A New Model for the Digital Transformation of the Strategic Procurement Function: A Case Study From the Aviation Industry

A New Model for the Digital Transformation of the Strategic Procurement Function: A Case Study From the Aviation Industry

Andrea Altundag
DOI: 10.4018/978-1-7998-7712-7.ch006
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Abstract

The purpose of this chapter is to illustrate the application of advanced data analytics in the domain of strategic procurement and its effects on processes, people, and on the procurement business model itself. Advanced data analytics are generally accepted as being one of the key enablers for organisations to build their capabilities to adapt quickly and navigate through volatile business circumstances successfully. Strategic procurement is in a pivotal position in a network of external suppliers and internal stakeholders, and thus ideally positioned to benefit from the introduction of advanced data analytics. However, to date, the application of these technologies has been limited, and clear evidence of benefits delivery is yet to be demonstrated. This chapter draws upon research results from a detailed case study in the aviation industry to assess the benefits of advanced data analytics in the strategic procurement function and puts forward a maturity model of relevance to both researchers and procurement professionals.
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Introduction

The aviation industry is comprised of the air transport sector (including airlines and airports) and the aircraft-manufacturing sector. The industry, as a whole, accounted for $961.3 billion directly and $816.4 billion indirectly of the world’s gross domestic product (GDP) in 2018-2019, and supported 11.3 million jobs directly and 18.1 million jobs indirectly worldwide before Covid-19 hit the industry (ATAG, 2020). Aviation offers global connectivity and has had a significant impact on people’s perception of distances and the so-called “shrinking of the world.” The advancement of aircraft technology and the increasing affordability in the transportation of goods and people have altered mobility patterns. Aviation has made an “incalculable contribution to global trade and economic development” (Asquith, 2020, April 6, para. 7).

Since 2010, the industry has seen steady growth, as aircraft manufacturers and their suppliers benefited from a “super cycle” of full order books and a substantial backlog of work (Airbus, 2020; Hader, 2020, February 24). In early 2020, global aircraft manufacturer Airbus forecast a prosperous, yet challenging, mid-term future for the industry, in which the biggest challenges would relate to the satisfaction of the immense demand for passenger aircraft, and the limitations of the industrial systems trying to cope with ever accelerating operations and increasing production rates. Further challenges included the fierce competition from existing and emerging competitors, in particular from Chinese aircraft manufacturers, who are expected to take advantage of their rapidly expanding domestic market, for which it is estimated 8,600 new airplanes will be required in the next 20 years (Boeing, 2020, p.8.). In addition, enhanced environmental awareness amongst society at large poses a severe challenge to the industry. Both politicians and the general public are demanding a substantial reduction of emissions, which puts immense pressure on the industry to improve the environmental footprint of its supply chain and operational performance. Sustainability emerges as one of the next megatrends, which will form an integral part of the annual corporate objectives of aviation industry companies for years to come.

With the Covid-19 pandemic negatively affecting the aviation industry since March 2020, all industry players face the prospect of an unpredicted and substantial business decline. Even though there have been some encouraging developments recently, the trajectory for recovery remains difficult to predict, but is likely to be slower than anticipated. This has served to underline the need for developing capabilities to adapt in an agile and flexible manner in this volatile environment. Both academic research and professional practice widely recognize digital transformation as a key facilitator in this ambition. Even in times of economic prosperity, the execution of a future-oriented transformation is often difficult, but the Covid-19 pandemic has only heightened the need for business re-focusing, forward planning and the acceleration of digital transformation (Adam et al., 2018; Schrage, 2020).

Data is at the heart of digital transformation, which has seen the emergence of a rapidly growing market related to data analytics and management, estimated to be worth $135 billion by 2025 (Bendor-Samuel, 2019, November 26). Nevertheless, organisations still struggle to transform to the “data-driven” enterprise, even though there is wide and cross-industry acknowledgement regarding the value of data analytics (Davenport & Bean, 2020). The concept of data-driven decision-making has been in evidence for at least three decades, but measurable business value has yet to be delivered in many areas of business operations. Strategic procurement as a function manages up to 80% of the external value add in an aircraft manufacturing company, and is acknowledged for its crucial contribution to business success. Corporate transformation must include the strategic procurement function to enable the development of a new value proposition, based on an accelerated and reliable sourcing process, thereby establishing a rationale for the continued existence of the function. A digitally transformed procurement function could both contribute to, and respond to, emerging tendencies that have an impact at corporate level, such as sustainability, which is generally accepted to be strongly interrelated with digital procurement (Nicoletti, 2020). To date, however, the response to the aforementioned challenges by strategic procurement functions across industry has generally been slow in practice, and the application of advanced data analytics in the area of strategic procurement is limited.

Key Terms in this Chapter

Strategic Procurement: The fundamental integration of purchasing and associated supply chain processes, responsible for procurement strategy and decision making in an organization. As an integrated, well-recognized corporate function, strategic procurement secures the application of a systematic sourcing process, targeting optimized cost, quality and time, and the management of supplier relationships, and thereby contributes to the organization’s long-term objectives.

Predictive Analytics: Analysis that uses past performance data and aims at forecasting developments. By extrapolating data, this technique helps to detect trends and hidden relationships to predict developments. Predictive analytics provide answers to the question: What will happen?

Descriptive Analytics: Analysis of historical data to identify patterns and trends within extant processual and functional environments to provide answers to the question: What has happened? Data is presented in relevant charts and reports to provide decision makers with standardized and customized summaries of information.

Advanced Data Analytics: The techniques used to identify, extract, and gather valuable knowledge from large amounts of data, structured and unstructured. The benefits stem from the combination of the immense volume of available data and the rapidly accelerating processing capabilities, both at technological and organisational level, to derive data-driven decision making.

Prescriptive Analytics: Identification and assessment of alternative possibilities to support advanced decision-making and thus business performance improvement, responding to the questions: What should I do to make it happen? and: Why should I do it? Prescriptive analytics entails a high level of data analytics maturity aimed at optimized decision making to underpin future business performance.

Business Intelligence (BI): A combination of processes, systems, tools, and methodologies understood as the first generation of data analytics with a focus on the analysis of historical and current data to facilitate effective decisions. Based on descriptive analytics, BI is often used for supporting current business operations.

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