Performance measurement is essential to provide information that reveals progress, diagnose problems, enhance communication, and provides critical feedback in the decision-making process (Chan et al., 2003). In the SC context, successful performance measurement should take a holistic system perspective beyond firm boundaries. Although there is a considerable stream of studies dealing with traditional (economic) performance measurement in SCM (Beamon, 1999; Neely, Adams and Kennerley, 2002; Chan et al., 2003; Gunasekaran and Kobu, 2007), the literature dealing with measurement of environmental and social aspects in SCM is not well developed (Taticchi, Tonelli and Pasqualino, 2013; Piotrowicz and Cuthbertson, 2015).
The performance measurement in SCM is hampered by many factors including lack of connection with strategy, lack of system thinking and loss of SC context, and lack of standardized measures across the SC (Wong and Wong, 2008; Gopal and Thakkar, 2012).
While overcoming these challenges is not a trivial issue, a sustainable SC should perform well on traditional measures (e.g., quality, costs) and on the social and environmental dimensions (Linton, Klassen and Jaraman, 2007). A successful performance measurement system facilitates the translation of strategies into measurable goals and actions and allows monitoring and analysing the progress regularly (Björklund, Martinsen and Abrahamsson, 2012).
The relationship between performance measurement and management is stressed by several authors. According to Bititci et al. (2011) performance management is a process that uses information generated by performance measurement to guide decision-makers aiming to connect strategy with daily operations. A similar argument is given by Atkinson (2012) who highlighted that performance management is a company-wide shared vision and performance measurement should be dynamic, flexible, and credible to support that vision. Furthermore, Grosvold, Hoejmose and Roehrich (2014) argue that a sustainable SC can be seen as a combination of three components: management, measurement, and performance. SC management and SC measurement influence on each-other in a circular relationship, and in turn both impact SC sustainability performance. Thus, a properly aligned sustainability performance measurement system in the SCM can help in
2.2 Measuring and managing sustainability performance in supply chains 31
developing collaborative inter-firm practices and processes that would allow a better understanding of SC goals and an enhancement of relationships across partnering firms.
It has been noticed that some companies provide lip service to integrate sustainable practices into their SC operations, suggesting a huge discrepancy between what practitioners say (theory) and do (practice) in reality (Walker and Jones, 2012; Taticchi et al., 2015). This might be because adopting sustainability across SC proved to be challenging (Morali and Searcy, 2013) and partly because many firms don’t know what to measure and how to measure their sustainability impacts (Beske-Janssen, Johnson and Schaltegger, 2015). Indeed the lack of measurement methods and tools is confirmed by many reviews focused on sustainability performance measurement of SCs (Taticchi et al., 2015; Schöggl, Fritz and Baumgartner, 2016; Qorri, Mujkić and Kraslawski, 2018).
However, as it was argued by several authors (Hervani, Helms and Sarkis, 2005;
Maestrini et al., 2017) measuring performance is difficult inside a single company, but when extending to the SC level it becomes highly complex (Sloan, 2010).
Improving competitive advantages requires measuring and managing sustainability across SCs (Qorri, Mujkić and Kraslawski, 2018). While performance metrics and measurement methods or tools are crucial components of the SC performance measurement system, the relevant SSCM literature is mostly focused on the first component. This is echoed by the review of Ahi and Searcy (2015) who identified over 2500 unique metrics, but how to aggregate or combine these metrics (By which method/tool?) into key performance indicators (KPIs) and to build a performance measurement system is rarely considered and thus it is unclear (Beske-Janssen, Johnson and Schaltegger, 2015; Büyüközkan and Karabulut, 2018). The KPIs assist in keeping managers and workers focused on core issues (Bai and Sarkis, 2014). KPIs should be arranged and generated by the performance measurement system to support (and control) managers in decision-making and to communicate SC sustainability performance to other stakeholders.
Another important aspect to highlight before discussing sustainability measurement methods or tools is related to the multidimensionality of both sustainability concept and SCM concept. First, a vast amount of data and information is required to be collected and processed to measure environmental, social, and economic dimensions of sustainability.
In addition, each dimension incorporates several aspects (e.g., environmental dimension covers aspects of air, water, land etc.), which in turn are measured through many various metrics. A visual representation of such a nested view of sustainability is best given by GRI (2010). Second, by definition, SC is “a set of three or more entities (organizations or individuals)…” (Mentzer et al., 2001), that is, a complex structure of flows and
relationships between SC members and their activities. Thus, when developing a performance measurement system, one should consider all these issues and usually in literature such complexity is presented by a hierarchical model. Some authors have also argued that sustainability assessment can be seen as multi-criteria decision-making (MCDM) problem (Diaz-Balteiro, González-Pachón and Romero, 2017). Overall, the idea is how to propose solutions for combining such complex sets of performance data in manageable quantitative or qualitative KPIs. Sustainability measurement methods and tools are used to generate such KPIs, and I briefly review them in the following section.
2.2.1 Methods for assessing sustainability in supply chains
As Beske-Janssen, Schaltegger and Liedke (2019) note the majority of articles dealing with sustainability performance measurement in SCs either do not consider at all or say little about specific measurement methods or tools. A small set of studies that suggest sustainability performance measurement approaches, methods or systems are indeed very different from each-other. The variety of approaches proposed spans from conceptual frameworks (Hassini, Surti and Searcy, 2012) to instruments such as Life cycle assessment (Hutchins and Sutherland, 2008) to modification of existing tools including balanced scorecard (Thanki and Thakkar, 2018) and Supply Chain Operations Reference (Bai et al., 2012).
While another set of studies utilizes the MCDM techniques (Büyüközkan and Çifçi, 2012;
Tajbakhsh and Hassini, 2015a), few others authors use also fuzzy set logic (Erol, Sencer and Sari, 2011; Uygun and Dede, 2016) to rank and aggregate sustainability metrics. A slightly different approach is also considered by using different standards and certifications such as International Organization for Standardization (ISO) 26000 (social responsibility), ISO 14032 (Environmental performance evaluation) (Nawrocka, Brorson and Lindhqvist, 2009). Other frameworks proposed by practitioners such as the Carbon Disclosure Project, GRI, and the International Federation of Accountants are sometimes used to measure sustainability performance (Taticchi, Tonelli and Pasqualino, 2013).
The use of such different tools or methods to measure sustainability of SCs, indicates the lack of a comprehensive and practical method or tool, which has been confirmed by several reviews (Sloan, 2010; Beske-Janssen, Johnson and Schaltegger, 2015; Qorri, Mujkić and Kraslawski, 2018). Additionally, the above mentioned tools have been criticized for not including all sustainability dimensions, for partly considering SC members (mainly the measurement is done between manufacturers and suppliers), most of them are developed to measure performance within the company and not across SC, most of them are also static by design and can process only quantitative data (but