ORCID Profile
0000-0001-9109-1929
Current Organisations
Torrens University Australia
,
Utilstra
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Publisher: SAGE Publications
Date: 03-2019
Abstract: The clear majority of pre-existing work in the published domain of competitive bidding requires large s le sizes for reliable econometric, probabilistic or game-theoretic modelling techniques. Such unrealistic large data requirements have prevented the successful application of bid modelling in managerial practice. This article presents a new predictive analytics method for very small s les of historical bidding data. Requiring as few as nine competitive bid prices for a group of pooled/aggregated competitors over a 30-month period is the standout differentiator of this research from any previously published research. This minimizes the demands on competitive intelligence and, therefore, realistically enables its application in the real world of practice. Maximum likelihood estimations are used to evaluate two new, revolutionary bid strategies against a range of evaluation criteria, taking into account the pricing judgements made by competitors, including a degree of competitive reaction among them. Using off-the-shelf analytics software, a case study of a bidder from the telecommunications infrastructure sector demonstrates how commercial outcomes can be improved substantially: A 400 per cent improvement in win ratio, an 86 per cent increase in contribution margin and 76 per cent revenue growth. In addition, the difference between the submitted bids and the lowest-priced competing bids (which is an opportunity cost, sometimes referred to as the ‘spread’ or ‘money left on the table’), has been reduced to 2 per cent on a total revenue of US$210 million.
Publisher: Elsevier BV
Date: 10-2022
Publisher: Torrens University Australia
Date: 2021
DOI: 10.25905/14751708.V2
Publisher: Inderscience Publishers
Date: 2020
Publisher: Edward Elgar Publishing
Date: 20-10-2023
Publisher: Edward Elgar Publishing
Date: 20-10-2023
Publisher: Elsevier BV
Date: 03-2023
Publisher: Wiley
Date: 17-10-0003
DOI: 10.1002/JSC.2525
Abstract: The banking, financial services, and insurance (BFSI) sector is one of the earliest and most prominent adopters of artificial intelligence (AI). However, academic research substantially lags behind the adoption of AI in practice. At the beginning of this century, AI research has been centered on the sector's credit risk. In the 2010s decade, expert systems were increasingly replaced by data‐driven, “algorithmic” AI. Big data enjoyed much hype in that decade, which diminished later mostly due to unsuccessful implementations. Much published research on big data actually relates to machine and deep learning but not to big data per se. These terms are often found to be conflated in research and practice. The insurance sector is substantially underrepresented in published AI research, and current research is dominated by banking and investments. Governance frameworks for “responsible AI” (RAI) are yet to be incorporated into practice by fintech companies as well as incumbent organizations. RAI is a particular issue for decentralized finance (DeFi). The most successful implementations of AI in BFSI practice, as well as dominant academic research areas, are in investments, securities, market making, customer relationships, lending, risk management, and compliance.
Publisher: Wiley
Date: 05-2021
DOI: 10.1002/JSC.2404
Publisher: Edward Elgar Publishing
Date: 20-10-2023
No related grants have been discovered for Heinz Herrmann.