would add value to their applicability.

IMPLEMENTATION PLAN FOR ENVIRONMENTAL INDICATORS The basic objective of developing environmental indicators was to measure the environmental performance of an organization across its entire supply chain. Having established that the indicators proposed in this paper actually did measure environmental performance, the next step would be to consider how best they could be implemented in the industry setting. As such a detailed implementation plan could be considered, including (1) process control analysis, (2) benchmarking, and (3) brainstorming procedures. The process control analysis relates to the monitoring aspect or the internal assessment of the performance of the green supply chain within its own operations in the company (internal benchmarking). The benchmarking aspect could be used to compare the performance of the company’s own green supply chain with that of the industry standard, and with that of a state-of-the-art company within the industry vertical, provided that such data is available (competition-oriented benchmarking) (Schaltegger, Herzig, Kleiber, & Muller, 2002). If there is any area where the indicators bring out an anomaly, and indicate the need for correction or attention, brainstorming procedures with active discussion regarding the possible causes of the anomaly would help to diagnose the reasons for such an occurrence (Park, 2003; Swanson, 1995).

Using Indicators in Process Control Analysis The indicator system for the green supply chain can be very useful for early warning and early identification

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purposes, should any part of the greening initiative not work, or not achieve environmental performance the way it was meant to. This facilitates timely diagnosis, sending a signal to the operations manager to look into the problem. Continuous monitoring would help to find out if the environmental performance as per any green supply chain indicator is not up to the mark. For instance, if the energy consumption per total output went up in any quarter, it would become an early warning system; the environment manager would have to investigate what caused the sudden increase. Similarly, if the water consumption or waste water output went up in any quarter, the manager would need to find out whether there was a leak in the water control system, etc.

Using Green Supply Chain Indicators to Benchmark Performance In this analysis, the indicator values are computed for the company as usual. These are then compared against the corresponding figures for the industry average, and also against the figures for the state-of-the-art company in that industry vertical (benchmark values). For both these sets, if the difference between the company indicator and the industry average figures, or the difference between the company indicator and the state-of-the-art company (benchmark values) are significant, then an investigation as to why the difference occurred would be required. The benchmarking with indicators serves as a motivating force for improvements and innovation. (Rao, 2008a)

Using Green Supply Chain Indicators to Identify Possible Causes for Anomalies Whenever the difference between the company’s performance and the benchmark performance is large, the company should be encouraged to hold a brainstorming session with its employees to investigate the cause of the difference, and to address the problem. For this purpose, a Fishbone/Cause and Effect/Ishikawa Diagram may be used3. The essence of this diagram is that any problem or concern could be attributed to four major reasons, broadly

speaking, emerging out of four factors relating to ‘man’, ‘machine’, ‘materials’, and ‘methods’. At the head of the diagram is the problem or the concern; the possible reasons leading to the concern are categorized under the four categories in the form of four causes, which are further subdivided into smaller and more detailed causes. In the present context, the main concern could be the fact that the company did not do well, as detected by the green supply chain indicators. The reasons for this, to be identified and detected during the brainstorming session, could be one or more of the following: a) Human problems (man): Lack of technical knowhow (which could be due to lack of proper education/training), attitude, fatigue (long hours), laziness (due to lack of motivation, or lack of sufficient financial incentive), etc. b) Machine-related problems (machine): Lack of technologically advanced machines which produce reduced quantities of emissions and waste. This could be due to financial reasons, lack of knowledge about advanced technology, etc. c) Material-related problems (materials): Using materials which are not environment-friendly (which may generate hazardous or other kinds of waste), using natural and virgin materials in excess, using materials which are not reusable or recyclable, etc. The reasons for not using environment-friendly materials could be lack of knowledge regarding such dimensions, high cost of such materials, etc. d) Processes-related problems (methods): Using technology which is not environment-friendly, that is not clean and green, etc. Again, this could be due to lack of knowhow, lack of finances, lack of awareness, etc.4 An example of a Cause and Effect Diagram for identifying the reason why EI-4 (proportion of hazardous material to total material input) is higher than the benchmark level is represented in Figure 3 for illustrative purposes.

4

http://en.wikipedia.org/wiki/Ishikawa_diagram, http://www. mind tools.com/pages/article/newTMC_03.htm.

3

http://www.doh.state.fl.us/hpi/pdf/Cause_and_EffectDiagram2.pdf http://www.hci.com.au/hcisite3/toolkit/causeand.htm

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http://www.google.co.insearch?q=ishikawa&hl=en&rlz=1R2SK PB_enPH372&prmd=imvnsb&tbm=isch&tbo=u&source=univ &sa=X&ei=o67UT8umEYqqrAeZ4_j8Dw&sqi=2&ved=0CIMBE LAE &biw=1080&bih=469.


Figure 3: A Cause and Effect Diagram

Cause MAN No Experience

Effect MATERIALS

WORK METHOD No knowledge on hazardous materials

Hazardous input

No Training

Hazardous catalysts

Lack of Skills

No training

No knowledge from supervisor

Hazardous packaging

No knowledge on non-hazardous alternative

No awareness

Proportion of hazardous material is more than benchmark

Company did not measure hazardous components Supplier did not measure hazardous inputs

MACHINE/MEASUREMENT

DISCUSSION Environmental indicators are operational management tools for measuring environmental performance and control, and can be used in planning, steering, and realigning processes which help conservation of resources, prevention of pollution, and minimization of waste. Using the green supply chain indicators and the three steps discussed earlier, a company should be able to (1) identify potential areas of improvement in the performance of its green supply chain so as to arrive at the benchmarking standard in the particular industry; (2) identify, on a quarter-to-quarter basis, if the environmental performance in any phase of the green supply chain is falling (from the process control analysis); (3) sort out concerns in the green supply chain by conducting brainstorming sessions involving employees and management; and (4) develop a system for Corporate Environmental Reporting (CER) to the government and community, and publish the levels of green supply chain indicators on an annual basis. VIKALPA • VOLUME 39 • NO 1 • JANUARY - MARCH 2014

The indicators, as described above, are in reality compressed representations of facts that can be recorded in quantitative terms (Schaltegger et al., 2002). Although they are used as relative indicators (ratio indicators) in the current paper — showing the ratio between two or more values — in order to make them easier to understand and compare, they could have been presented as absolute indicators, such as means, sums, and differences. We chose to use them as ratio indicators because the Philippine companies in the sample had difficulty providing the data in terms of absolute indicators.

LIMITATIONS The sampling frame for the research comprised 570 medium to large companies of the total population of 900 companies in the semi-conductor industry operating in the National Capital Region. This is not a very large number, which indicates a limitation to the research. Also with this sampling frame, a random sampling could not be conducted because it would have further reduced the number of companies to whom the questionnaires were

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mailed. However, the response rate was good, and yielded a sample of size 124, where the margin of error was acceptable (less than 10%). However, the responses were presumably obtained from companies which were already aware of greening initiatives and green supply chains, which could mean a possible response bias. Moreover, the four factors of environmental sustainability, when considered as dependent variables, are measured based on the subjective judgements of the companies (i.e. the representatives of the companies) themselves. Many companies in the semi-conductor industry said that they had difficulty in working out the indicators because they did not maintain data pertaining to the proportion of recycled materials to total material input, the proportion of hazardous material, etc. It was explained that they could request the internal auditors within the company to work out approximate levels for the various indicators. Thus, the research was conducted with these approximate levels rather than accurate levels of the indicators.

CONCLUDING REMARKS The aim of this exploratory study was to examine the importance of measurement and the use of environmental performance indicators representing numerical measures to provide key information related to environmental issues across the integrated supply chain. Several reasons justifying the importance of environmental indicators across the supply chain were considered. Essentially, the indicators were to be used as components of the performance assessment system to help the implementation and evaluation aspects of a green supply chain. Thus the indicators were to act as monitoring and benchmarking tools for a performance monitoring system which would encompass the entire supply chain. In today’s world, organizations are increasingly being held responsible for environmental actions, as reflected by the growing number of laws, regulations, and penalties in this area. Consequently, organizations are now obliged to measure, control, and disclose, not only their own environmental performance, but also of their entire supply chain. Also, to see if the system of indicators which an organization uses for the supply chain indeed measures environmental performance, it is necessary to check if they correlate significantly with the organization’s environmental performance. In this research, this environ-

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mental performance has been described as comprising resource conservation, energy efficiency, hazardous waste reduction, and minimization of the emission of greenhouse gases (Vachon & Mao, 2008). In order to check the linkages of environmental indicators to the four constituents of environmental performance, four multiple regression models were run. In the first model, the dependent variable was resource conservation, the independent predictor variables being the 20 environmental indicators grouped under the four constructs: Inbound logistics, production or internal logistics, outbound logistics, and reverse logistics. In the second, third, and fourth models, the dependent variables were energy efficiency, reduction of hazardous waste, and minimization of emission of greenhouse gases, respectively. Upon running the four regression models, it was observed that out of four, three models were significant at 5 percent level of significance, considering the F-test of the R-square value. Thus, the models for resource conservation, reduction of hazardous waste, and reduction of emission of greenhouse gases were significant at 5 percent significance level. The model for energy efficiency was significant only at 10 percent level. The indicators for inbound logistics and production correlated significantly with environmental performance in three out of four models. The indicators for outbound logistics is significant only for minimization of greenhouse gases and that for reverse logistics is significant for resource conservation and hazardous waste. Looking at the results from a practical viewpoint, one observes that what emerged as statistical output makes a lot of logical sense. The inbound indicators did correlate with resource conservation, energy efficiency, and reduction of hazardous waste, while production indicators correlated with energy efficiency, hazardous waste, and greenhouse gases. This result is totally expected because efficiency in input materials has to translate into environmental performance in conservation, energy, and hazardous waste. Again, production indicators signifying cleaner production and cleaner process must lead to efficiency in energy use, hazardous waste reduction, and emissions.


Thus, the statistical conclusions, as observed from modeling procedures, did conform to managerial practical logic which is what makes this research pertinent and useful.

only and also within one industry vertical. It still needs to be implemented in other regions and verticals, and even in non-manufacturing sector to see if the indicator system would still validate the four hypotheses.

The list of indicators used could have been more extensive but the practicality of getting values for them from companies was a concern. Hence, as a starting point, organizations adopting green supply chain concept can surely consider adopting the environmental indicators on their processes too to measure and monitor whether the expected environmental performance is actually achieved.

In another dimensions of future research, the set of 20 indicators could be expanded to include many more indicators, which would help to monitor environmental performance in green supply chains. A larger population, sampling frame, and sample size would help to enhance the theoretical foundation in the proposed models. Additionally, instead of arriving at the measures of environmental sustainability by self-assessed responses, actual measurements based on company databases could be used. A larger set of indicators would also decrease the possibility of missing out on important indicators that affect sustainability, which in turn could yield much higher levels of R-square, and thereby yield more significant statistical models.

FUTURE RESEARCH DIRECTIONS When the four regression models were run, for each model, even for significant ones, one observed that the R-square value was not very high, being > 50 percent only for resource conservation and greenhouse gases. Hence, while it is true that the environmental indicators do lead to environmental performance, which validates the four hypotheses H1 (at 5% level), H2 (at 10% level), H3 (at 5% level), and H4 (at 5% level), there are other predictors too which contribute to environmental performance. This could be examined as part of future research. Again, this research is carried out in only one country

Future research could also focus on developing indicators which would capture the interactions of the networks of companies in the supply chain. The indicators described in the current paper do capture the impact of interactions. However, further indicators could be developed which depict the interactions more clearly and directly so that a better measurement of these interactions is possible.

APPENDIX Exhibit 1: Constructs for Supply Chain and Associated Indicators for Greening Inbound Indicators

EI-1

Proportion of recycled materials to total material input in %

EI-2

Proportion of recyclable materials to total material input

EI-3

Proportion of reusable materials to total material input

EI-4

Proportion of hazardous materials to total material input

EI-5

Proportion of suppliers with environmental policy

EI-6

Proportion of suppliers for whom environmental audit is conducted by the company

Production/Internal Indicators EI-7 EI-8

Outbound Indicators

Raw material efficiency = Proportion of raw material in tonnes to total product output in tonnes (%) Proportion of cost of energy in production to total value of output (%)

EI-9

Water efficiency = Proportion of total water consumption to value of total output (%)

EI-10

Proportion of material recycled in the production phase to total value of output (%)

EI-11

Proportion of total cost of renewable energy in production to total value of output (%)

EI-12

Proportion of total solid waste to total output in value (%)

EI-13

Proportion of total solid waste for recycling to total product output (%)

EI-14

Packaging proportion = Proportion of total value of packaging to total output

EI-15

Proportion of hazardous waste produced to total product output

EI-16

Volume of air emissions per year (NOx, SOx, CO2, VOC, etc.)


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Reverse Logistics Indicators

EI-17

Proportion of total waste water produced to total output

EI-18

Proportion of total waste water treated to total product output

EI-19

Use of vehicles that run on renewable energy, electricity, and natural gas (for distribution of finished products)

EI-20

Proportion of output recovered from customers after use and put back in the supply chain, if any

Source: Rao, 2008a

Exhibit 2: Sample Questions from the Questionnaire used for Data Collection Q.1. On account of the greening of the supply chain, significant improvement in the conservation of resources has been achieved

4) Strongly agree 3) Agree 2) Somewhat agree 1) Disagree

Q.2. On account of the greening of the supply chain, significant improvement in energy efficiency has been achieved


Q.3. On account of the greening of the company’s supply chain, significant reduction in the generation of hazardous waste has been achieved


Q.4. On account of the greening of the company’s supply chain, significant reduction in the generation of greenhouse gases has been achieved


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Purba H Rao is a professor of Business Analytics and has been teaching, consulting and conducting research in India, Philippines and Indonesia. She is a Fellow in Management from IIM Calcutta and is currently a Visiting Professor in IIM Ahmedabad, IIM Ranchi, and Great Lakes Institute of Management, Chennai. She has published four books including Business Analytics: An Application Focus, PHI, 2013 and Green-

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