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Journal articles on the topic 'Classification of inventory'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

Ly, Rathanaksambath, and Morrakot Raweewan. "Flexible ABC Inventory Classification." KnE Life Sciences 4, no. 2 (March 1, 2018): 228. http://dx.doi.org/10.18502/kls.v4i2.1676.

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ABC inventory classification is a well-known approach to assign inventory item into A, B, and C groups based on their sales and usage volume. This helps inventory management become more efficient. Behind its advantage, it usually shows some problems with an inventory budget and warehouse space because the ABC assignment of SKUs are mad e without an inventory budget and space available involved. In this paper, the ABC group under restricted of an inventory budget and warehouse space to maximize the profit with optimal service level is presented. We establish this proposed model to enhance the existing ABC approach to be more applicable in real life, which has the limited inventory budget and warehouse space. Keywords: ABC Inventory Classification; Inventory Management

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May, Benjamin Isaac, Michael P. Atkinson, and Geraldo Ferrer. "Applying inventory classification to a large inventory management system." Journal of Operations and Supply Chain Management 10, no. 1 (June 28, 2017): 68. http://dx.doi.org/10.12660/joscmv10n1p68-86.

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3

Dhoka, Dinesh Kumar. "“XYZ” Inventory Classification & Challenges." IOSR Journal of Economics and Finance 2, no. 2 (2013): 23–26. http://dx.doi.org/10.9790/5933-0222326.

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4

Dhoka, Dinesh. "ABC Classification for Inventory Optimization." IOSR Journal of Business and Management 15, no. 1 (2013): 38–41. http://dx.doi.org/10.9790/487x-1513841.

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5

JESNESS, CARL F. "The Jesness Inventory Classification System." Criminal Justice and Behavior 15, no. 1 (March 1988): 78–91. http://dx.doi.org/10.1177/0093854888015001007.

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The Jesness Inventory Classification System is a simple, objective method for classifying offenders into one of nine subtypes. Having its origin in the I-level classification, the JI system has gone through a long evolution. The procedure for classification now requires only the responses to the 155-item Jesness Inventory, which can be hand-scored. Studies with both delinquents and nondelinquents have shown the classification to be comprehensive and valid.

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Flores, Benito E., David L. Olson, and V. K. Dorai. "Management of multicriteria inventory classification." Mathematical and Computer Modelling 16, no. 12 (December 1992): 71–82. http://dx.doi.org/10.1016/0895-7177(92)90021-c.

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7

Van Zandt, CDR Robert A., and Jorice Williams. "RESPONSE RESOURCE INVENTORY." International Oil Spill Conference Proceedings 1997, no. 1 (April 1, 1997): 909–10. http://dx.doi.org/10.7901/2169-3358-1997-1-909.

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ABSTRACT The Oil Pollution Act of 1990 mandated the creation of a national database of response resources. This has been accomplished with the creation of the Response Resource Inventory (RRI) located and maintained at the National Strike Force Coordination Center in Elizabeth City, North Carolina. Recent revisions to the Oil Spill Removal Organization (OSRO) classification program have mandated the use of the RRI by OSRO applicants. This poster session will describe how data are obtained from OSRO applicants, how they are manipulated to determine the OSRO's classifications, and how plan holders can access and use the RRI as a tool to conduct their own analysis of an OSRO's capacity to meet their specific planning requirements.

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Taminskas, Julius, Marijus Pileckas, Rasa Šimanauskienė, and Rita Linkevičienė. "Wetland classification and inventory in Lithuania." Baltica 25, no. 1 (June 12, 2012): 33–44. http://dx.doi.org/10.5200/baltica.2012.25.03.

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9

Massmann, Melina, Maurice Meyer, Maximilian Frank, Sebastian von Enzberg, Arno Kühn, and Roman Dumitrescu. "Method for data inventory and classification." Procedia CIRP 93 (2020): 234–39. http://dx.doi.org/10.1016/j.procir.2020.04.033.

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10

Finlayson, C. M., and A. G. Valk. "Wetland classification and inventory: A summary." Vegetatio 118, no. 1-2 (June 1995): 185–92. http://dx.doi.org/10.1007/bf00045199.

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11

Wikner, Joakim, and Eva Johansson. "Inventory classification based on decoupling points." Production & Manufacturing Research 3, no. 1 (January 2015): 218–35. http://dx.doi.org/10.1080/21693277.2015.1067845.

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12

Prasad, Sameer. "Classification of inventory models and systems." International Journal of Production Economics 34, no. 2 (March 1994): 209–22. http://dx.doi.org/10.1016/0925-5273(94)90037-x.

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13

Yung, Kai Leung, George To Sum Ho, Yuk Ming Tang, and Wai Hung Ip. "Inventory classification system in space mission component replenishment using multi-attribute fuzzy ABC classification." Industrial Management & Data Systems 121, no. 3 (February 1, 2021): 637–56. http://dx.doi.org/10.1108/imds-09-2020-0518.

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PurposeThis project attempts to present a space component inventory classification system for space inventory replenishment and management. The authors propose to adopt a classification system that can incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment.Design/methodology/approachA fuzzy-based approach with ABC classification is proposed to incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment of the soil preparation system (SOPSYS) which is used in grinding and sifting Phobos rocks to sub-millimeter size in the Phobos-Grunt space mission. An information system was developed using the existing platform and was used to support the key aspects in performing inventory classification and purchasing optimization.FindingsThe proposed classification system was found to be able to classify the inventory and optimize the purchasing decision efficiency. Based on the information provided from the system, implementation plans for the SOPSYS project and related space projects can be proposed.Research limitations/implicationsThe paper addresses one of the main difficulties in handling qualitative or quantitative classification criteria. The model can be implemented using mathematical calculation tools and integrated into the existing inventory management system. The proposed model has important implications in optimizing the purchasing decisions to shorten the research and development of other space instruments in space missions.Originality/valueInventory management in the manufacture of space instruments is one of the major problems due to the complexity of the manufacturing process and the large variety of items. The classification system can optimize purchasing decision-making in the inventory management process. It is also designed to be flexible and can be implemented for the manufacture of other space mission instruments.

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Mohammaditabar, Davood, Seyed Hassan Ghodsypour, and Chris O'Brien. "Inventory control system design by integrating inventory classification and policy selection." International Journal of Production Economics 140, no. 2 (December 2012): 655–59. http://dx.doi.org/10.1016/j.ijpe.2011.03.012.

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Bergeron, Jean-François, Jean-Pierre Saucier, Denis Robert, and André Robitaille. "Québec forest ecological classification program." Forestry Chronicle 68, no. 1 (February 1, 1992): 53–63. http://dx.doi.org/10.5558/tfc68053-1.

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In 1986, the ministère des Forêts du Québec instituted a provincial program to study forest ecosystems entitled the "Forest Ecological Classification (FEC) Program." Under this program, a multidisciplinary team was charged with conducting ecological surveys, analyzing and characterizing the variables of the physical environment, classifying vegetation and preparing integrated forest inventory maps. Their goal is to complete the ecological classification of the forests in all territories south of the 52nd parallel. To undertake such a vast project, it was necessary to prepare detailed methodological guides for data collection, data analysis and mapping. The following products are now available for many different ecological regions: classifications of forest types, toposequences, physiographic and surface deposit maps and integrated forest inventory maps. Multivariate analysis methods are used in analyzing ecological data; in this way, hierarchical classifications and ordinations can be used as the basis for identifying and describing forest types, vegetation-physical environment relationships and successional patterns. Such ecological classification products are an indispensable tool for forest managers and users. Key words: ecological classification, forest ecology, forest management, forest site classification, multivariate analysis, physical environment, Québec.

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16

Jadhav, Pragati, and Maheshwar Jaybhaye. "A Manufacturing Industry Case Study: ABC and HML Analysis for Inventory Management." International Journal of Research in Engineering, Science and Management 3, no. 9 (September 29, 2020): 146–49. http://dx.doi.org/10.47607/ijresm.2020.315.

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This article is a contextual analysis of ABC and HML examination of inventory control methods for an effective Inventory administration framework in the assembling business of India. The organization needs to control its inventory expense, so it utilized various kinds of inventory methods to control this. There are various strategies, for example, ABC, HML, VED, XYZ and S-O-S. In the ABC analysis, things are classifications in classifications A, B, C as per the utilization of the all-out expense. In the HML analysis, things are classifications in H, M, L classification as per the unit cost. Information assortment is chiefly a year through the head supervisor of the store and other work force associated with the Inventory control activity of the assembling business.

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17

Kabir, Golam. "Multiple Criteria Inventory Classification Under Fuzzy Environment." International Journal of Fuzzy System Applications 2, no. 4 (October 2012): 76–92. http://dx.doi.org/10.4018/ijfsa.2012100105.

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A systematic approach to the inventory control and classification may have significant influence on company competitiveness. In practice, all inventories cannot be controlled with equal attention. In order control the inventory items and to determine the suitable ordering policies for them, multiple criteria inventory classification is used. A systematic and logical approach is structured for multiple criteria inventory classification under fuzzy environment through integrating Fuzzy Delphi Method (FDM) with Fuzzy Analytic Hierarchy Process (FAHP). Fuzzy Delphi method is used to identify the important criteria and, Fuzzy AHP is used to determine the relative weights of the criteria, and to classify inventories into different categories. To accredit the proposed model, its implemented for the 351 raw materials of switch gear section of Energypac Engineering Limited (EEL), a large power engineering company of Bangladesh.

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18

Altay Guvenir, H., and Erdal Erel. "Multicriteria inventory classification using a genetic algorithm." European Journal of Operational Research 105, no. 1 (February 1998): 29–37. http://dx.doi.org/10.1016/s0377-2217(97)00039-8.

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19

Jesness, Carl F. "Validity of Jesness Inventory Classification with Nondelinquents." Educational and Psychological Measurement 46, no. 4 (December 1986): 947–61. http://dx.doi.org/10.1177/001316448604600414.

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20

Bala, Pradip, and M. Xavier. "CLASSIFICATION BASED FORECASTING FOR IMPROVED INVENTORY MANAGEMENT." International Journal of Strategic Management 13, no. 1 (March 1, 2013): 115–22. http://dx.doi.org/10.18374/ijsm-13-1.13.

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21

Gopal, B., and M. Sah. "Inventory and classification of wetlands in India." Vegetatio 118, no. 1-2 (June 1995): 39–48. http://dx.doi.org/10.1007/bf00045189.

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22

Rahim, Robbi, Ilka Zufria, Nuning Kurniasih, Muhammad Yasin Simargolang, Abdurrozzaq Hasibuan, Dian Utami Sutiksno, Ricardo Freedom Nanuru, Jusuf Nikolas Anamofa, Ansari Saleh Ahmar, and Achmad Daengs GS. "C4.5 Classification Data Mining for Inventory Control." International Journal of Engineering & Technology 7, no. 2.3 (March 8, 2018): 68. http://dx.doi.org/10.14419/ijet.v7i2.3.12618.

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Data Mining is a process of exploring against large data to find patterns in decision making. One of the techniques in decision-making is classification. Classification is a technique in data mining by applying decision tree method to form data, algorithm C4.5 is algorithm that can be used to classify data in tree form. The system has been built that shows the results of good performance and minimal error in view of the system that is able to distinguish the anomaly traffic with normal traffic. Data mining inventory system applications can facilitate the control of inventory in the company to reduce production costs.

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23

Ladhari, Talel, M. Zied Babai, and Imen Lajili. "Multi-criteria inventory classification: new consensual procedures." IMA Journal of Management Mathematics 27, no. 2 (March 12, 2015): 335–51. http://dx.doi.org/10.1093/imaman/dpv003.

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24

Teunter, Ruud H., M. Zied Babai, and Aris A. Syntetos. "ABC Classification: Service Levels and Inventory Costs." Production and Operations Management 19, no. 3 (November 11, 2009): 343–52. http://dx.doi.org/10.1111/j.1937-5956.2009.01098.x.

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25

Li, Zhi, Xunbo Wu, Fan Liu, Yelin Fu, and Ke Chen. "Multicriteria ABC inventory classification using acceptability analysis." International Transactions in Operational Research 26, no. 6 (May 15, 2017): 2494–507. http://dx.doi.org/10.1111/itor.12412.

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26

Rezaei, Jafar, and Negin Salimi. "Optimal ABC inventory classification using interval programming." International Journal of Systems Science 46, no. 11 (October 16, 2013): 1944–52. http://dx.doi.org/10.1080/00207721.2013.843215.

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27

Jalali, Hamed, and Inneke Van Nieuwenhuyse. "Simulation optimization in inventory replenishment: a classification." IIE Transactions 47, no. 11 (April 15, 2015): 1217–35. http://dx.doi.org/10.1080/0740817x.2015.1019162.

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28

Soylu, Banu, and Bahar Akyol. "Multi-criteria inventory classification with reference items." Computers & Industrial Engineering 69 (March 2014): 12–20. http://dx.doi.org/10.1016/j.cie.2013.12.011.

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29

Xue, Dong Juan, Tian Yi Gao, Guang Yu Mu, and Ying Pan. "Research of Parts Inventory Classification and Management Strategy in Manufacturing Enterprises." Applied Mechanics and Materials 433-435 (October 2013): 2359–62. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.2359.

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The correct inventory strategy is important to control reasonable inventory within manufacturing enterprises. And a new material inventory strategy is proposed based on the classification scheme according to the prosperities. First the material inventory styles are classified. Then a decision tree model is defined based on inventory classification result. The value of the node is decided by Fuzzy Neural Network if multi-attribute decision is needed and material inventory strategy can be decided with the classification tree and inventory strategy table. In the end, the implementation of the model in a manufacturing enterprise resource plan system is presented.

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Arikan, Feyzan, and Senay Citak. "Multiple Criteria Inventory Classification in an Electronics Firm." International Journal of Information Technology & Decision Making 16, no. 02 (March 2017): 315–31. http://dx.doi.org/10.1142/s0219622017500018.

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Efficient inventory classification is a vital activity for electronics firms that work with a large amount of inventory items. Although one of the most widely used techniques in inventory classification is ABC analysis, this technique considers only a single criterion as the annual sales volume of each item. In practice, inhomogeneity and the differences among the inventory items necessitate considering multiple criteria to obtain a reliable classification. In this study, an integrated process of the analytic hierarchy process (AHP)-The technique for order preference by similarity to the ideal solution (TOPSIS)-ABC approach is proposed and performed to solve the multiple criteria inventory classification problem in an electronics firm. The steps of the interactive approach are programmed using MATLAB. The results of the integrated interactive approach and of the traditional ABC analysis are presented. The proposed approach gives effective and implementable results for the firm.

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Kabir, G., and R. S. Sumi. "Integrating Fuzzy Delphi with Fuzzy Analytic Hierarchy Process for Multiple Criteria Inventory Classification." Journal of Engineering, Project, and Production Management 3, no. 1 (January 31, 2013): 22–34. http://dx.doi.org/10.32738/jeppm.201301.0004.

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Agarwal, Reshu. "Optimal Order Quantity and Inventory Classification Using Clustering." International Journal of Applied Management Sciences and Engineering 4, no. 2 (July 2017): 41–52. http://dx.doi.org/10.4018/ijamse.2017070104.

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Clustering is the process of analyzing data to find clusters of data objects that are similar in some sense to one another. Some research studies have also extended the usage of clustering concept in inventory management. Yet, not many research studies have considered the application of clustering approach on determining both optimal order quantity and loss profit of frequent items. In this paper, ordering policy of frequent items in each cluster is determined and inventory is classified based on loss rule in each cluster. This helps inventory manager to determine optimum order quantity of frequent items together with the most profitable item in each cluster for optimal inventory control. An example is illustrated to validate the results.

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Hatefi, S. M., and S. A. Torabi. "A Common Weight Linear Optimization Approach for Multicriteria ABC Inventory Classification." Advances in Decision Sciences 2015 (January 1, 2015): 1–11. http://dx.doi.org/10.1155/2015/645746.

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Organizations typically employ the ABC inventory classification technique to have an efficient control on a huge amount of inventory items. The ABC inventory classification problem is classification of a large amount of items into three groups: A, very important; B, moderately important; and C, relatively unimportant. The traditional ABC classification only accounts for one criterion, namely, the annual dollar usage of the items. But, there are other important criteria in real world which strongly affect the ABC classification. This paper proposes a novel methodology based on a common weight linear optimization model to solve the multiple criteria inventory classification problem. The proposed methodology enables the classification of inventory items via a set of common weights which is very essential in a fair classification. It has a remarkable computational saving when compared with the existing approaches and at the same time it needs no subjective information. Furthermore, it is easy enough to apply for managers. The proposed model is applied on an illustrative example and a case study taken from the literature. Both numerical results and qualitative comparisons with the existing methods reveal several merits of the proposed approach for ABC analysis.

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Kaabi, Hadhami, Khaled Jabeur, and Talel Ladhari. "A Genetic Algorithm-Based Classification Approach for Multicriteria ABC Analysis." International Journal of Information Technology & Decision Making 17, no. 06 (November 2018): 1805–37. http://dx.doi.org/10.1142/s0219622018500475.

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ABC analysis is a widespread classification technique designed to manage inventory items in an effective way by relaxing controls on low valued items and applying more rigorous controls on high valued items. In the literature, many classification models issued from different methodologies such as Mathematical Programming (MP), Metaheuristics, Artificial Intelligence (AI) and Multicriteria Decision Making (MCDM) are proposed to perform the ABC inventory classification. To the best of our knowledge, the cross-fertilization of classification models issued from different methodologies is rarely tackled in the literature. This paper proposes some hybrid classification models based on both Genetic Algorithm (Metaheuristics) and two MCDM methods (Weighted Sum (WS) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)) to carry out the ABC inventory classification. To test the performance of the proposed classification models with respect to some existing models, a benchmark dataset from a Hospital Respiratory Therapy Unit (HRTU) is used. The computational results show that our proposed models outperformed the existing classification models according to some inventory performance measures. An additional performance analysis has also shown the effectiveness of our proposed models in inventory management.

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35

Clifford, James P. "Manage Work Better to Better Manage Human Resources: A Comparative Study of Two Approaches to Job Analysis." Public Personnel Management 25, no. 1 (March 1996): 89–102. http://dx.doi.org/10.1177/009102609602500108.

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This study looks at the consequences of analyzing the same work. with two different methods of job analysis. Position Classification Questionnaire and Task Inventory approaches were used to study the same work functions. The Position Classification questionnaire divided these work functions into sixteen unique classifications for purposes of wage and salary determination. The Task Inventory approach divided these work functions into 28 unique classifications for purposes developing training programs. The two approaches arrived at considerably different conclusions with regard to how work should be organized into jobs and how jobs should be grouped into classifications. The data collected through job analysis will have significant impact on subsequent human resource decisions. Organizations should take great care in the collection data relating to the work of the organization in order efficiently and effectively manage the organization's human resources.

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Priyadharshini, S., S. Rama Subramoniam, K. Ganesha Raj, and V. Anandhi. "Coconut Inventory and Mapping Using Object Oriented Classification." International Journal of Current Microbiology and Applied Sciences 8, no. 08 (August 10, 2019): 58–65. http://dx.doi.org/10.20546/ijcmas.2019.808.007.

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37

Agarwal, Reshu, and Mandeep Mittal. "Inventory Classification Using Multi-Level Association Rule Mining." International Journal of Decision Support System Technology 11, no. 2 (April 2019): 1–12. http://dx.doi.org/10.4018/ijdsst.2019040101.

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Popular data mining methods support knowledge discovery from patterns that hold in relations. For many applications, it is difficult to find strong associations among data items at low or primitive levels of abstraction. Mining association rules at multiple levels may lead to more informative and refined knowledge from data. Multi-level association rule mining is a variation of association rule mining for finding relationships between items at each level by applying different thresholds at different levels. In this study, an inventory classification policy is provided. At each level, the loss profit of frequent items is determined. The obtained loss profit is used to rank frequent items at each level with respect to their category, content and brand. This helps inventory manager to determine the most profitable item with respect to their category, content and brand. An example is illustrated to validate the results. Further, to comprehend the impact of above approach in the real scenario, experiments are conducted on the exiting dataset.

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Lolli, Francesco, Alessio Ishizaka, Rita Gamberini, Elia Balugani, and Bianca Rimini. "Decision Trees for Supervised Multi-criteria Inventory Classification." Procedia Manufacturing 11 (2017): 1871–81. http://dx.doi.org/10.1016/j.promfg.2017.07.326.

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39

Rizkya, I., R. M. Sari, Erwin, and R. F. Sari. "Determination of Inventory Policy based on ABC Classification." IOP Conference Series: Materials Science and Engineering 851 (May 30, 2020): 012014. http://dx.doi.org/10.1088/1757-899x/851/1/012014.

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40

Scott, D. A., and T. A. Jones. "Classification and inventory of wetlands: A global overview." Vegetatio 118, no. 1-2 (June 1995): 3–16. http://dx.doi.org/10.1007/bf00045186.

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Yang, Liu, Haitao Li, James F. Campbell, and Donald C. Sweeney. "Integrated multi-period dynamic inventory classification and control." International Journal of Production Economics 189 (July 2017): 86–96. http://dx.doi.org/10.1016/j.ijpe.2017.04.010.

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Chen, Jin-Xiao. "Peer-estimation for multiple criteria ABC inventory classification." Computers & Operations Research 38, no. 12 (December 2011): 1784–91. http://dx.doi.org/10.1016/j.cor.2011.02.015.

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43

Hadi-Vencheh, A. "An improvement to multiple criteria ABC inventory classification." European Journal of Operational Research 201, no. 3 (March 2010): 962–65. http://dx.doi.org/10.1016/j.ejor.2009.04.013.

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Xue, Dong Juan. "Inventory Classification and Management Strategy of Components and Parts in Assembly Workshops." Applied Mechanics and Materials 687-691 (November 2014): 5028–31. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.5028.

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Material inventory management plays an increasingly important role in modern operations management within manufacturing enterprises. And a multi-attribute classification model has been put up based on the application of the decide tree model and fuzzy artificial neural network. First the material inventory styles are classified. Then a decision tree model is defined based on inventory classification result. The value of the node is decided by Fuzzy Neural Network if multi-attribute decision is needed and material inventory strategy can be decided with the classification tree and inventory strategy table. In the end, the implementation of the model in a manufacturing enterprise resource plan system is presented.

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Nallusamy, S., R. Balaji, and S. Sundar. "Proposed Model for Inventory Review Policy through ABC Analysis in an Automotive Manufacturing Industry." International Journal of Engineering Research in Africa 29 (March 2017): 165–74. http://dx.doi.org/10.4028/www.scientific.net/jera.29.165.

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A global market inventory is one of the largest and most significant resources of a manufacturing business. ABC analysis is one of the methods used extensively in manufacturing industries for inventory classification. The familiar ABC inventory classification approach categorizes inventory items as A, B and C classes according to their annual consumption value. To direct and control the inventory items more proficiently the inventories managers regularly classify and group all the inventory items. The objective of this paper is to create a periodic review policy based on the ABC classification in order to control the raw materials more efficiently. Based on the data collected through original equipment manufacturer, the necessary analysis was carried out. From the observed results, it was found that the developed periodic review policy improved the inventory turnover ratio from 3.15 to 2.13 by optimizing the inventory level for one year.

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Agarwal, Reshu. "Ordering Policy and Inventory Classification Using Temporal Association Rule Mining." International Journal of Productivity Management and Assessment Technologies 6, no. 1 (January 2018): 37–49. http://dx.doi.org/10.4018/ijpmat.2018010103.

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A modified framework that applies temporal association rule mining to inventory management is proposed in this article. The ordering policy of frequent items is determined and inventory is classified based on loss rule. This helps inventory managers to determine optimum order quantity of frequent items together with the most profitable item in each time-span. An example is illustrated to validate the results.

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47

Dreger, Ralph Mason. "Background for and Development of a Brief Children's Behavioral Classification Project Inventory." Psychological Reports 79, no. 3 (December 1996): 963–77. http://dx.doi.org/10.2466/pr0.1996.79.3.963.

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A brief form of the Children's Behavioral Classification Project Inventory was developed. A Proto-CBCP Inventory consisting of 229 behavioral items and 11 demographic variables was administered to 351 parents of children with clinical problems and 80 normal control parents. The analyses of these data were thoroughly criticised by experts in psychology and related fields. From this critique and trial runs which included 22 demographic variables, a full inventory of 274 behavioral items plus child's age, sex and clinic vs nonclinic status resulted; this inventory has a 30-factor structure. Given the length of the full-scale inventory and the number of factors derived from it, a briefer form was created consisting of 189 items from which were derived 13 factors. This inventory was standardized on a total n of 1379 for use as a screening device in clinics.

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Franklin, Steven E. "Pixel- and object-based multispectral classification of forest tree species from small unmanned aerial vehicles." Journal of Unmanned Vehicle Systems 6, no. 4 (December 1, 2018): 195–211. http://dx.doi.org/10.1139/juvs-2017-0022.

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Forest inventory, monitoring, and assessment requires accurate tree species identification and mapping. Recent experiences with multispectral data from small fixed-wing and rotary blade unmanned aerial vehicles (UAVs) suggest a role for this technology in the emerging paradigm of enhanced forest inventory (EFI). In this paper, pixel-based and object-based image analysis (OBIA) methods were compared in UAV-based tree species classification of nine commercial tree species in mature eastern Ontario mixedwood forests. Unsupervised clustering and supervised classification of tree crown pixels yielded approximately 50%–60% classification accuracy overall; OBIA with image segmentation to delineate tree crowns and machine learning yielded up to 80% classification accuracy overall. Spectral response patterns and tree crown shape and geometric differences were interpreted in context of their ability to separate tree species of interest with these classification methods. Accuracy assessment was based on field-based forest inventory tree species identification. The paper provides a brief summary of future research issues that will influence the growth of this geomatics innovation in forest tree species classification and forest inventory.

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Jiang, R., and Xiao Na Yuan. "A New Approach for Multi-Criteria ABC Analysis." Advanced Materials Research 44-46 (June 2008): 581–86. http://dx.doi.org/10.4028/www.scientific.net/amr.44-46.581.

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Abstract:

Manufacturing enterprises need to manage many inventory items. To effectively manage the inventory, a usual practice is to classify the inventory items into several groups and use different management strategies for the items in different groups. ABC classification has been widely used, and has evolved into various multi-criteria classification schemes. Recently, a few multi-criteria ABC classification approaches using variable weights appeared in the literature. This paper analyzes their properties and proposes an improved approach. A numerical example is included to illustrate the appropriateness of the proposed approach.

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Ly, Rathanaksambath, and Morrakot Raweewan. "Optimizing Inventory Classification and Service Levels Under Budget and Warehouse Space Control." International Journal of Knowledge and Systems Science 12, no. 3 (July 2021): 80–92. http://dx.doi.org/10.4018/ijkss.2021070104.

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Abstract:

There are several techniques of inventory classification and prioritization based on a single criteria, bi-criteria, or multiple criteria; however, managing inventory can fail when trying to fit inventory classes into the operating budget and available warehouse space. This research focuses on optimizing inventory classes and determining an optimal service level of each class while simultaneously satisfying operating budget and space constraints. The idea is to help decision makers to effectively rank and group SKUs and manage them within the constraints while satisfying customers. This proposed methodology is an optimality-based approach that uses mixed integer linear programing to solve the problem. Computational experiments are conducted to illustrate the proposed method. Results are compared with a classical ABC inventory analysis.

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