Utilizing enterprise systems for managing enterprise risks

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Abstract

Enterprise risk management is a critical concept in the current business environment that supports use of tools and processes directed toward monitoring and mitigating organizational risks. Many organizations have embraced enterprise systems (ESs) technology for improving organizational efficiency and effectiveness. ESs provide value by identifying opportunities in operations and assist in managing risks through context sensitive analyses by eliciting relevant information. This research investigates how ES data were transformed into knowledge by a hi-tech manufacturing firm from an ES implementation, and how this knowledge was used to manage risks by utilizing an ES data transformation model from existing literature. Findings indicate that the ES data transformation process resulted from knowledge-leveraging actions at both executive and operational levels. At the executive level, the use of business intelligence module in conjunction with cascades of balanced scorecards helped in assessing progress for achieving goals, and translated decisions into risk-eliminating actions at the operational level. An initial technology-push approach assisted in creating semantically rich representative process models by simulating risk scenarios, leading to a strategy-pull approach for deploying business strategies and decisions. A value assessment strategic model articulates the knowledge-leveraging processes combining human skills with ES tools to optimize enterprise risks.

Introduction

Enterprise risk management (ERM) is a vital concept in the current, volatile business environment that supports use of tools and processes, which provide naturally to data analysis for better managing risks and identifying opportunities. Organizational risks may include disrupted production in supply chain operations due to erroneous information, context-insensitive analyses, or a human error from oversight in task breakdowns. With the advancement of information technology (IT), software tools in ERM decision-support systems can be used to elicit data attributes into semantically rich representative process models by simulating risk scenarios, which assist in establishing knowledgeable decisions [1], [2]. Since 1990s many organizations have implemented enterprise systems (ESs), also known as enterprise resource planning systems, to achieve integration of business activities and enhance organizational effectiveness [3]. ESs are packaged software applications offered by firms such as SAP and Oracle which allow organizations to procure them off-the-shelf and align them to their individual needs [4]. An ES usually supports in optimizing business operations and provides context-rich information to facilitate decision making and mitigate risks [5]. ESs enhance knowledge sharing within and across organizations assisting firms to access multiple viewpoints on long-term and short-term objectives optimally utilizing organizational resources. A successfully integrated ES can enhance operational efficiency by supporting a firm's business processes as well as create competitive advantages by enabling innovative practices [6]. Complex business processes can be mapped by decoupling different functional workflows to identify critical activities, which require monitoring to achieve business benefits. There have been quite a few studies to understand the critical success factors for ES implementations [7], [8], [9] as well as many studies to establish the business benefits organizations obtain from ES implementations [10], [11], [12]. However, there has been little research to understand how ES data are transformed into knowledge for decision-making, and how this leads to the benefits that mitigate organizational risks. “Very few studies have gone beyond looking at implementation to tackle issues related to longer-term usage and the impacts of these technologies on organizations” [13, p. 152]. This makes it difficult to draw explicit conclusions on the impact of ES on organizational performance [14].

The purpose of this study is to explore (1) how ES data are transformed into knowledge and (2) how this knowledge is used to realize risk-mitigating benefits. An ES data transformation model from existing literature is utilized to gain insights from an organizational perspective via a case study in a hi-tech manufacturing company. This company has deployed an ES for more than five years and so is in a mature stage of implementation. The results of this study, especially the insight gained from user–practitioners through the application of the transformational model, are useful to both academia and industry practitioners, which is a distinctive contribution of this study.

The paper is organized as follows. This first section introduced the focus of this paper with a brief background on enterprise risk management and enterprise systems. The next four sections review related literature, beginning with an analysis of ESs and risk management benefits followed by associated technologies such as knowledge management and business intelligence. The section concludes with the application of a model for ES data transformation into knowledge and results. The sixth section outlines the research methodology. The seventh section presents the empirical findings from a case study that applied the model in a hi-tech manufacturing organization. The eighth section discusses the results of the findings. Finally, the results are summarized and suggestions for future research are offered.

Section snippets

Enterprise systems and risk management benefits

ESs amalgamate a wider array of business automation tasks such as inventory management, sales order processing, financial accounting, production scheduling, materials planning, and supply chain management. These systems create data sources which provide valuable information to meet an organization's business intelligence and knowledge requirements [15]. Having shared access to complete and accurate data, which can be tracked down to functional tasks, provides visibility and improves risk

Knowledge management

Knowledge management (KM) theories have emerged from a broad range of research fields such as sciences, economics and management. The diversity of these fields enables knowledge to be abstracted at different functional levels, allowing many viewpoints for understanding organizational knowledge, which is key for maximizing risk-mitigating benefits. KM is “the ability to selectively capture, archive, and access the best practices of work-related knowledge and decision making from employees and

Business intelligence

For driving business collaboration, organizations necessitate data management and the intelligence to evaluate specific data objects and establish informed decisions. Business intelligence (BI) is an activity that supports transformation of data into valuable information, insightful analysis leading to knowledgeable action. Over the years, BI has become a top “business priority” against being a top “technology priority” earlier [29, p. 1530]. It is described as a rational approach to

A transformation model for assessment of benefits from an ES implementation

A model conceptualized by Davenport for evaluating the process of ES data transformation into knowledge and results is shown in Fig. 1. The model comprises three major stages. The first is establishing the context. This includes the pre-existing factors – strategic, organizational and cultural, skills and knowledge, data, and technology – that must be present to achieve successful transformation of ES data into knowledge and results. The second stage is the transformation of ES data into

Research methodology

In this research, the assessment of risk-mitigating benefits from an ES is conceptualized as a series of steps that begin with identification of risks and conclude with realization of benefits. The stages mirror the typical analytical and decision-making process. Using a qualitative research methodology, data were collected by way of semi-structured interviews with key respondents in an organization that has implemented an ES. A single case study has been used focusing on the deeper dynamics of

Case study findings

Cevon (a pseudonym) is a successful high-tech manufacturing business based on global positioning system (GPS) technology. Specifically, Cevon is involved in the design and manufacture of electronic devices used in marine applications, personal in-car navigation and wireless fleet tracking at their manufacturing plant in Auckland, New Zealand (NZ). Established in 1987, by 2002 Cevon had grown from seven employees to 250 with annual revenue of more than NZ$100 million. Since 1998 Cevon had been

Results and discussion

In answering the first research question, how are ES data transformed into knowledge in seeking risk-mitigating benefits from an ES, the extensive use of ES functionalities and business tools have emerged from this study. The major methods include representative models employing standard and custom ES reports, forms, and user-friendly interfaces for writing queries. Additionally, data warehouses are used when cross-functional data are brought in from various heterogeneous environments and the

Conclusions and future research directions

Several key findings have emerged from this study. The research has examined the effectiveness of ESs on organizational functions and processes for achieving risk-mitigating benefits. The study has provided an increased understanding of the various knowledge-leveraging processes organizations adopt in use of ES and its information for managing risks. The study highlights that results follow when risk-mitigating strategies are clearly articulated and defined. A value creation process is

Dr. Sanjay Mathrani is a Senior Lecturer in the School of Engineering and Advanced Technology at Massey University, Auckland, New Zealand. He holds degrees in Bachelor of Technology in Mechanical Engineering, Masters in Management Sciences, and a PhD in Information Technology from Massey University. He has more than twenty years of product development, manufacturing and global supply chain experience and has held senior positions in various multinationals namely Greaves, Alstom, and Navman. A

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    Dr. Sanjay Mathrani is a Senior Lecturer in the School of Engineering and Advanced Technology at Massey University, Auckland, New Zealand. He holds degrees in Bachelor of Technology in Mechanical Engineering, Masters in Management Sciences, and a PhD in Information Technology from Massey University. He has more than twenty years of product development, manufacturing and global supply chain experience and has held senior positions in various multinationals namely Greaves, Alstom, and Navman. A qualified auditor of quality management systems to ISO 9001:2000, he has assisted organizations in developing quality management practices and implementing process improvement strategies for ISO accreditation endeavors. He has been a practitioner of ERP and business intelligence systems in hi-tech engineering operations and is a consulting chartered professional engineer to New Zealand industry. His research interests are in information and knowledge management, project and product development, quality and manufacturing operations, and enterprise service-oriented architectures. He has published several papers in international journals, books, and conferences and is an invited speaker at international universities.

    Anuradha Mathrani is a Senior Lecturer in Information Technology within the School of Engineering and Advanced Technology, Massey University, Auckland, New Zealand. She holds an Engineering degree in Electronics and Telecommunications, a Master's degree in Management Sciences, and a PhD in Information Technology. Anuradha has a rich industry experience, having worked in research and development laboratories of reputed multinational organizations before joining academia. Her research interests include software application lifecycle management, software assessment and governance methods, quality and reliability measurements, and development practices in distributed software teams. Anuradha has published several research papers in refereed journals, book chapters, and national/international conferences.

    Note from Editors: This paper originally formed part of a Special Issue on “Optimizing Enterprise Risk Management in Industry”, guest-edited by Desheng Dash Wu (University of Toronto), David L. Olson (University of Nebraska) and John Birge (University of Chicago). Due to the lack of sufficient papers, it was decided to cancel/postpone this special issue. The Editors acknowledge the excellent work done by the guest editors in sourcing, reviewing and editing this paper.

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