Success Factors and Failure Causes in Projects: Analysis of Cluster Patterns Using Self-organizing Maps

Montequín, Vicente Rodríguez; Cousillas, S.M.; Álvarez, Viteri; Villanueva, J.

doi:10.1016/j.procs.2016.09.180

Cited by 21 publications

(31 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“… Contract specifications index includes the following subindices: Contract Price (Lopez & Salmeron, ; Montequin et al, ; Ngai & Wat, ): The higher the contract price, the higher the sensitivity and risk‐taking. Therefore, its insurance will be regarded as risk management to the employer and the contractor. The number of system users: Higher number of users indicates the larger size of system.…”

Section: System Overviewmentioning

confidence: 99%

“…More experienced contractors have higher chance to gain better positions in the market. Like rank of the consultant/supervisor of the contract, high rank of contactor (in lower number ranking system) causes more assurance about correct and timely performance of the system, which lowers the need for contract insurance coverage. The number of similar products sold by the contractor within the past 3 years (Alami, ; Montequin et al, ; Ngai & Wat, ): A greater number of sold products signals less risk. Therefore, it is evident that there is less need for insurance coverage of the contract. Positive commercial records of the contractor (by giving good performance certificate; Lipińska et al, ; Montequin et al, ): Historical records and variety of the contractor's contracts do not necessarily indicate high quality of his performance.…”

Section: System Overviewmentioning

confidence: 99%

“…Subsequently, the system decides whether the insurance company should pay to each patient (Lipińska, Szydło, & Lipiński, 2009). A knowledge rule-based system helps improve decisions about life insurance with fuzzy processing by assessing risks (Montequin, Cousillas, Alvarez, & Villanueva, 2016). Another expert system is suggested for recognizing group violations in automotive insurance and its consequent researches (Šubelj, Furlan, & Bajec, 2011).…”

Section: Expert Systems In the Scm And Insurance Contractsmentioning

confidence: 99%

“…1. Contract Price (Lopez & Salmeron, 2014;Montequin et al, 2016;Ngai & Wat, 2003): The higher the contract price, the higher the sensitivity and risk-taking. Therefore, its insurance will be regarded as risk management to the employer and the contractor.…”

Section: Explaining Indices and Subindicesmentioning

confidence: 99%

“…3. The number of similar products sold by the contractor within the past 3 years (Alami, 2016;Montequin et al, 2016;Ngai & Wat, 2003): A greater number of sold products signals less risk. Therefore, it is evident that there is less need for insurance coverage of the contract.…”

Section: Explaining Indices and Subindicesmentioning

confidence: 99%

See 4 more Smart Citations

Implementing a fuzzy expert system for ensuring information technology supply chain

et al. 2018

View full text Add to dashboard Cite

In the business environment, information technology (IT) plays an important role for firms' performance. It provides information flow that makes the supply chain more robust and resilient without undermining its efficiency. Smart systems use artificial intelligence methods for solving problems and facilitating decision‐making through rule‐based deduction. Accordingly, these systems can present specialists' skills and simulate their thinking process. The primary goal of expert systems is to implement knowledge acquisition process by converting knowledge to wisdom. This process is vital for critical decision‐making regarding important issues such as determining necessities of a particular contract. Companies use professional liability insurance of the products and services to ensure the purchasers and prevent potential losses. Although this practice is highly prevalent, there is not any particular procedure for measuring necessities of contracts. The main purpose of this paper is to design a fuzzy expert system for measuring the necessities of professional contracts regarding insurance coverage and improve the supply chain management using IT. This system can measure and report these obligations, considering specifications of each project. Taking into perspective variety of professional services/products, we consider software as a type of professional contracts, extract its important indices and give it to the system as the input. After the necessary stages, the system produces a proper response and presents the generated response to the user. The software of this expert system is web based, and there are four operating layers in its architecture. We implemented this program in MS Visual Studio Framework with C#.NET programming language. Moreover, we implemented MS SQL‐Server Database Management.

show abstract