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Gives an overview of quantitative model‐based research in operations management, focusing on research methodology. Distinguishes between empirical and axiomatic research, and furthermore between descriptive and normative research. Presents guidelines for doing quantitative model‐based research in operations management. In constructing arguments, builds on learnings from operations research and operations management research from the past decades and on research from a selected number of other academic disciplines. Concludes that the methodology of quantitative model‐driven empirical research offers a great opportunity for operations management researchers to further advance theory.

The purpose of this paper is to develop a new vibration probe sensor for measurement of particle mass flow rate in gas–solid two phase flow.

A new vibration probe sensor based on polyvinylidene fluoride (PVDF) piezoelectric film is designed. The particle impact model according to Hertz contacting theory is presented. The average amplitude, standard deviation and spectral peak at the natural frequency of the probe (21.2 kHz) of the signals acquired through experiments are chosen as characteristic quantities for further analysis.

Through experimental study of relation between three characteristic quantities and the mass flow rate and air flow velocity, a good regularity is found in the average amplitude and the spectral peaks at natural frequency of the probe. According to the particle impact model, the structure of quantitative model is built and parameters of two models are calculated from experimental data. Additionally, tests are made to estimate mass flow rate. The average errors are 5.85 and 4.26 per cent, while the maximum errors are 10.81 and 8.65 per cent. The spectral peak at natural frequency of the probe is more applicable for mass flow rate measurement.

The sensor designed and the quantitative models established may be used in dilute phase pneumatic conveying lines of coal-fired power plants, cement manufacturing facilities and so on.

First, the new sensor is designed and the quantitative models are established. Second, the spectral peak at natural frequency of the probe is found that can be used for measurement of mass flow rate.

Products that have a short selling season face high uncertainty in demand. Retailers who sell such products therefore find the task of pricing and inventory challenging. Many retailers consider making these decisions as an art form and do not use quantitative models that are developed by researchers. Describes how retailers typically make pricing and inventory decisions and also reviews quantitative models that have been developed by researchers to improve on one or more of these decisions. A classification of these models is developed and how they can assist the retailer is explained. A simple explanation of two mathematical tools, Bayesian updating of information and dynamic programming, which are commonly mentioned in the literature are also given.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

The bid/no-bid decision is critical to the success of construction contractors. The factors affecting the bid/no-bid decision are either qualitative or quantitative. Previous studies on modeling the bidding decision have not extensively focused on distinguishing qualitative and quantitative factors. Thus, the purpose of this paper is to improve the bidding decision in construction projects by developing tools that consider both qualitative and quantitative factors affecting the bidding decision.

This study proposes a mixed qualitative-quantitative approach to deal with both qualitative and quantitative factors. The mixed qualitative-quantitative approach is developed by combining a rule-based expert system and fuzzy-based expert system. The rule-based expert system is used to evaluate the project based on qualitative factors and the fuzzy expert system is used to evaluate the project based on the quantitative factors in order to reach the comprehensive bid/no-bid decision.

Three real bidding projects are used to investigate the applicability and functionality of the proposed mixed approach and are tested with experts of a construction company in Alberta, Canada. The results demonstrate that the mixed approach provides a more reliable, accurate and practical tool that can assist decision-makers involved in the bid/no-bid decision.

This study contributes theoretically to the body of knowledge by (1) proposing a novel approach capable of modeling all types of factors (either qualitative or quantitative) affecting the bidding decision, and (2) providing means to acquire, store and reuse expert knowledge. Practical contribution of this paper is to provide decision-makers with a comprehensive model that mimics the decision-making process and stores experts' knowledge in the form of rules. Therefore, the model reduces the administrative burden on the decision-makers, saves time and effort and reduces bias and human errors during the bidding process.

The purpose of this paper is to develop a quantitative model of problem solving performance of students in the computer-based mathematics learning environment.

Regularized logistic regression was used to create a quantitative model of problem solving performance of students that predicts whether students can solve a mathematics problem correctly based on how well they solved other problems in the past. The usefulness of the model was evaluated by comparing the predicted probability of correct problem solving to the actual problem solving performance on the data set that was not used in the model building process.

The regularized logistic regression model showed a better predictive power than the standard Bayesian Knowledge Tracing model, the most frequently used quantitative model of student learning in the Educational Data Mining research.

Providing instructional scaffolding is critical in order to facilitate student learning. However, most computer-based learning environments use heuristics or rely on the discretion of students when they determine whether instructional scaffolding needs be provided. The predictive model of problem solving performance of students can be used as a quantitative guideline that can help make a better decision on when to provide instructional supports and guidance in the computer-based learning environment, which can potentially maximize the learning outcome of students.

Time and cost are usually critical to construction clients. Given the many contributory factors, improved quantitative models of time and cost may help clients to predict project outcomes at the outset, and also at different stages of the project life span. These can also help to compare deviations in significant contributory factors, and to suggest corrective actions. Multiple linear regression (MLR) and artificial neural networks (ANN) were applied in developing such quantitative models in a research project based in Hong Kong. A comparative study indicated that ANN had better prediction capabilities than MLR by itself. Significant factors identified through quantitative models developed, indicated that time over‐run levels were mainly governed by non‐procurement related factors (e.g. project characteristics and client/client representative characteristics), while cost over‐run levels were significantly influenced by both procurement and non‐procurement related factors (e.g. project characteristics, client/client representative characteristics and contractual payment modalities). A parallel approach yielded interesting comparisons of the variations of mean time and cost over‐runs, when comparing groups of projects using different procurement sub‐systems, from the Hong Kong sample.

Quantitative modelling seems admirably suited to help managers in their strategic decision making on operations management issues, but in practice models are rarely used for this purpose. Investigates the reasons why, based on a detailed cross‐case analysis of six cases of modelling‐supported strategic decision making. In several of these cases, effective strategic decision making was achieved despite unfavourable technical contingencies, such as a lack of quantitative data, or low tangibility of the issue at stake. This suggests that such technical conditions cannot be crucial for effective model‐based support. However, no case was found where good overall results were achieved under adverse organizational conditions, such as low quality of communication between stakeholders during the modelling/decision‐making process and low ownership with these stakeholders for the resulting model and its implications. This suggests that such organizational contingencies are indeed crucial for effective model‐based support. The modelling method described achieved good communication and ownership by operating in a process‐oriented consulting mode, where client participation in group model‐building sessions played a central role.

This paper presents a comparative study of three well‐established building maintenance forecasting models in conjunction with a quantitative model which has been developed recently. After a brief introduction of each method, data collected from a large building survey were mapped into four distinctive methods and comparison was carried out in terms of priorities that these four methods adopt for future maintenance work. An analysis of responses to a questionnaire including 100 companies and individuals involved in maintenance and facilities management work (including quantity surveyors’ organisations) revealed that 100 percent of the public sector organisations use maintenance assessment methods for their prioritisation of maintenance management work. It also revealed that 92 percent of the private sector and 95 percent of all the organisations use condition based maintenance assessment methods for the prioritisation of maintenance operations. Results of this questionnaire have been used as introductory material to support the necessity for this piece of research.

To enable quantitative and qualitative modelling of information systems security management that takes into account technology and human factor.

The approach is based on systems dynamics and it is done in two phases. In the first phase two basic qualitative models are developed, while in the second phase a possibility to further develop them into quantitative models is studied.

Appropriate approach to IS security management requires addressing “hard” and “soft” factors. Further, to enable quantitative study of such systems, which are highly non‐linear, exact analytical (mathematically rigorous) treatment is close to impossible. Thus, computer simulations have to be used. One appropriate methodological answer to the above requirements is systems (business) dynamics.

Research limitations are partially related to system dynamics, which operates on an aggregates level. This prevents or makes harder study of phenomena at the micro level, from where the above‐mentioned aggregates emerge. Further, many sub‐areas need further standardisation to enable more realistic simulations – one such case is security policy standardisation and quantification. Similar holds true for threats/vulnerabilities and related taxonomies.

The research presents one of first steps in the direction that could provide quantitative models for effective IS security policy management in organisations.

The research presents two models, one for risk management and the other, which is a generic model that identifies basic variables that have to be addressed for IS security management. Further, findings can be used for security awareness courses.