Multi-criteria ABC analysis using artificial-intelligence-based classification techniques

Yu, Min-Chun

doi:10.1016/j.eswa.2010.08.127

Cited by 111 publications

(45 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Techniques Research Single-criteria Traditional ABC Canen andGalvão (1980), Chrisman (1985) , Portougal (2002), Onwubolu and Dube (2006) Multi-criteria Analytic hierarchy process (AHP) Gajpal, Ganesh, and Rajendran (1994), Nan-fang and Xue (2004), Molenaers, Baets, Pintelon, and Waeyenbergh (2012), Lolli, Ishizaka, and Gamberini (2014), Munna, Rahman, and Roney (2015), Arikan and Citak (2017) Cluster analysis Cohen (1990), Canetta*, Cheikhrouhou, andGlardon (2005), Decision tree Huiskonen (2001), Boylan, Syntetos, and Karakostas (2008) Distance modeling Chen, Li, Kilgour, and Hipel (2008), Keshavarz Ghorabaee, Zavadskas, Olfat, and Turskis (2015) Genetic algorithm Guvenir andErel (1998) Graphical matrix D'Alessandro andBaveja (2000), Ghobbar and Friend (2002), Syntetos, Boylan, and Croston (2005) Neural network Partovi and Anandarajan (2002), Yu (2011), Kabir and Akhtar Hasin (2013) Optimization models Ramanathan (2006), Ng (2007), Zhou and Fan (2007), Hadi-Vencheh (2010), Hatefi and Torabi (2010), Torabi et al (2012), Park et al (2014) …”

Section: Methodsmentioning

confidence: 99%

Statistical analysis of multi-criteria inventory classification models in the presence of forecast upsides

Iqbal

Malzahn

Whitman

2017

Production & Manufacturing Research

View full text Add to dashboard Cite

Companies may receive forecast upsides that can undermine their ability to support customer demand on time. Therefore, it is critical to include forecast upsides when performing comparative analysis of inventory classification models. This study focuses on this subject. The study includes statistical methods and sensitivity analysis to determine the performance of which MCIC model is statistically significant with respect to inventory and customer orders fill rate. Results show that the PBB-model outperforms other models when forecast upside is present and the result is statistically significant. On the other hand, when no forecast upside is present, the R-model, which does not use descending ranking criteria, outperforms other models, and the difference is statistically significant. We also find that adding descending ranking criteria to the R-model and ZF-model does not improve their Service-Cost Performance Index.

show abstract

Section: Methodsmentioning

confidence: 99%

Statistical analysis of multi-criteria inventory classification models in the presence of forecast upsides

Iqbal

Malzahn

Whitman

2017

Production & Manufacturing Research

View full text Add to dashboard Cite

show abstract

“…The process of ABC analysis classifies inventory items into A, B or C categories based on so-called annual dollar usage. Annual dollar usage is calculated by multiplying the dollar value per unit by the annual usage rate [38]. Inventory items are then arranged according to the descending order of their annual dollar usage.…”

Section: Abc Analysis Methodsmentioning

confidence: 99%

Importance Degree Research of Safety Risk Management Processes of Urban Rail Transit Based on Text Mining Method

Wang

et al. 2018

Information

View full text Add to dashboard Cite

China's urban rail transit (URT) construction is coming into the stage of rapid development under the guidance of national policies. However, the URT construction projects belong to high-risk projects and construction safety accidents occur frequently. Presently, safety risk management is in continuous development. Unfortunately, due to risk data deficiencies and lack of relationship between participants and safety risk factors, most of the research results cannot be well applied to URT projects. To overcome the limits, this paper has applied the text mining method into safety risk analysis. Through word frequency analysis and cluster analysis, 15 safety risk factors and 3 participants are identified from 156 accident reports. In addition, the accident descriptive model has been established, which is composed of indirect safety risk factors (management defects), direct safety risk factors and participants. In this model, each accident is the standardized description of the corresponding accident information. This is useful for risk data accumulation and analysis. Then the network structure analysis and risk assessment methods are utilized to make clear 63 relationships among participants, management defects and direct safety risk factors. Subsequently, the risk value of each relationship is evaluated. These safety risk information is integrated into the accident descriptive model by using accident points. Finally, ABC analysis which is a popular and effective method used to classify items into specific categories that can be managed and controlled separately is used to analyze the safety risk management's core process(A), important process(B) and general process(C) in the accident descriptive model. The research results show that the constructor should pay attention to construction coordination, safety specifications, safety measures and personnel education, the supervisor should attach importance to timely communication, the monitoring unit should pay attention to advanced forecast and dynamic control. The main research contributions are as follows: (1) A method of obtaining risk data from unstructured content has been provided; (2) The accident descriptive model could be utilized for risk data continuous accumulation; (3) The emphases of URT construction safety risk management are made clear.

show abstract

“…Single-criteria Traditional ABC [14][15][16][17][18] Multicriteria Analytic hierarchy process (AHP) [19,20] Cluster analysis [21,22] Decision tree [23,24] Distance modeling [25] Genetic algorithm [6] Graphical matrix [26][27][28] Neural network [2,29,30] Optimization models [1,3,[31][32][33][34][35][36][37] optimization models will highlight the shortcomings of using one model over other models. This will help a decision-maker in selecting the right model for the data.…”

Section: Methodsmentioning

confidence: 99%

Selecting a Multicriteria Inventory Classification Model to Improve Customer Order Fill Rate

Iqbal

Malzahn

Whitman

2017

Advances in Decision Sciences

View full text Add to dashboard Cite

Multicriteria models have been proposed for inventory classification in previous studies. However, it is important to make a decision when a particular multicriteria inventory classification model should be preferred over other models and also if the highest performing model remains the highest performing at all times. Companies always look for ways to improve customer order fulfillment process. This paper shows how better inventory classification can improve customer order fill rate in variable settings. The method to compare the inventory classification models with regard to improving customer order fill rate is proposed. The cut-off point is calculated which indicates when a model currently in use should be dropped in favor of another model to increase revenue by filling more orders. Sensitivity analysis is also performed to determine how holding cost and demand uncertainty affect the performance metric. Finally, regression analysis and hypothesis testing inform the decision-maker of how a model's performance differs from other models at various values of holding cost and standard deviation of demand.

show abstract

Multi-criteria ABC analysis using artificial-intelligence-based classification techniques

Cited by 111 publications

References 13 publications

Statistical analysis of multi-criteria inventory classification models in the presence of forecast upsides

Statistical analysis of multi-criteria inventory classification models in the presence of forecast upsides

Importance Degree Research of Safety Risk Management Processes of Urban Rail Transit Based on Text Mining Method

Selecting a Multicriteria Inventory Classification Model to Improve Customer Order Fill Rate

Contact Info

Product

Resources

About