An empirical investigation into intelligent cost analysis in purchasing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Frank Bodendorf ◽  
Manuel Lutz ◽  
Stefan Michelberger ◽  
Joerg Franke
Keyword(s):  
Cost Analysis ◽  
Cost Estimation ◽  
Expert Knowledge ◽  
Added Value ◽  
Cost Calculation ◽  
Content Type ◽  
Intelligent Decision Support Systems ◽  
The One ◽  
Management Recommendations ◽  
Intelligent Decision Support

Purpose Cost transparency is of central importance to reach a consensus between supply chain partners. The purpose of this paper is to contribute to the instrument of cost analysis which supports the link between buyers and suppliers. Design/methodology/approach Based on a detailed literature review in the area of cost analysis and purchasing, intelligent decision support systems for cost estimation are identified. Subsequently, expert interviews are conducted to determine the application possibilities for managers. The application potential is derived from the synthesis of motivation, identified applications and challenges in the industry. Management recommendations are to be derived by bringing together scientific and practical approaches in the industry. Findings On the one hand, the results of this study show that machine learning (ML) is a complex technology that poses many challenges for cost and purchasing managers. On the other hand, ML methods, especially in combination with expert knowledge and other analytical methods, offer immense added value for cost analysis in purchasing. Originality/value Digital transformation allows to facilitate the cost calculation process in purchasing decisions. In this context, the application of ML approaches has gained increased attention. While such approaches can lead to high cost reductions on the side of both suppliers and buyers, an intelligent cost analysis is very demanding.

scholarly journals Adaptive decision support for suggesting a machine tool maintenance strategy

2018 ◽  
Vol 24 (3) ◽  
pp. 376-399 ◽  
Author(s):  
Abubaker Shagluf ◽  
Simon Parkinson ◽  
Andrew Peter Longstaff ◽  
Simon Fletcher
Keyword(s):  
Decision Support ◽  
Machine Tool ◽  
Cost Estimation ◽  
Expert Knowledge ◽  
Production Costs ◽  
Maintenance Cost ◽  
Maintenance Strategy ◽  
Content Type ◽  
Machine Tool Accuracy

Purpose The purpose of this paper is to produce a decision support aid for machine tool owners to utilise while deciding upon a maintenance strategy. Furthermore, the decision support tool is adaptive and capable of suggesting different strategies by monitoring for any change in machine tool manufacturing accuracy. Design/methodology/approach A maintenance cost estimation model is utilised within the research and development of this decision support system (DSS). An empirical-based methodology is pursued and validated through case study analysis. Findings A case study is provided where a schedule of preventative maintenance actions is produced to reduce the need for the future occurrences of reactive maintenance actions based on historical machine tool accuracy information. In the case study, a 28 per cent reduction in predicted accuracy-related expenditure is presented, equating to a saving of £14k per machine over a five year period. Research limitations/implications The emphasis on improving machine tool accuracy and reducing production costs is increasing. The presented research is pioneering in the development of a software-based tool to help reduce the requirement on domain-specific expert knowledge. Originality/value The paper presents an adaptive DSS to assist with maintenance strategy selection. This is the first of its kind and is able to suggest a preventative strategy for those undertaking only reactive maintenance. This is of value for both manufacturers and researchers alike. Manufacturers will benefit from reducing maintenance costs, and researchers will benefit from the development and application of a novel decision support technique.

Complex maintenance programs quantification (CMPQ) to better control production systems

2014 ◽  
Vol 25 (4) ◽  
pp. 491-509 ◽  
Author(s):  
Thomas Ruin ◽  
Eric Levrat ◽  
Benoît Iung ◽  
Antoine Despujols
Keyword(s):  
Expert Knowledge ◽  
Model Development ◽  
Added Value ◽  
Target System ◽  
System Modelling ◽  
Real Problem ◽  
Industrial Case Study ◽  
Content Type ◽  
Executable Model

Purpose – The purpose of this paper is to develop a methodology for supporting complex maintenance programs quantification (CMPQ) for industrial systems. The methodology is based on a generic formalization of static and behavioral expert knowledge both on the target system and on the maintenance one. The formalization is carried out first by means of system modelling language (SysML) diagrams to model knowledge concepts and second by the transformation of these concepts into Altarica data flow (ADF) language for developing stochastic simulation. Design/methodology/approach – An industrial case study (ARE system) proposed by the electricite de France (EDF) company is used initially to show a real problem statement on CMPQ. It allows highlighting key scientific issues considered as the basis for methodology development. Main issues are related to static and dynamic knowledge formalization justifying the choice of SysML and ADF languages. The added value of this methodology is finally shown on the same case study serving as benchmark. Findings – This paper demonstrates the suitability of using of SysML language for modelling the CMPQ knowledge and then of ADF language in building executable model implementing simulation as needed for assessing key performance indicators of CMPQ. ADF is based on formal mode automaton. Mapping rules are developed to ensure correspondence between the concepts of these two languages. Research limitations/implications – Additional industrial validations of the methodology should be performed to really evaluate its benefits. Practical implications – This work was made possible thanks to a partnership with the EDF Company (French energy supplier). The results are therefore directly usable at practical industrial levels. Originality/value – The CMPQ methodology proposed is fully generic leading to offering a library of atomic ADF components (COTS) which can be instantiated to develop executable model with regards to each specific application. It allows to favor reusability and makes easier the model development above all for a user who knows nothing about the language.

Expert knowledge elicitation for decision-making in home energy retrofits

2014 ◽  
Vol 32 (5) ◽  
pp. 377-395 ◽  
Author(s):  
Daniel Yaw Addai Duah ◽  
Kevin Ford ◽  
Matt Syal
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Expert Knowledge ◽  
Knowledge Elicitation ◽  
Content Type ◽  
Intelligent Decision Support System ◽  
Intelligent Decision ◽  
Intelligent Decision Support

Purpose – The purpose of this paper is to develop a knowledge elicitation strategy to elicit and compile home energy retrofit knowledge that can be incorporated into the development of an intelligent decision support system to help increase the uptake of home energy retrofits. Major problems accounting for low adoption rates despite well-established benefits are: lack of information or information in unsuitable and usable format for decision making by homeowners. Despite the important role of expert knowledge in developing such systems, its elicitation has been fraught with challenges. Design/methodology/approach – Using extensive literature review and a Delphi-dominated data collection technique, the relevant knowledge of 19 industry experts, selected based on previously developed determinants of expert knowledge and suitable for decision making was elicited and compiled. Boolean logic was used to model and represent such knowledge for use as an intelligent decision support system. Findings – A combination of comprehensive knowledge elicitor training, Delphi technique, semi-structured interview, and job shadowing is a good elicitation strategy. It encourages experts to describe their knowledge in a natural way, relate to specific problems, and reduces bias. Relevant and consensus-based expert knowledge can be incorporated into the development of an intelligent decision support system. Research limitations/implications – The consensus-based and relevant expert knowledge can assist homeowners with decision making and industry practitioners and academia with corroboration and enhancement of existing knowledge. The strategy contributes to solving the knowledge elicitation challenge. Originality/value – No previous study regarding a knowledge elicitation strategy for developing an intelligent decision support system for the energy retrofit industry exists.

Intelligent Decision-Support Systems for e-Tourism

2010 ◽  
Vol 2 (1) ◽  
pp. 36-48 ◽  
Author(s):  
Ángel García-Crespo ◽  
Ricardo Colomo-Palacios ◽  
Juan Miguel Gómez-Berbís ◽  
Javier Chamizo ◽  
Ismael Rivera
Keyword(s):  
Service Provision ◽  
Service Providers ◽  
Added Value ◽  
Semantic Technologies ◽  
Intelligent Decision ◽  
Combined Use ◽  
Intelligent Decision Support Systems ◽  
Destination Management Organizations ◽  
Intelligent Decision Support ◽  
Tourist Service

The Internet has disrupted traditional tourism in which a promising landscape of intelligent service provision has erupted by applying a new lattice of cutting-edge technologies. Thus, the different actors of the tourist services are in a new environment and they should operate in a coordinated manner to increase the value of tourism, to keep current tourists and attract new ones. One of the major players of this technological disruption are Semantic Technologies, which have profited from the combined use of pervasive elements and recommender systems to bring added value to tourist actors. Based on previous works, this paper presents a new module that enables SPETA II to act as a recommender not only for tourists, but to destination management organizations and tourist service providers. Searches, decisions and preferences of tourists are used to “pull” tourist service providers and destination management organizations to create and adapt services based on new recommendations.

An exploratory model of interventionist research to calculate costs and prices in small Brazilian manufacturers, combining training and intervention phases

2017 ◽  
Vol 9 (3) ◽  
pp. 315-332 ◽  
Author(s):  
José Carlos Tiomatsu Oyadomari ◽  
Renato Monteiro da Silva ◽  
Octávio Ribeiro de Mendonça Neto ◽  
Carlos Alberto Diehl
Keyword(s):  
Large Scale ◽  
Long Term Results ◽  
Future Research ◽  
Cost Calculation ◽  
Training Phase ◽  
Manufacturing Companies ◽  
Content Type ◽  
Implementation Phase ◽  
Two Phases ◽  
The One

Purpose This paper aims to propose an interventionist research model for cost measurement in small manufacturing companies. Design/methodology/approach The study was based on an interventionist model that consisted of two phases – training and intervention. The innovative model used in the study combined Labro and Tuomela’s (2003) framework with the socialization, externalization, combination and internalization model developed by Nonaka et al. (2001), and it was subsequently applied to two Brazilian manufacturing companies. Findings The main findings were as follows: the training phase is the one that generated the greatest impact on the cost calculation; competitors should not be invited to participate in the same program; it is necessary for the researchers to have professional experience of the subject being investigated and to have experience of micro and small enterprises; the training phase must be presented using appropriate language; and a better understanding of the costs can increase entrepreneurs’ confidence when negotiating prices with clients. Research limitations/implications The main limitation was the small number of companies that were included in the study. Future research could involve longitudinal studies to evaluate the long-term results of interventionist studies. Practical implications The study showed that even small business owners can implement costing techniques, but that this requires the development of an environment of knowledge creation, followed by an implementation phase. The model can be replicated on a large scale, with affordable costs. Social implications Improving the performance of small and medium-sized enterprises, which are high employers, with low implementation cost is a demand of society. Originality/value The model proved to be valid, and it could easily be replicated on a larger scale; the study therefore helps to demonstrate the benefits of interventionist research.

Intelligent Decision-Support Systems for e-Tourism

2012 ◽  
pp. 37-50
Author(s):  
Ángel García-Crespo ◽  
Ricardo Colomo-Palacios ◽  
Juan Miguel Gómez-Berbís ◽  
Javier Chamizo ◽  
Ismael Rivera
Keyword(s):  
Service Providers ◽  
Added Value ◽  
The Internet ◽  
Semantic Technologies ◽  
Intelligent Decision ◽  
Combined Use ◽  
Intelligent Decision Support Systems ◽  
Destination Management Organizations ◽  
Intelligent Decision Support ◽  
Tourist Service

The Internet has disrupted traditional tourism in which a promising landscape of intelligent service provision has erupted by applying a new lattice of cutting-edge technologies. Thus, the different actors of the tourist services are in a new environment and they should operate in a coordinated manner to increase the value of tourism, to keep current tourists and attract new ones. One of the major players of this technological disruption are Semantic Technologies, which have profited from the combined use of pervasive elements and recommender systems to bring added value to tourist actors. Based on previous works, this paper presents a new module that enables SPETA II to act as a recommender not only for tourists, but to destination management organizations and tourist service providers. Searches, decisions and preferences of tourists are used to “pull” tourist service providers and destination management organizations to create and adapt services based on new recommendations.

How Phillips saw the light

2020 ◽  
Vol 36 (8) ◽  
pp. 29-31
Keyword(s):  
Design Methodology ◽  
Reading Time ◽  
Daily Basis ◽  
Business World ◽  
Content Type ◽  
Freak Show ◽  
The World ◽  
Pertinent Information ◽  
The One ◽  
Practical Implications

Purpose Reviews the latest management developments across the globe and pinpoints practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings The problem with developing a reputation of being something of an oracle in the business world is that all of a sudden, everyone expects you to pull off the trick of interpreting the future on a daily basis. Like a freak show circus act or one-hit wonder pop singer, people expect you to perform when they see you, and they expect you to perform the thing that made you famous, even if it is the one thing in the world you don’t want to do. And when you fail to deliver on these heightened expectations, you are dismissed as a one trick pony, however good that trick is in the first place. Originality/value The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

PROMETHEE-based ranking of project managers based on the five personality traits

Kybernetes ◽  
2019 ◽  
Vol 49 (4) ◽  
pp. 1083-1102
Author(s):  
Georgios N. Aretoulis ◽  
Jason Papathanasiou ◽  
Fani Antoniou
Keyword(s):  
Personality Traits ◽  
Questionnaire Survey ◽  
Multicriteria Decision Making ◽  
Project Managers ◽  
Preference Ranking ◽  
Infrastructure Projects ◽  
Public Infrastructure ◽  
Content Type ◽  
The One ◽  
Selection Of

Purpose This paper aims to rank and identify the most efficient project managers (PMs) based on personality traits, using Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) methodology. Design/methodology/approach The proposed methodology relies on the five personality traits. These were used as the selection criteria. A questionnaire survey among 82 experienced engineers was used to estimate the required weights per personality trait. A second two-part questionnaire survey aimed at recording the PMs profile and assess the performance of personality traits per PM. PMs with the most years of experience are selected to be ranked through Visual PROMETHEE. Findings The findings suggest that a competent PM is the one that scores low on the “Neuroticism” trait and high especially on the “Conscientiousness” trait. Research limitations/implications The research applied a psychometric test specifically designed for Greek people. Furthermore, the proposed methodology is based on the personality characteristics to rank the PMs and does not consider the technical skills. Furthermore, the type of project is not considered in the process of ranking PMs. Practical implications The findings could contribute in the selection of the best PM that maximizes the project team’s performance. Social implications Improved project team communication and collaboration leading to improved project performance through better communication and collaboration. This is an additional benefit for the society, especially in the delivery of public infrastructure projects. A lot of public infrastructure projects deviate largely as far as cost and schedule is concerned and this is an additional burden for public and society. Proper project management through efficient PMs would save people’s money and time. Originality/value Identification of the best PMbased on a combination of multicriteria decision-making and psychometric tests, which focus on personality traits.

A new approach to bracket prediction in the NCAA Men’s Basketball Tournament based on a dual-proportion likelihood

2015 ◽  
Vol 11 (1) ◽  
Author(s):  
Ajay Andrew Gupta
Keyword(s):  
National Collegiate Athletic Association ◽  
Expert Knowledge ◽  
Probability Model ◽  
Expected Value ◽  
Division I ◽  
Prediction System ◽  
New Approach ◽  
Men's Basketball ◽  
Popular Interest ◽  
The One

AbstractThe widespread proliferation of and interest in bracket pools that accompany the National Collegiate Athletic Association Division I Men’s Basketball Tournament have created a need to produce a set of predicted winners for each tournament game by people without expert knowledge of college basketball. Previous research has addressed bracket prediction to some degree, but not nearly on the level of the popular interest in the topic. This paper reviews relevant previous research, and then introduces a rating system for teams using game data from that season prior to the tournament. The ratings from this system are used within a novel, four-predictor probability model to produce sets of bracket predictions for each tournament from 2009 to 2014. This dual-proportion probability model is built around the constraint of two teams with a combined 100% probability of winning a given game. This paper also performs Monte Carlo simulation to investigate whether modifications are necessary from an expected value-based prediction system such as the one introduced in the paper, in order to have the maximum bracket score within a defined group. The findings are that selecting one high-probability “upset” team for one to three late rounds games is likely to outperform other strategies, including one with no modifications to the expected value, as long as the upset choice overlaps a large minority of competing brackets while leaving the bracket some distinguishing characteristics in late rounds.

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