On the relationship between financial performance and position of businesses in supply chain networks

Abstract

We investigate how the network position of organisations in an extended supply chain network impacts their financial performance. The paper argues that performance measurement tools should incorporate a network (external, connectedness) perspective in addition to an internal financial perspective. We merge local networks of companies in a supply chain into a single, extended network in which the transactional value-flows on arcs are known. Network position characteristics of focal companies are determined using social network analysis. The impact of these characteristics on financial performance is studied using regression analysis. In the context of our case study, there is some evidence that profitability is related to connectedness and market share. In this way, the paper describes how organisations might enrich their performance measurement tools with connectedness metrics.

Introduction

Many organisations, to be more effective and efficient, link with other organisations to form supply chains. The nature and extent of the linkages in these supply chains are expected to impact upon business dynamics and organisational performance (Wagner et al., 2012; Um and Kim, 2018). Global manufacturing operations mean that networks formed of supply chains are typically large and complicated. Furthermore, organisations realise that, in the future, competition to attract more end-customers will be transferred to the supply chain level, pitting one supply chain against another. Verganti and Pisano (2008) state that “it is not about the decision whether to collaborate, but the need to understand and to take informed decisions”. Also, it is increasingly recognised that “if a supply chain is properly managed, its whole value can be greater than the sum of its parts” (Shi and Yu, 2013). Thus, it is important to understand competitive advantage by establishing a set of metrics that can measure and evaluate the performance of the entire supply chain, and thereby guide strategic interventions (Gunasekaran et al., 2004; Chae, 2009). These modern, global realities together with a need for new concepts in performance analysis has led to increased research into supply chain performance measurement (Gopal and Thakkar, 2012; Balfaqih et al., 2016; Maestrini et al., 2017).

In general, performance measurement systems are associated with reference models that contain a standard description of management processes, a framework of relationships among the standard processes, and standard metrics to measure process performance (Ittner et al., 2003). However, organisations often neglect the use of performance measurement tools, basing their management and analysis on experience and intuition (Simchi-Levi et al., 2004), or even, fail to implement effective tools or extract maximum value from their performance measurement data (Hudson et al., 1999).

A broad categorisation of performance measures is obtained by classifying the different measures into financial and non-financial, both often referred to as synthetic process indicators (Merchant and Van der Stede, 2007). Many organisations, when typically monitoring their broad set of activities and processes, use financial performance measures. When these processes are linked to external entities, the focus needs to extend beyond the boundary of companies, constructing inter-organisational management and network accounting systems (Chenhall, 2005). These systems should better support a single organisation (called a focal company) working within a network. They should also better support the management and performance of the entire network, by monitoring the ability of the network itself to leverage network output. Indeed, when Melnyk et al. (2014) consider whether performance measurement is fit for the future, they find that practitioners are often struggling to manage in volatile environments. While performance measurement applied within the boundaries of the focal company has evolved from design and implementation (Bourne et al., 2000; Neely et al., 2001) to application and review (Henri, 2006; Braz et al., 2011), the external view has fallen behind. Thus, the literature on performance measurement describes performance measurement across organisational boundaries as a challenge (Brewer and Speh, 2001; Chan and Qi, 2003; Folan and Browne, 2005; Li et al., 2005; Shepherd and Günter, 2006; Acar et al., 2009; Hernández‐Espallardo et al., 2010; Maestrini et al., 2017).

Consequently, it is no longer sufficient to analyse performance only from the narrow perspective of the focal company and only in financial terms (i.e. returns), ignoring aspects such as cooperation and socialisation among firms (Franco-Santos et al., 2012; Um and Kim, 2018). In our paper, therefore, we present research that uses social network analysis to investigate the linkage between organisations in supply chain networks and their performance. In so doing, we evaluate influences on the performance of an organisation by measuring key position variables that are derived from social network theory and that reflect the entire network structure and its relationships. Our focus is small and medium enterprises (SMEs) in the German automotive plastics processing industry.

We study a single, complete supply chain network of fifteen focal companies and their suppliers and customers. A software tool created by us merges the supply chain data of the focal companies into the single, extended supply chain network which has no clear boundaries. Our approach is more holistic than the standard analysis of dyadic connections of company-specific supply chains. We collect data on the network position of the focal companies by detecting and interpreting patterns of links among all stakeholders within the network. Also, our network is more than binary because we collect data on transactional value flows on the network arcs (links between the focal companies). We evaluate the financial performance of these companies using business reports drawn from the business register of the German Federal Gazette and data available from commercial providers of digital business information such as Bisnode. In our network, we study 448 companies in all, the fifteen focal companies and their 433 suppliers and customers. Connections between the companies are evaluated on the basis of two different weighting schemes: cash flows (for products or materials) and product-type flows (number of distinct product-types).

This paper is important because we quantify the effect of network position upon financial performance of organisations in a visible network using real-time transactional data that distinguishes upstream and downstream linkages. Maestrini et al. (2018) recently identified the need for such a study. Other studies similar to ours either do not make the network visible (Li et al., 2013), treat networks separately by overlooking possible interactions (Kim et al., 2011), use questionnaire data to quantify the network (Gronum et al., 2012) or study binary networks (Schilling and Phelps, 2007). On visualisation, as Basole et al. (2016) state, this is challenging because “complete or even comprehensive supply network data are generally not available”, if they are “the information can often be overwhelming to the end user if not presented appropriately”, and therefore “supply network visualisation hardly happens”. Furthermore, Nooy et al. (2011) state that visualisation is important to trace and understand patterns of ties intuitively.

Our purpose is to provide an evidential basis for the enhancement of standard performance measures with metrics that quantify the connectedness of an organisation in its supply chain network. We also address the need for further research into external aspects of performance measurement, as “most studies addressing these aspects are either discussed theoretically or investigated by means of context-specific implementation of a particular supply chain performance measurement system framework” (Maestrini et al., 2017).

The structure of the paper is as follows. In the next section, we position our study and its contribution within the existing literature. Then, in section 3 we describe our methodology and present our research questions, specifying well-defined and testable hypotheses relating to these. Section 4 describes the supply chain network of interest in detail. In section 5, the results of our analysis are presented, and we make conclusions and discuss avenues for further research in section 6.