Smart Buyer: A Bayesian Network modelling approach for measuring and improving procurement performance in organisations

Abolbashari, Mohammad Hassan; Chang, Elizabeth; Hussain, Omar Khadeer; Saberi, Morteza

doi:10.1016/j.knosys.2017.11.032

Cited by 28 publications

(40 citation statements)

References 68 publications

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“…{2, 3,…n} and returning to the current state. These possible transitions motivate the application of methods such as a Bayesian network (Abolbashari, Chang, Hussain, & Saberi, 2018), which can make the model more interesting. The SMCDM method will reach its maturity with the result of future studies that consider wider applications and integrations with other tolls and techniques.…”

Section: Nos Cst =∏ (15)mentioning

confidence: 99%

The stratified multi-criteria decision-making method

Asadabadi

2018

Knowledge-Based Systems

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Section: Nos Cst =∏ (15)mentioning

confidence: 99%

The stratified multi-criteria decision-making method

Asadabadi

2018

Knowledge-Based Systems

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“…Besides, an important and unique advantage of BNs over other methods, including SEM and ANN, is its capability for belief updating by introducing new evidence, which is a feature that helps make decisions in contradictory situations. In fact, BN models can also be utilized as decision-making tools, and it has been shown that BN models are more powerful than conventional decision-making approaches such as the analytic hierarchy process and analytic network process [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting needlestick and sharps injuries and determining preventive strategies using a Bayesian network approach in Tehran, Iran

et al. 2018

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OBJECTIVESRecent studies have shown that the rate of needlestick and sharps injuries (NSIs) is unacceptably high in Iranian hospitals. The aim of the present study was to use a systematic approach to predict and reduce these injuries.METHODSThis cross-sectional study was conducted in 5 hospitals in Tehran, Iran. Eleven variables thought to affect NSIs were categorized based on the Human Factors Analysis and Classification System (HFACS) framework and modeled using a Bayesian network. A self-administered validated questionnaire was used to collect the required data. In total, 343 cases were used to train the model and 50 cases were used to test the model. Model performance was assessed using various indices. Finally, using predictive reasoning, several intervention strategies for reducing NSIs were recommended.RESULTSThe Bayesian network HFACS model was able to predict 86% of new cases correctly. The analyses showed that safety motivation and fatigue were the most important contributors to NSIs. Supervisors’ attitude toward safety and working hours per week were the most important factors in the unsafe supervision category. Management commitment and staffing were the most important organizational-level factors affecting NSIs. Finally, promising intervention strategies for reducing NSIs were identified and discussed.CONCLUSIONSTo reduce NSIs, both management commitment and sufficient staffing are necessary. Supervisors should encourage nurses to engage in safe behavior. Excessive working hours result in fatigue and increase the risk of NSIs.

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“…Challenges in purchasing performance measurement can be exposed by interviewing employees who are directly associated with purchasing activities [28]. Therefore, measuring purchasing performance captures a specific aspect of the company's performance [86]. To measure the purchasing performance, a smart buyer defines different key performance indicators (KPIs) such as purchasing cycle time, emergency purchasing, sustainable purchasing, and purchasing integrity and makes effort to improve the purchasing performance by managing, improving, and integrating the KPIs [86].…”

Section: Purchasing Performance Managementmentioning

confidence: 99%

Application of Industry 4.0 in the Procurement Processes of Supply Chains: A Systematic Literature Review

et al. 2021

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The fourth industrial revolution has significantly changed the traditional way of managing supply chains. The applications of Industry 4.0 (I4.0) technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI) in different processes of supply chains have assisted companies to improve their performance. Procurement can be considered a critical process in supply chain management since it can provide novel opportunities for supply chains to improve their efficiency and effectiveness. However, I4.0 applications can be costly and may not be reasonably affordable. Therefore, the benefits of implementing these technologies should be clarified for procurement managers before investing in the digitalization of the procurement process. Despite the importance of this issue, few papers have attempted to address the effects of I4.0 technologies and smart systems in procurement. To fill this gap, a Systematic Literature Review (SLR) on the applications of I4.0 technologies in procurement has been used in this study. By reviewing 70 papers through appropriate keywords, a conceptual framework is developed to classify different value propositions provided by the different applications of I4.0 technologies in procurement processes. Results reveal nine value propositions that can provide a better understanding for the procurement department to analyze the benefits of implementing the related I4.0 technologies in different activities. Finally, findings and future study opportunities are concluded.

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Smart Buyer: A Bayesian Network modelling approach for measuring and improving procurement performance in organisations

Cited by 28 publications

References 68 publications

The stratified multi-criteria decision-making method

The stratified multi-criteria decision-making method

Predicting needlestick and sharps injuries and determining preventive strategies using a Bayesian network approach in Tehran, Iran

Application of Industry 4.0 in the Procurement Processes of Supply Chains: A Systematic Literature Review

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