A Supply Chain Management View of Human Capital-Intensive Firms

Introduction

At the heart of global competition and technological change is the growing conviction among organizations that human capital is a key driver of competitive advantage, and especially for high human capital intensive firms (HCIFs) (Hatch and Dyer 2004; Coff and Kryscynski 2011; Campbell et al., 2012a,b; Carnahan et al., 2012; Nyberg et al., 2014; Della Torre et al., 2018). In many HCIFs, the increased importance of specific human capital is coinciding with similarly increased exploitation of massive data, and the control over results and forecasting which they allow. While much research attention has focused on HCIFs (Campbell et al., 2012b; Carnahan et al., 2012) and massive data (Wang et al., 2016), few studies have examined the management of massive data within HCIFs to understand the changes they induce. The management of massive data has transformed data management methods quite radically, making it possible to adapt data technologies and strategies by providing information critical for targeted and optimized actions. Data management is providing opportunities to access new business opportunities and better control the inherent risks. The quality and governance of Big Data are major concerns for HCIFs. Development tools are being implemented to increase the uptake of these new technologies but the importance of human-capital-intensive employees (such as highly skilled developers) should not be overlooked.

Our approach to HCIFs focuses on the logistics sector. This sector is an example of the transformation of the traditional “old economy” such as manufacturing (and automotives in particular) to an economy in which innovation capacity linked to the immaterial is an important part of value creation. In the logistics sector in particular, disruptive technologies linked to Big Data management along global supply chains are being implemented. Productive activity no longer involves traditional factors such as fixed capital and labor; it requires specialized skills, organizational structures and procedures, a corporate culture, and a myriad other resources described as “intangible assets” (Brynjolfsson et al., 2002). This is making it difficult to keep human assets subordinate to physical assets. The growing intensity of globalized competition, and the technological progress and the increased financing possibilities in financial markets are reinforcing the substantial nature of innovation. Innovation necessarily requires employees with specific and specialized human capital. The race to innovate is driven essentially by human capital, and especially those working in R&D but increasingly also, employment in all other phases of the production process. In this dynamic perspective, tangible assets are losing their importance in favor of the individuals employed in the productive activities of firms.

This chapter aims to shed pragmatic light on the management of the disruptive technologies underlying Big Data within HCIFs in the field of logistics and supply chain management (SCM). How do HCIFs mobilize and value disruptive technologies based on massive data? The chapter is structured in five sections. The first section discusses how HCIFs mobilize and value disruptive technologies, based on massive data, in the context of the existing literature. In the second section, we identify the dimensions of digitization and digitalization by studying three emblematic HCIF cases that rely on these disruptive technologies−namely the control towers of logistics service providers (LSPs), and the business spheres and supply chain cockpits of shippers. These three cases make it possible to identify what is at stake for the HCIF, and the limits imposed by massive data management. In the third section, we propose some solutions and recommendations to mitigate the limitations identified. We suggest that a new generation of functional and informational integration tools is emerging based on the human capital and SCM in the context of massive data. The final two sections discuss some future perspectives, and outline our conclusions.