INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019
UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK
USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA.
India: Editor International Journal of Innovative Technology & Creative Engineering 36/4 12th Avenue, 1st cross St, Vaigai Coliny Ashok Nagar Chennai , India 600083 Email: editor@ijitce.co.uk
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019
IJITCE PUBLICATION
International Journal of Innovative Technology & Creative Engineering Vol.9 No.8 August 2019
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019
Dear Researcher,
Greetings! Articles in this issue discusses about Industry 4.0— Emerging Trends. And Corporate social Responsibility, Effective Feature Selection Method for Cervical Cancer Dataset Using Data Mining Classification Analytical Model. IJITCE was also invited as a Journal Partner to associate with ECARGOLOG in the conference on Logistics. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies in the next month as we cover two major conferences. We will also be presenting a new section” Industry4.0 Corner” where you will see series of articles on latest developments in NextGen manufacturing. Thanks, Editorial Team IJITCE
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019
Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbara Ph.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. Selvanathan Arumugam Ph.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA. Dr. S.Prasath Ph.D Assistant Professor, Department of Computer Science, Nandha Arts & Science College, Erode , Tamil Nadu, India
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019
Review Board Members Mr. Rajaram Venkataraman Chief Executive Officer, Vel Tech TBI || Convener, FICCI TN State Technology Panel || Founder, Navya Insights || President, SPIN Chennai Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. MutamedTurkiNayefKhatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), TulKarm, PALESTINE. Dr.P.UmaMaheswari Prof &Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore. Dr. T. Christopher, Ph.D., Assistant Professor &Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India. Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE HOD & Associate Professor, IT Dept, Medi-Caps Inst. of Science & Tech.(MIST),Indore, India Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019 Dr. doc. Ing. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). Dr. Ing. RostislavChotěborský,ph.d, Katedramateriálu a strojírenskétechnologie, Technickáfakulta,Českázemědělskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21
Dr. AmalaVijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-BangloreWesternly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech& PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ. Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY SeraphinChallyAbou Professor,Mechanical& Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 OrdeanCourt,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019 Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials CSIRO Process Science & Engineering Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688
Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar)01332-000, São Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road - Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019 Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579 Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. SeraphinChallyAbou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 558123042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India. MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol"Noshirvani" University of Technology, Iran. Dr. Kulasekharan, N, Ph.D Technical Lead - CFD,GE Appliances and Lighting, GE India,John F Welch Technology Center,Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India. Dr. Manjeet Bansal Dean (Post Graduate),Department of Civil Engineering,Punjab Technical University,GianiZail Singh Campus,Bathinda -151001 (Punjab),INDIA Dr. Oliver Jukić Vice Dean for education,Virovitica College,MatijeGupca 78,33000 Virovitica, Croatia Dr. Lori A. Wolff, Ph.D., J.D. Professor of Leadership and Counselor Education,The University of Mississippi,Department of Leadership and Counselor Education, 139 Guyton University, MS 38677
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019
Contents Effective Feature Selection Method for Cervical Cancer Dataset Using Data Mining Classification Analytical Model ….……...[ 718] Corporate Social Responsibility
….……...[ 727]
Industry 4.0
….……...[736]
The three-day expo of Logmat & Warehousing
….……...[741]
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019
Effective Feature Selection Method for Cervical Cancer Dataset Using Data Mining Classification Analytical Model Dr.D.Rajakumari1, S.Karthika2 Assistant Professor, Department of Computer Science, Nandha Arts and Science College, Erode, Tamilnadu, India1 rsrajakumarid@gmail.com MPhil Part-Time Scholar, Department of Computer Science, Nandha Arts and Science College, Erode, Tamilnadu, India2 Karthika109suresh@gmail.com Abstract— This paper Effective Prediction Model for I. INTRODUCTION Cervical Cancer disease Using Data Mining Data Mining is one of the most encouraging Classification Algorithm describes classification areas of research with the purpose of finding useful techniques and shows the advantage of feature information from voluminous data sets. It has been used selection approaches to the best predicting of in many domains like image mining, opinion mining, web cervical cancer disease. There are 32 attributes with mining, text mining, graph mining etc. Its applications 858 samples. Besides, this data suffers of missing include anomaly detection, financial data analysis, values and imbalance data. Therefore, over- medical data analysis, social network analysis, market sampling, under-sampling and imbedded over and analysis etc, under sampling have been used. In this paper implemented a feature model construction and Data Mining is particularly useful in medical field when comparative analysis for improving prediction no availability of evidence favouring a particular accuracy of cervical cancer patients in four phases. treatment option is found. Large amount of complex data In first phase, min-max normalization algorithm is is being generated by healthcare industry about applied on the original cervical cancer patient patients, diseases, hospitals, medical equipment, claims, datasets collected from UCI repository. In cervical treatment cost etc. that requires processing and analysis cancer dataset prediction second phase, by the use for knowledge extraction. Data mining comes up with a of feature selection, subset (data) of cervical cancer set of tools and techniques which when applied to this patient dataset from whole normalized cervical processed data, provides knowledge to healthcare cancer patient datasets is obtained which comprises professionals for making appropriate decisions and only significant attributes. Third phase, enhancing the performance of patient management classification algorithms are applied on the data set. tasks. In the fourth phase, the accuracy will be calculated Millions of early deaths among women is due to using root mean square value, root mean error value. KNN and SVM algorithm is considered as the lung and breast cancer but cervical cancer is most better performance algorithm after applying feature treacherous because it is only diagnosed in females. selection. Finally, the evaluation is done based on Woman’s reproductive system consists of cervix, uterus, accuracy values. Thus outputs shows from vagina and the ovaries. Cervix is the opening to the proposed GA base feature extraction with uterus from the vagina where cervical cancer occurs [4]. classification model implementations indicate that Sexually transmitted human papillomavirus (HPV) is the KNN and SVM algorithm performances all other important cause of cervical cancer. classification algorithm with the help of feature Cervical Cancer occurrence is plentiful in low- and selection with an accuracy of 97.60%. Keywords: Cervical Cancer dataset, Data Mining middle-income countries. The important task of cervical cancer is screening. A perfect screening test is the one Algorithm, KNN, SVM that is least incursive, easy to accomplish, acceptable to subject, inexpensive and effective in diagnosing the
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019 disease process in its early incursive stage when the treatment is easy for illness. There are four screening methods including cervical cytology also called Pap smear test, biopsy, Schiller and Hinslemann.
Cytology screening method is a microscopic analysis of cells scratched from the cervix and is used to detect cancerous or pre-cancerous conditions of the cervix. Biopsy method is a surgical process which includes finding of a living tissue sample for performing diagnosis. The solution of iodine has applied for visual inspection of cervix known as Hinslemann test. Lugol's iodine is used for visual assessment of cervix after smearing Lugol's iodine recognition rate of doubtful region over the cervix, this is also known as Schiller test. This research work focuses on a prediction of disease. Since there are many related diseases, the Cervical Cancer disease is very dangerous because it leads to failure and also it cannot be predicted at early stages. The Cervical Cancer disease has stages which can be identified by regular checkup. If the disease is diagnosed than the patient's past history is analyzed. The classification model plays a vital role in the prediction of diseases. The aim of this research work is to develop an efficient predictive healthcare decision support system using data mining techniques. A common or dataset is trained in this system using KNN, MLP, SVM and Naïve Bayes classification algorithms and tested with the sample data which predict the patent’s outcome of Cervical Cancer Diseases. Data mining has been with success utilized in data discovery for prognostic functions to form a lot of active and correct call. Different data mining techniques i.e. Decision Tree, Bayesian Network, K-Nearest Neighbor, Naïve Bayes, Support Vector Machine, Multi layer perceptron etc. are used to predict disease in early stage which also helps to avoid the patient’s complications. The main objective of this research work is to predict disease using Step wise Regression Model (SRM) and Built around the Random Forest Classification algorithm (BRFC), the result is obtained by comparing the algorithms and analysis the performance of the algorithm. Different data mining techniques are used to pull data. The experimental comparison of KNN, MLP, SVM and NBC are done based on the
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performance measures of classification accuracy and execution time. II. RELATED WORKS A. Ashfaq Ahmed et al., [1] have given a piece exploitation machine learning techniques, particularly Support Vector Machine [SVM] and Random Forest [RF]. These were wont to study, classify and compare cancer, liver and cardiovascular disease knowledge sets with variable kernels and kernel parameters. Results of Random Forest and Support Vector Machines were compared for various knowledge sets like carcinoma unwellness dataset, disease dataset and cardiovascular disease dataset. It’s over that variable results were determined with SVM classification technique with completely different kernel functions. B. Giovanni Caocci et al., [2] so as to predict future urinary organ Transplantation Outcome, they taken discrimination between a man-made Neural Network and supplying Regression. Comparison has been done supported the Sensitivity and specificity of supplying Regression and a man-made Neural Network within the prediction of urinary organ rejection in 10 coaching and corroborative datasets of urinary organ transplant recipients. From the experimental results that each the formula approaches were complementary and their combined algorithms won’t to improve the clinical decisionmaking method and prognosis of urinary organ transplantation. C. Lakshmi.K.R et al., [3] analyzed Artificial Neural Networks, call tree and Logical Regression supervised machine learning algorithms. These algorithms are used for urinary organ chemical analysis. For classification method they used an information mining tool named Tanagra. The tenfold cross validation is employed so as to gauge the classified knowledge proceeded by the comparison of these knowledge. From the experimental result they absorbed that ANN performed higher than the choice tree and Logical Regression algorithms. D. Neha Sharma et al., [4] detected and expected urinary organ diseases as a prelude to correct treatment to patients. The system was used for detection in patients with disease and also the results of their IF-THEN rules expected the presence
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019 of a disease. Their technique used fuzzy systems and a neural network referred to as a neural blur system, supported the results of the input file set obtained. Their system was a mix of fuzzy systems that created results exploitation correct mathematical calculations, rather than probabilistic based mostly classifications. Usually results supported arithmetic tends to possess higher accuracies. Their work was ready to acquire helpful knowledge in conjunction with optimizations in results. E. Swathi Baby P et al., [5] n contestable that data processing strategies may be effectively employed in medical applications. Their study collected knowledge from patients affected with excretory organ diseases. The results showed knowledge mining’s pertinence during a sort of medical applications. K-means (KM) rule will verify range of clusters in massive knowledge sets. Their study analyzed tree AD, J48, star K, theorem wise, random forest and tree - based ADT naive theorem on J48 renal disorder knowledge Se and noted that the techniques offer applied mathematics analysis on the utilization of algorithms to predict excretory organ diseases in patients. G. Talha Mahboob Alam et al., [6] in their study data mining techniques including decision tree algorithms are used in biomedical research for predictive analysis. Cervical cancer prediction through different screening methods using data mining techniques like Boosted decision tree, decision forest and decision jungle algorithms as well performance evaluation has done on the basis of Area under Receiver operating characteristic(AUROC) curve, accuracy, specificity and sensitivity. H.Veenita Kunwar et al., [7] in their study had foreseen Cervical Cancer disorder (CKD) mistreatment naive theorem classification and artificial neural network (ANN). Their results showed that naive theorem created correct results than artificial neural networks. it had been conjointly ascertained that classification algorithms were wide used for investigation and identification of CKDs. I. Vijayarani et al., [8] classification method is employed to classify four varieties of excretory organ diseases. Comparisons of Support Vector Machine (SVM) and Naïve mathematician classification algorithms are done supported the performance
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factors, classification, accuracy and execution time. As results, the SVM achieves enhanced classification performance. Therefore it's thought-about because the best classifier when put next with Naïve mathematician classifier rule. However, Naïve mathematician classifier classifies the information with minimum execution time. during this study, we tend to apply data processing techniques, recently hierarchic among the highest ten as best classifiers, to predict Cervical disorder on the idea of the data attributes within the info employed in order to reason patients World Health Organization are littered with the Cervical Cancer renal disorder (ckd) and patients World Health Organization don't seem to be littered with it (not ckd). J. Dhayanand et al., [9] have conferred a piece to predict renal disorder by classifying four varieties of excretory organ diseases: Acute Nephritic Syndrome, Cervical Cancer renal disorder, acute failure and Cervical Cancer nephritis, mistreatment Support Vector Machine (SVM) and Artificial Neural Network (ANN), then examination the performance of these two algorithms on the idea of accuracy and execution time. The results show that the performance of the ANN is healthier than the SVM rule. K. Sharma et al., [10] applied varied machine learning algorithms to a tangle within the domain of diagnosis and analysed their potency in predicting the results. The matter selected for the study is that the designation of the Cervical Cancer nephropathy. The dataset used for the study consists of four hundred instances and twenty four attributes. The authors evaluated twelve classifications on techniques by applying them to the Cervical Cancer nephropathy knowledge. So as to calculate potency, results of the prediction by candidate ways were compared with the particular medical results of the topic. III. RESEARCH METHODOLOGY In the paper system, a classical approach is papered for locating the diseases of urinary organ cancer victimization data processing classification techniques of Random Forest and Naïve mathematician. The techniques offer profit to the doctors, physicians, medical students and patients to form call relating to the diagnosing of the urinary organ cancer diseases.
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019 The papered KNN primarily based classifier determines neighborhoods directly from coaching observations and it works with numeric feature vector of the urinary organ cancer dataset. The foremost advantage of this approach is that the correct operative coming up with supported image diagnostic information of the urinary organ cancer patients. The papered approach is employed to discover the urinary organ cancer patients affected and also the experimental application shows the results of the potency of the papered approach.
In addition to it for analyzing aid information, major steps information mining approaches like preprocess data, replace missing values, feature choice, machine learning and build call square measure applied on train dataset. Finally the random forest methodology has been dead on the coaching dataset of urinary organ cancer sickness for the classification method. • Decision tree predicts a category victimization predefined classification tree with contains each numerical and categorical feature vector. • To guarantee the validity of result's allotted by distribution varied values of K. • The application is often utilized by anybody particularly for medical practitioners via web for diagnosing purpose. • Select the class-outliers, that is, coaching information that square measure classified incorrectly by Random forest (for a given N time k th) A. System Architecture • Step1: Read the Cervical Cancer Dataset from UCI Machine learning Repository. The dataset have 400 records. • Step 2: Normalize the Cervical patient dataset using Z-Score Normalization. • Step 3: Feature extraction will be done by using Step wise Regression Model (SRM)
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UCI Cervical Cancer Dataset
Normalization
Feature Selection
Classification Algorithm KNN, RF, MLP, SVM, NBC
Validation Metrics Analysis
Accuracy Results
Fig 3.1 System Architecture • Step4: The feature will be selected and put in to data frame. • Step5: Classification algorithms are applied on the selected feature. • Step6: KNN Classification to create centered point of data a new group contains the most important data points and others will be considered as outliers • Step 7: RF classification, multiple trees are induced in the forest, the number of trees is pre-decided by the parameter N-tree. • Step 8: SVM Classification to create a new group contains the most important data points and others will be considered as outliers. • Step 9: NBC Classification prediction values for RPCC and RCC and compare train dataset and accuracy calculate. • Step 10:To apply Cancer dataset using MLP classification model and accuracy calculated.
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019 • Step 11: The results are obtained, MLP and SVM gives better accuracy when compare to other algorithms. • Step 12: Accuracy will be analyzed. • Step13: Finally the evaluation metrics will be calculated. B. Normalization Normalization is scaling technique or a pre process stage. Where, we are able to discover new dimension from associate degree existing one series. It is often useful for the prediction or statement operates heaps. Therefore maintain the big distinction of prediction and statement the standardization technique is needed to form them nearer. Z-score is that the variety of normal deviations from the mean an information purposes. However additional technically it’s calculated of what percentage normal deviations below or on top of the population means that a rough score. A z-score is additionally referred to as a customary score and it are often placed on a standard distribution curve. Z-scores vary from -3 normal deviations (which would fall to the left of the conventional distribution curve) up to +3 normal deviations (which would fall to the way right of the conventional distribution curve). so as to use a z-score, you wish to spot the mean μ and conjointly the population variance σ. z = (x – μ) / σ C. Feature Selection Feature extraction is that the model of choosing a set of the terms gift within the coaching set and victimization solely this set as options in text classification. Feature extractions provide 2 main functions. First, it makes coaching and applying a classifier additional powerful by decreasing the scale of the adequate vocabulary. Feature extraction method is of explicit significance for classifiers that, unlike NB, square measure costly to coach. Second, feature extraction typically will increase classification accuracy by eliminating noise options. A noise feature is one that, once joined to the document illustration, will increase the classification error on new knowledge. Facilitating knowledge visual image is dashing up the execution of mining algorithms and reducing descending times
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Stepwise regression n may be a combination of the forward and backward choice techniques. Stepwise regression may be a modification of the forward choice in order that when every step within which a variable was added , all candidate variables within the model square measure checked to examine if their significance has been reduced below the required tolerance level. If a no important variable is found, it's aloof from the model. Stepwise regression needs 2 significance levels: one for adding variables and one for removing variables. The cutoff likelihood for adding variables ought to be but the cutoff likelihood for removing variables in order that the procedure doesn't get into associate degree infinite loop. D. Classification Algorithm a) RF Algorithm RF is an algorithmic program accustomed manufacture a choice tree that is increase of previous ID3 calculation. It en-large the ID3 algorithmic program is managing each continuous and distinct property, missing values and pruning trees once construction. The choice trees created by C4.5 are often used for grouping and sometimes cited as a applied math classifier. C4.5 creates call trees from a group of coaching urinary organ information same approach as Id3 algorithmic program. Because it could be a supervised learning algorithmic program it needs a group of coaching examples which may be seen as a pair: input object and a desired output worth (class). The algorithmic program analyzes the coaching set and frame a classifier that has to have the dimensions to accurately prepare each coaching and take a look at cases b) NBC Model: The Naive Bayesian classifier relies on Bayes’ theorem with independence assumptions between predictors. Naive Thomas Bayes classifiers area unit a family of straightforward probabilistic classifiers supported applying theorem. Thomas Bayes theorem provides some way of conveying the posterior likelihood, P(c/x), from P(c), P(x), and P(x/c). It assumes that the result of the worth of a predictor (x) on a given class (c) is freelance of the values of alternative predictors. This assumption is named category conditional independence. The Naïve Bayesian classification predicts that the tuple ‘x’ belongs to the category ‘c’ victimization the formula. P(c/x)= (x ⁄c ) / ( P (x )
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019 •
P (c/x) is that the posterior likelihood of sophistication (target) given predictor (attribute). • P(c) is that the previous likelihood of sophistication. • P (x/c) is that the chance that is that the likelihood of predictor given category. • P(x) is that the previous likelihood of predictor. c) KNN Classification K-Nearest Neighbor (Knn) –Techniques KNN could be a supervised learning algorithmic program that classifies new information supported minimum distance from the new information to the K nearest neighbor. The papered work has used geometrician Distance to outline the closeness. Pseudo-code for the KNN classifier is declared below: •
• • •
Step 1: Input: D= x=(x1,…..,xn) new instance to be classified • Step 2: for every labeled instance (xi,ci) Calculated(xi, x) Step 3: Ordered(xi, x) from lowest to highest, (i=1,….,N) Step 4: Select the K nearest instances to x : Dx K Step 5: Assign to x the foremost frequent category in Dx K
d) MLP (Multilayer Perceptron) A multilayer perceptron (MLP) could be a feed forward artificial neural network model that maps urinary organ datasets of input file onto a collection of applicable outputs. Associative MLP classification could be a multiple layer of nodes in a much-directed graph, with every layer totally connected to following one. A side from the input nodes, every node could be a nerve cell (or process element) with a nonlinear activation perform. MLP classification urinary organ dataset utilizes a supervised learning technique known as back propagation for coaching urinary organ the network. MLP could be a modification of the quality linear perceptron and might distinguish knowledge that isn’t linearly dissociable urinary organ dataset method. E. ANALYSIS METRIC Mean Absolute Error Statistical exactness metrics valuate the accuracy of a system by examination the numerical recommendation scores against the particular user ratings for the user-item pairs within the take a look at
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dataset. Mean Absolute Error (MAE) between ratings and predictions could be a wide used metric Root Mean sq. Error The Root Mean sq. Error (RMSE) (also known as the foundation mean sq. deviation, RMSD) could be a oftentimes used live of the distinction between values expected by a model and therefore the values truly determined from the setting that's being modeled. These individual variations also are known as residuals, and therefore the RMSE serves to combination them into one live of prognostic power. The RMSE of a model prediction with relevancy the calculable variable X model is outlined because the root of the mean square error: Root Relative Squared Error Correlation – usually measured as a parametric statistic – indicates the strength and direction of a linear relationship between 2 variables (for example model output and determined values). Variety of various coefficients square measure used for various things Kappa Metrics It returns the constant value. It measures the agreement between classification and truth values. It of one represents good agreement, whereas a price of zero represents no agreement. IV. EXPERIMENTAL RESULTS A. Dataset description Cervical cancer data involves 858 samples and 32 features as well as four classes (Hinselmann, Schiller, Cytology and Biopsy) has been published in. This paper focuses on studying the Biopsy target as it recommended by the literature review.
Fig 4.1. Cervical Cancer Dataset Attribute
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019 B. Performance Analysis Table 4.1 describes a training dataset for cervical cancer dataset classification for NBC Model and SVM analysis model. The table contains precision, recall, Fmeasure and accuracy details are shown TABLE 4.1 CERVICAL TRAINING DATASET METRICS ANALYSIS Technique s
NBC
SRC Feature
Precision
Recall
No of Attributes
500
32Including class Label
0.7152
MM)
SRC Feature Ins ta nc es 25 0
NBC
Fmeasure
12 Including class Label
0.7554
SVM (FSD MM) 0.7642
0.7772
0.8035
0.8182
25 0
0.7821
SVM
500
Techni ques
Accuracy
Instance s
(FSD
TABLE 4.2 CERVICAL TEST DATASET METRICS ANALYSIS
0.8045
No of Attribute s 32Including class Label 10 Including class Label
Precisio n
Recall
Fmeas ure
Accurac y
0.7092
0.7565
0.7656
0.7981
0.7333
0.7964
0.8072
0.8323
Fig 4.3 describes a test dataset for cervical cancer dataset classification for NBC Model and FSDMM analysis model. The figure contains precision, recall, F-measure and accuracy details are shown.
Fig 4.2 describes a training dataset for cervical cancer dataset classification for NBC Model and SVM analysis model. The figure contains precision, recall, Fmeasure and accuracy details are shown.
Accuracy Rate (%)
Cervical Training Dataset Metrics Analysis
Accuracy Rate (%)
Cervical Test Dataset Metrics Analysis
NBC Model
Metrics
NBC Model
Metrics
Fig 4.2 Cervical Training Dataset Metrics Analysis Table 4.2 describes a test dataset for cervical cancer dataset classification for NBC Model and FSDMM analysis model. The table contains precision, recall, Fmeasure and accuracy details are shown.
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Fig 4.3 Cervical Test Dataset Metrics Analysis In this table 4.3 describe time efficient analysis for Cervical cancer prediction model. In this table contain number of dataset, average time for execution for cancer prediction model details are shown, Table 4.3 Time Analysis for NBC and FSDMM Model using Cervical Cancer Dataset Number of Number of NBC Model FSDMM Dataset Attribute (ms) Model (ms) 150 29 0.233 0.192 250 24 0.345 0.203 350 25 0.456 0.335 400 24 0.522 0.418 450 22 0.633 0.553 600 12 0.693 0.592 In this figure 4.3 describe time efficient analysis for Cervical cancer prediction model. In this figure contain number of dataset, average time for execution for cancer prediction model details are shown,
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Fig 4.4Time Analysis for NBC and FSDMM Model using Cervical Cancer Dataset In this Table 4.4 describe performance analysis for cervical cancer prediction model. In this figure contain number of dataset, average dataset prediction for cancer prediction model details are shown, Table 4.3 Performance Analysis for NBC and FSDMM Model using Cervical Cancer Dataset Number of Number of NBC Model FSDMM Dataset Attribute (%) Model (%) 150 29 77.33 76.55 250 24 79.68 80.23 350 25 81.67 82.44 400 24 82.04 82.89 450 22 83.78 84.67 600 12 85.66 87.78 IN THIS FIGURE 4.5 DESCRIBE PERFORMANCE ANALYSIS FOR CERVICAL CANCER PREDICTION MODEL. IN THIS FIGURE CONTAIN
NUMBER
OF
DATASET,
AVERAGE
DATASET
PREDICTION FOR CANCER PREDICTION MODEL DETAILS ARE SHOWN
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Fig 4.5 Performance Analysis for NBC and FSDMM Model using Cervical Cancer Dataset V. CONCLUSION In paper feature selection is done with the help of SRS approach. The whole datasets of cervical cancer patients is comprised of all relevant or irrelevant attributes. By the use of feature selection, a subset (data) of cervical cancer patient from whole cervical cancer patient datasets will be obtained which comprises only significant attributes This result in the selection of 32 significant attributes consists of values of different classification algorithms. Comparison is made among classification algorithms out of which NBC and SVM algorithm is considered as the better performance algorithm. Because it gives higher accuracy in respective to other classification algorithms after applying feature selection: with an accuracy of 87.78%. The proposed methodology is used to predict the cervical cancer region into separable compartments. However, the method requires further improvement mostly regarding feature selection and segmentation of the cervical dataset into multiple components: renal cortex, renal column, renal medulla and renal pelvis. In addition this paper can be employed for detecting the heart diseases in future with the heart and liver dataset and classification of the diseases.
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.9 NO 8 AUGUST 2019 REFERENCES [1] Ashfaq Ahmed, K., Aljahdali, S., Hussain, S.N.: “Comparative prediction performance with support vector machine and random forest classification techniques”, International Journal Computer Applications. 69 (11), 12–16 ,2016. [2] Giovanni Caocci, Roberto Baccoli, Roberto Littera, Sandro Orrù, Carlo Carcassi and Giorgio La Nasa, “Comparison Between an Artificial Neural Network and Logistic Regression in Predicting Long Term Kidney Transplantation Outcome”, Chapter 5, an open access article distributed under the terms of the Creative Commons Attribution License, http://dx.doi.org/10.5772/53104,2017. [3] Lakshmi. K.R, Nagesh. Y and VeeraKrishna. M “Performance Comparison of Three Data Mining Techniques for Predicting Kidney Dialysis Survivability”, International Journal of Advances in Engineering & Technology, Mar., Vol. 7, Issue 1, pg no. 242-254, 2016. [4] Neha Sharma, Er. Rohit Kumar Verma, “Prediction of Kidney Disease by using Data Mining Techniques”, Prediction of Kidney Disease by using Data Mining Techniques, 2016. [5] Swathi Baby P and Panduranga Vital T, “Statistical Analysis and Predicting Kidney Diseases using Machine Learning Algorithms”, International Journal of Engineering Research & Technology (IJERT), 2015. [6] Talha Mahboob Alam,Muhammad milhan afzal khan,”Cervical Cancer Prediction through different screening methods using data mining”,International Journal of Advanced Computer Science and Applications(IJACSA),2019. [7] Veenita Kunwar, Khushboo Chandel, A. Sai Sabitha, and Abhay Bansal, “Chronic Kidney Disease Analysis Using Data Mining Classification Techniques”, IEEE, 2016. [8] Vijayarani, S., Dhayanand, S.: “Data mining classification algorithms for kidney disease prediction”, International Journal of Cybern. Inf. (IJCI) 4(4), 13–25, 2017. [9] Vijayarani, S., Dhayanand, S.: “Kidney disease prediction using SVM and ANN algorithms”, International Journal Comput. Business Res. 6(2), 2017.
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Sharma, S., Sharma, V., & Sharma, A. (2017). Performance Based Evaluation of Various Machine Learning Classification Techniques for Chronic Kidney Disease Diagnosis. arXiv preprint arXiv:1606.09581,2017.
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Corporate Social Responsibility (CSR) M P Saravanan Master Class Certified, IoD An abstract of IoD Master Class Dissertation 148th Batch Master Class for Directors, Chennai India
/ Responsible Business) is a form of corporate selfregulation integrated into a business model. CSR policy functions as a self-regulatory mechanism whereby a business monitors and ensures its active compliance with the spirit of the law, ethical standards and international norms. With some models, a firm's implementation of CSR goes beyond compliance and engages in "actions that appear to further some social good, beyond the interests of the firm and that which is required by law." CSR aims to embrace responsibility for corporate actions and to encourage a positive impact on the environment and stakeholders including consumers, employees, investors, communities, and others.
Government of India has actually implemented the concept of CSR in the new Companies Act 2013, On 27th February, 2014, the Government of India has notified the rules for CSR spending u/s 135 of the New Companies Act 2013 along with Companies (Corporate Social Responsibility Policy) Rules, 2014 effective from 1st April 2014. Turning the CSR from voluntary activities to the mandated responsibilities, also governed by the bundle of regulations as follows: Eligibility Criteria:
The term "corporate social responsibility" became popular in the 1960s and has remained a term used indiscriminately by many to cover legal and moral responsibility more narrowly construed.
Turnover up to “1000 Crores” having a net profit of at least ‘5crore’ during any financial year, are covered by this provision.
Proponents argue that corporations increase long term profits by operating with a CSR perspective, while critics argue that CSR distracts from business' economic role.
I.
DEFINITION
Corporate Social Responsibility (CSR), a term widely use for defining the responsibilities of corporate world towards the society & environment. Although the term is not new in this Corporate world but its scope & meaning has undergone major changes from treating it as a mere charity in comparison with the responsibilities/duties of the Corporate towards the outer world. There are many big entities who have been actively engaged in the CSR activities but unfortunately the number is relatively less. In order to encourage more entities to participate in the process of development of the society via- CSR, the
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Company (includes foreign company with branches or project in India) having minimum net worth of Rs 500 Crores.
Composition of CSR Committee The Company should constitute a Corporate Social Responsibility Committee as follows: 1. The Committee shall consist of minimum 3 (three) including 1 (one) Independent Director, however in case of Private Company or the Company, which is not required to appoint Independent Director on board, or Foreign Company the committee can be formulated with (2) two directors. 2. The CSR Policy shall be formulated in accordance with Schedule VII and the CSR Committee will be responsible for framing the policy, finalizing the amount to be spent on CSR, monitoring & implementation of the Scheme. 3. If Company ceases to fulfill the eligibility criteria for three consecutive years, then the company is not required to comply until the company will meet the eligibility criteria once again.
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The CSR Rules provides the manner in which CSR committee shall formulate, monitor the policy and manner of understanding for CSR activities. Under the rules, the Government has also fixed a threshold limit of 2% of the “Average’ Net Profits of the block of previous three years on CSR activities and if Company fails to spend such amount, disclosures are to be made for the same. But an exemption has been given to the Companies that do not satisfy the above threshold for three consecutive years.
II.
COMPANIES ACT 2013
‘Governance’ is derived from the Greek verb kubernáo meaning ‘to steer’. A brief on CSR Activities as prescribed under Schedule VII of CA, 2013 1. Objective to efface the daily life segments including poverty, malnutrition and hunger while enhancing the standard of living and promoting the facets of better health care and sanitation. 2. Initiative to promote the different segments of education including special education and programs to enhance the vocation skills for all ages like children, women, elderly and conducting other livelihood enhancement projects. 3. Aim to bring the uniformity in respect of different sections of the society to promote gender equality and other facilities for senior citizens and developing hostels for women and orphans and taking initiative for empowering women and lowering inequalities faced by socially and economically backward groups. 4. Elevate the segment of flora and fauna to bring the ecological balance and environmental sustainability in respect of animal welfare, conservation of natural resources and ago forestry while maintaining the quality of air, water and soil. 5. Enhancement of Craftsmanship while protecting art and culture and measures to restore sites of historical importance and national heritage and
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promoting the works of art and setting up of public libraries. 6. Steps to bring worthy to the part of war windows, armed force veterans and their departments. 7. Sports programs and training sessions to enhance the level of rural sports, nationally recognized sports, Para Olympic sports and Olympics sports.
8. Favoring to Prime Minister’s National Relief Fund and contribution to other fund set up by the Central Government to promote socio-economic development and welfare of the schedule castes and Schedule Tribes and for supporting backward classes, minorities and women.
9. To uplift the technology of incubator that’s comes under academic institutions and which are approved by the Central Government. 10. Introducing varied projects for Rural Development. The below activities doesn’t include under the CSR activities of the Company. 1. Business run in the normal course. 2. Outside the territory of the India or abroad. 3. For the welfare of tshe employees and their families. 4. Political party contribution of any amount directly and indirectly as defined u/s 182 of the Act. The above CSR activities shall be undertaken by the Company, as per its stated CSR policy, in consonance with the new or ongoing projects excluding activities undertaken in pursuance of its normal course of business. The Board of Directors may decide to undertake its CSR activities approved by the CSR Committee, through a registered trust or a registered society.
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III.
CSR AND IMPORTANCE
Corporate Social Responsibility: People, Planet and Profit It is important to understand the legislative, policy and institutional frameworks that govern contemporary CSR and CSI practices and programmes, and how concepts like the Triple Bottom Line – understood in the context of People, Planet, Profit (Social, Environmental, Financial measures) – can benefit the organisation and the environment in which the company conducts its business. Previously, the terms CSR and CSI were used interchangeably. Subsequently, with the change of the corporate and Broad–Based Black Economic Empowerment landscapes, these terms have been defined separately. Corporate Social Investment is a sub-component of Corporate Social Responsibility. Let’s begin by defining CSR. CSR refers to an organisation’s total responsibility towards the business environment in which it operates. CSR encompasses a broader solution to the Triple Bottom Line mentioned above. The term ‘Corporate Social Responsibility’ came about in the late 1960′s and early 1970′s after many multinational corporations used it to describe organisational activities that impacted their responsibility towards the greater environment. CSR originated in philanthropy. Currently it supports projects external to the normal business activities of a company that are not directed towards making a profit. Typically, such projects have a strong developmental approach and utilise company resources to benefit non-profit organisations and communities. CSR spend must not be confused with marketing spend, which is utilised to promote the profile of the company brand. CSR Standards and Practices ISO 26000 is the recognised international standard body for CSR. The ISO 26000 standards benefit CSR because they provide clarity on an organisation’s concepts, terms and definitions related to social responsibility. ISO 26000 intends
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to assist organisations in contributing to sustainable development. The standards provide insight into trends and characteristics of social responsibility. ISO 26000 therefore aims to integrate, implement and promote socially responsible behaviour throughout the organisation and in its engagement with its stakeholders. It is important for businesses not only to provide products and services to satisfy the customer, but also to ensure that the business is not harmful to the environment in which it operates. In order for an organisation to be successful, the business must be built on ethical practices. Companies are increasingly pressurised to behave ethically. This pressure comes from customers, consumers, governments, associations and the public at large. ISO 26000 was created with this in mind, to provide guidance on the international standards on CSR. It is intended for organisations in both public and private sectors, in developed and developing countries. These standards motivate businesses to go beyond legal compliance, recognising that compliance with the law is a fundamental duty of any organisation and an essential part of their social responsibility. Being trustworthy and transparent, however, increases consumers’ preference for a company and its product or service. The King Report on Corporate Governance (South Africa 2009 – King III) promotes good social and environmental practices as part of good corporate governance. It is closely aligned with the standards for international corporate governance. The JSE (Johannesburg Stock Exchange) Securities Exchange prescribes compliance with King III for listed companies. CSR focuses on achieving economic success through responsible corporate governance in a company’s core area of business. CSR pushes organisations to do better because their actions affect customers, suppliers, employees, shareholders and the community at large. Partnerships with the communities, particularly those that have been disadvantaged, can help companies build productive relationships and stimulate economic growth in disadvantaged areas.
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Around the world, companies are motivated to make their business decisions more sustainable by applying the principles of CSR within their organisations. Examples include the protection of human rights, drawing up and implementing employment and environmental standards, and minimising corruption. Choosing the Right Corporate Social Investment (CSI) Strategy Corporate Social Investment is a strategically focused investment in bringing about meaningful transformation that is in line with core business objectives.
However, not all businesses operate in the same way. Ethical companies that relocate their manufacturing facilities to developing countries must not tolerate certain practices that are acceptable in some of those countries, such as child labour, poor health and safety, poverty-level wages and coerced employment. It is important for companies to understand the importance of operating ethically and to measure their success by more than just profitability. Corporate Social Responsibility is more than just philanthropic activity. There must be measurable and sustainable action with each program that is implemented.
There are four CSI strategies, organisations usually fit in one of the following
IV.
These include: Obstructive strategy – these are companies that meet economic demands; Defensive strategy – these are companies that meet economic and legal responsibilities; Accommodative strategy – these are companies that meet economic, legal and ethical responsibilities; and Proactive strategy – these are companies that meet economic, legal, ethical and discretionary responsibilities. It must be the goal of every organisation to use a proactive strategy where they do what is right, meet legal obligations and contribute to the community, while still making a profit. A well-known example of this strategy is the Tylenol case in 1982. Johnson & Johnson spent over $100 million dollars recalling Tylenol, its best-selling product, after someone tampered with bottles of the painkiller. The result was a rise in consumer confidence despite the contamination scare. Companies that operate with business ethics have a competitive advantage because consumers are more willing to trust ethical brands and remain loyal to those products, even during difficult periods.
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CSR IMPLEMENTATION
The following is a list showing Implementation There is no “one-size-fits-all” method for pursuing a Corporate Social Responsibility (CSR) approach. Each firm has unique characteristics and circumstances that will affect how it views its operational context and its defining social responsibilities. Each will vary in its awareness of CSR issues and how much work it has already done towards implementing a CSR approach. That said, there is considerable value in proceeding with CSR implementation in a systematic way—in harmony with the firm’s mission, and sensitive to its business culture, environment and risk profile, and operating conditions. Many firms are already engaged in customer, employee, community and environmental activities that can be excellent starting points for firm-wide CSR approaches. CSR can be phased in by focusing carefully on priorities in accordance with resource or time constraints. Alternatively, more comprehensive and systematic approaches can be pursued when resources and overall priorities permit or require. The bottom line is that CSR needs to be integrated into the firm’s
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core decision making, strategy, management processes and activities, be it incrementally or comprehensively. The impulse for harmonization also stems from the wider social context. As will be described below, there are a number of governmental and partnership developed initiatives that have emerged to provide guidance on governmental and societal expectations of business. By using these instruments—such as the OECD MNE Guidelines or the UN Global Compact—business users can be confident that they are basing their efforts oninternationally-endorsed approaches. What follows below is a broad framework for implementing a CSR approach that builds on existing experience as well as knowledge of other fields, such as quality and environmental management. The framework follows the familiar “plan, do, check and improve” model that underlies such well-known initiatives as those of the International Organization for Standardization (ISO) in the areas of quality and environmental management systems. The framework is also intended to be flexible, and firms are encouraged to adapt it as appropriate for their organization.
CSR in Small Business Enterprises
Consider using one of the many existing selfassessment tools and check-lists. Another good resource is industry associations, which quite frequently take leadership roles on issues such as CSR and may offer assistance with selfassessments. Consider working with a non-profit organization to conduct an eco-audit, or hiring a student or consultant. The main objective is to review current business practices to identify activities that fall under the heading of CSR (e.g., recycling), as well as potential activities (e.g., purchasing products from developing countries where workers are paid living wages or that protect core labor rights). A key resource to draw on in this regard is staff. As the front-line personnel carrying out the functions of the business, employees are often very aware of a number of ways in which the firm’s activities affect stakeholders, and frequently have suggestions for improvement. Sample CSR small business checklist Can we: • Provide a safer working environment educational assistance to employees?
and
• Improve contractual relations with employees?
“This seems like too large a task to undertake. We have very limited time and resources in our small office. How can we find all this CSR information and still focus on our day-to-day operations?” The small size of operations may make it easier to find information on actual and potential CSR activities and impacts. Also, many small businesses operate closely with local communities and understand the issues. Assign one person to create a checklist (see below), with input from other employees, of all the CSR activities and initiatives that the company might put in place, and check off what it is already doing, noting any gaps.
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• Enhance gender equality in the workplace? • Use more energy-efficient appliances (e.g., light bulbs) or vehicles? • Source more from local suppliers? • Improve customer service standards? • Support more local community projects? • Purchase fair trade products that support workers in developing countries? • Recycle more waste?
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• Ensure a better work/life balance for employees? than a lakh at inception, the Gross Premium went up to Rs.58 crores in 1973 and during 2015-16 the and figure stood at a mammoth Rs. 8611 crores. • Be more accessible to customers of various abilities? CSR SET UP BOARD LEVEL
V.
The Company has constituted CSR (Corporate Social Responsibility) Committee, a SubCommittee of the Board of Directors of the Company as detailed below:
CSR CASE STUDY
Oriental Insurance The Oriental Insurance Company Ltd was incorporated at Bombay on 12th September 1947. The Company was a wholly owned subsidiary of the Oriental Government Security Life Assurance Company Ltd and was formed to carry out General Insurance business. The Company was a subsidiary of Life Insurance Corporation of India from 1956 to 1973 ( till the General Insurance Business was nationalized in the country). In 2003 all shares of our company held by the General Insurance Corporation of India have been transferred to Central Government. The Company is a pioneer in laying down systems for smooth and orderly conduct of the business. The strength of the company lies in its highly trained and motivated work force that covers various disciplines and has vast expertise. Oriental specializes in devising special covers for large projects like power plants, petrochemical, steel and chemical plants. The company has developed various types of insurance covers to cater to the needs of both the urban and rural population of India. The Company has a technically qualified and competent team of professionals to render the best customer service. Oriental Insurance made a modest beginning with a first year premium of Rs.99,946 in 1950. The goal of the Company was “Service to clients” and achievement thereof was helped by the strong traditions built up overtime. ORIENTAL with its head Office at New Delhi has 31 Regional Offices and nearly 1800+ operating Offices in various cities of the country. The Company has overseas operations in Nepal, Kuwait and Dubai. The Company has a total strength of around 14,000+ employees. From less
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S.No. Name
Position Company
in Position in Committee
1
Chairman-cumShri Sujay Managing Director Banarji (Officiating)
Chairperson
2
Shri Jatinderbir Singh
Director
Member
3
Dr. N. Srinivasa Director Rao
Member
4
Ms Mudita Director Mishra
Member
5
Shri V. E Director Kaimal
Member
6
Shri Atul General Sahai Manager(CSR)
Invitee Member
CORPORATE LEVEL A Corporate CSR Department under the guidance and supervision of GM (CSR) has been set up to implement, monitor and evaluate CSR Activities undertaken in the Company. For implementation of various CSR activities, the services of Regional Offices of the Company spread across the Country are also utilized for implementation of CSR
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initiatives in various parts of the Country and in selection of implementing agencies.
CSR Intiatives Launch The Company launched its CSR activities on its 67th Founder’s Day on 12th September, 2014 by organizing an Educational Trip for 500 children living in orphanages under control of Department of Women & Child Development at Azad Hind Gram, an historic and education spot under control of DT & TDC, housing a museum on famous freedom fighter, Netaji Subash Chandra Bose, and giving an insight on heroic activities of Azad Hind Fauz which played an important role in India’s freedom fight against British Government. The children also participated in many activities such as Camel Cart ride, Rope walking, Potter’s wheel, besides watching a film on life of Netaji Subash Chandra Bose. The children also gained knowledge on cleanliness, brotherhood and harmony. The Company also arranged for refreshments and distributed educational books amongst the children, who left the venue with a promise to COME BACK AGAIN. For The Oriental Insurance Company Limited, Corporate Social Responsibility philosophy is delineating its responsibility as a Corporate Citizen and laying down the guidelines and mechanism for undertaking socially useful programmes for welfare & sustainable development of the community. The Vision for CSR is that ORIENTAL INSUARANCE, in its role as a socially responsible corporate citizen will endeavor to participate in programmes that benefit the society at large and also those who need special assistance. The objective of ORIENTAL INSURANCE for CSR is to operate its business in an economically, socially & environmentally sustainable manner, to directly or indirectly take up programmes that benefit the community, enhance the quality of life for people for whom the specific programme is designed, to create community goodwill and enhance its socially responsible image .
CSR ACTIVITIES UNDERTAKEN IN 2014-15 1.
Contribution to Swachchh Bharat Kosh for building of toilets in schools under Swachchh Bharat Abhiyan. Contribution to Swachchh Bharat Abhiyan 2. Contribution to Andhra Pradesh Chief Minister’s Relief Fund for relief and rehabilitation of victims of HUDHUD Cyclone.Contribution to Andhra Pradesh Relief Fund 3. Educational trip to Azad Hind Gram for Children of orphanages run by Department of Women & Child Development, Government of Delhi.Educational trip to Azad Hind Gram 4. Educational trip to National Science Museum for Children of Lady Noyce School for hearing impaired run by department of Social Welfare, Government of Delhi.Educational trip to National Science Museum 5. Educational trip to Akshardham for Children of Nirmal Chanyya Home for girls run by Department of Women & Child Development, Government of Delhi.Educational trip to Akshardham 6. Educational Trip ( Second Time) for the children of orphanages of all homes (run by the Delhi Government, Department of Women and Child Development) who could not earlier attend the trip to Azad Hind Gram, New Delhi.Refer to Pic Azad_Hind_Gram 7. Contribution made to the Relief and Rehabilitation Jammu and Kashmir in the supply of food and clean CM_ReliefFund_JK
CM’s Relief Fund for of victims of floods in area of Medical Aid, water. Refer to Pic
8. Contribution made to CM’s Relief Fund for the Relief and Rehabilitation of victims of floods in
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Assam in the area of Medical Aid, supply of food and clean water. 9. Contribution made to CM’s Relief Fund for the Relief and Rehabilitation of victims of floods in Meghalaya in the area of Medical Aid, Supply of food and clean water. Refer to Pic ReliefFund_Assam_Megh
10.
Contribution made in ‘Clean Ganga Fund’ for ‘Namami Gange Mission’. Refer to Clean_Ganga_Fund 11. Providing infrastructure facilities like , Air conditioners, Refrigerator, fans, Water Coolers, Water Purifier and Chappati Making Machine to senior citizens of ‘Arya Mahila Ashram’ at New Rajinder Nagar and ‘ Old Age Home, Bindapur run by Delhi Government. Refer to Pic Arya_Mahila_Ashram
Attract, retain and maintain a ‘Happy workforce and be an Employer of Choice’ Save money on energy and operating costs and manage risk Differentiate yourself from your Competitors Generate innovation and learning and enhance your influence Improve your business reputation and standing Provide access opportunities
to
investment and funding
Generate positive publicity and media opportunities due to media interest in ethical business activities
VI.
DELIVERABLES
12. Organizing Health Camps (Personal and Veterinary) across the Country. Refer to Pic Health_Camps
With the understanding the each client’s program design is adjusted to fit their unique needs, typical deliverables include the following:
Why do we need CSR?
A structured volunteering and giving program, including:
Consumers increasingly don't accept unethical business practices or organisations who act irresponsibly. Advances in social media (giving everyone a voice) mean that negative or destructive practices quickly fuel conversations online. Organisations are accountable for their actions like never before.
A complete measurement framework “Assessment Questionnaire” and Scorecard
with
Required policy and procedure materials Administrative infrastructure (Leveraging the interests and skills of employees)
The Business Benefits of CSR CSR should not be viewed as a drain on resources, because carefully implemented CSR policies can help your organization:
The right technology to support the program objectives and processes Reward and recognition programs
Win new business
Vetted partners
Increase Customer retention
Strong communications strategies and materials for internal promotion
Develop and enhance relationships with Customers, Suppliers and create networks
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Opportunities for giving and volunteering that fit employee interests and levels of available commitment A high level of engagement with employee support tools such as workplace giving software Opportunities for giving and volunteering that are engaging and increase levels of employee satisfaction
Company’s net worth globally which is gaining momentum. References Official website : The oriental Insurance Company Ltd. Oriental Insurance Company Annual Reports
Enthusiastic employee volunteers on the ground, providing support to CSR staff, prepared to recruit and support other volunteers.
Arthaud-Day, M.L. "Transnational Corporate Social Responsibility: A Tri-Dimensional Approach to International CSR Research." Business Ethics Quarterly 15 (2005): 1–22.
A program that is highly inclusive, extending beyond “corporate” and empowering the individual passions of employees.
Carroll, A.B., and A.K. Buchholtz. Business and Society: Ethics and Stakeholder Management. 5th ed. Australia: Thomson South-Western, 2003. Garriga, E., and D. Mele. "Corporate Social
IX.
CONCLUSION
Responsibility
Theories:
Mapping
the
Territory." Journal of Business Ethics 53 (2004): In conclusion, by becoming a good corporate citizen, an Organization can improve its competitive edge in respect of attracting and retaining investors, clients and employees. If carefully aligned to the core business strategy (as well as to company and industry charters from Broad–Based Black Economic Empowerment, Social Responsibility Index and Global Reporting Index), organizational CSR and CSI strategies can maximize opportunities for Indian and International Corporate(s). This will enable them to go beyond compliance and a ‘tick-box’ exercise, to good corporate citizenship and sustainability.
51–71.
It is no doubt that Social CSR Audit being conducted as per bench mark laid down. There would be also in future similar to ICRA, CRISIL, A.M.BEST and S&P Credit Rating agencies meant to measure the CSR level at both listed Companies, Unlisted Companies which reflecting the confidence of common citizen as well share holders to benchmark the CSR rating and
A
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Marquez, A., and C.J. Fombrun. "Measuring Corporate
Social
Responsibility." Corporate
Reputation Review 7 (2005): 304–308. Post,
J.E.,
A.T.
Lawrence,
and
J.
Weber. Business and Society. 10th ed. Boston: McGraw-Hill, 2002. TVS Group CSR http://www.tvssst.org/faq.php to
Z
of
CSR
http://as.wiley.com/WileyCDA/WileyTitle/produc tCd-0470723955.html
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Industry 4.0 Premanand Narasimhan FIET, MIEEE, MBCS (UK), FIoD (Ind) Chennai, India premvn@gmail.com
Abstract— The term "industry 4.0" refers to the
concept of factories in which machines are augmented with wireless connectivity and sensors, connected to a system that can visualise the entire production line and make decisions on its own. I.
INTRODUCTION
The term Industry 4.0 was first publicly published in 2011 as “Industrie 4.0” by a group of representatives from different fields (such as business, politics, and academia) under an initiative to enhance the German competitiveness in the manufacturing industry This era is in the midst of a significant transformation regarding the way we produce products. This transition is so compelling that it is being called Industry 4.0 to represent the fourth revolution that has occurred in manufacturing. From the first industrial revolution (mechanization through water and steam power) to the mass production and assembly lines using electricity in the second, the fourth industrial revolution will take what was started in the third with the adoption of computers and automation and enhance it with smart and autonomous systems fueled by data and machine learning.
II.
DEFINITIONS
IoT IoT is short for Internet of Things. The Internet of Things refers to the ever-growing network of physical objects that feature an IP address for internet connectivity, and the communication that occurs between these objects and other internet-enabled devices and systems. Your Wi-fi doorbell, or smart refrigerator are everyday examples of IoT devices. IIoT Industrial Internet of Things (IIoT) is a subset of IoT, aimed specifically at industrial applications. IIoT is about connecting machines to other machines/data management and the optimization and productivity that is possible to make “smart factories.” Industry 4.0 This is a phrase coined in Europe. It means the same as IIoT and refers to the fourth industrial revolution, The term is interchangeable with IIoT and is now recognized globally.
Industry 4.0: Sub Components To take full advantage of IoT, IIoT, and Industry 4.0 benefits, there are several components that must first be understood. More about these components are given below..
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1. The Cloud Utilizing the intranet to access data at any • location where internet connectivity is possible, the cloud is an IT paradigm. Moving from conventional servers to the cloud empowers• the availability of data wherever and whenever needed. Furthermore, the cloud enables companies to focus on their core expertise rather than investing large sums of money on computer infrastructure and maintenance. Cloud computing relies on the sharing of resources to achieve economies of scale, like a public utility.
2. Sensors And Connected Devices Just about every product bought today is equipped with an IP address. Those who use Nest at home, view home cameras from a mobile phone, or start a car from an app are all using IOT. IIOT provides the same capabilities, but for larger pieces of equipment. This typically requires integration into corporate software like Manufacturing Resource Planning (MRP), Product Lifecycle Management (PLM), or asset management software. In some cases the equipment is older and might not have internet connectivity; thankfully, there are numerous sensors available on the market for making old equipment compatible.
Phone/Tablet: When you hold up your device to view a piece of equipment, a digital overlay can provide additional data regarding that equipment, KPI’s, Graphical Data, Schematics, Graphical Data, and Digital twin data, among others. Assisted Reality Wearable: Like Google Glass, this displays an image of computer screen typically to one eye, providing on-the-spot data. Immersive Augmented Reality Wearable: Typically, this involves glasses that attempt to cover your most of your viewing field, with the potential to show KPI’s, graphical data, schematics, and digital twin data, among other functionality.
It doesn’t take much to notice that phone, tablet, and immersive AR wearables share many of the same display opportunities. Phones and tablets are excellent in many use cases, but the distinguishing factor comes in when you need to perform work at the point-ofuse. In these cases, wearable devices are easier to utilize.
4. Artificial Intelligence Artificial Intelligence (AI) is the phenomenon of computer and machine learning. Devices are now available that recognize their environment and begin to take actions based on that environment to maximize achieving a goal or a result. AI is also now being used to recognize and separate parts (sorting good from bad parts). We have seen it in the food industry for years, but it is now capable of sorting parts by size or in so cases a good part vs a bad part.
3. Augmented Reality 5. Big Data Data is available almost everywhere, which also increases our urge to be able to view it almost anywhere. Providing data with context almost immediately makes it more meaningful. This is where augmented reality (AR) comes in, and it can be implemented in many variants:
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Big data refers to data sets that are so big and complex even traditional data-processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis,
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search, sharing, transfer, visualization, querying, updating, information privacy, and data source.
6. Digital Twin Digital twin is a digital representation of a physical asset. Digital twins can be used to show how an item is serviced. This can also be used in combination with AI tool sets, software analytics, and real-world data to create living digital simulation models that update and change along with their physical counterparts.
III. KEY COMPONENTS OF INDUSTRY 4.0 The key components of successful implementation of Industry 4.0 are given below. Every organization large or small should concentrate and inculcate these components in every activity that they undertake to conduct the business, most important are points A, D,F and G. F. Greater Customization Manufacturing. ...
through
Additive
G. Full Integration of Advanced Analytics. ... H. A Move Beyond Postmodern ERP. ... I. Widespread Incorporation of the Internet of Things. ... J. Increased Reliance Upon the Cloud. ... K. Autonomous (and Cooperative) Robots. ... L. Enhanced Cybersecurity.
7. Cybersecurity M. Effective Risk Management
The increased demand for cloud and internetbased services increases the need for protection of computer systems from theft of or damage to their hardware, software or electronic data, as well as from disruption or misdirection of the services they provide. Cyber security includes controlling physical access to system hardware, as well as protecting against harm that may be done via network access, malicious data and code injection.
N. Discipline and Commitment. O. Fairness to Employees and Customers. P. Transparency and Information Sharing. Q. Corporate Social Responsibility. R. Regular Self-Evaluation
IV. BENEFITS OF INDUSTRY 4.0
Predictive Maintenance 8. Additive Manufacturing and Digital Scanning The significant price reduction of digital scanners and 3D printers enables much faster prototyping of products/product development. A few large companies are now looking to use 3D printing in production, allowing more complex parts to be made in significantly less time.
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Years of machine data analysis reveal events that have triggered failures in equipment, coupled with real-time monitoring, can warn of performance trends. This can provide advanced warnings when pieces of equipment are about to fail. In turn, workers can schedule equipment maintenance at a convenient time rather than reactive maintenance when the machine crashes and the assembly line comes to a stop.
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Demand Prediction
Costs
Big data analysis can review market trends associated with your commodity, AI can then assist with inventory review and tracking market pricing. The result is having more accurate demand prediction, along with the ability to buy at market lows. This ultimately results in improved margins.
Given how great this all sounds, you may be starting to think about the associated costs. Many people get to this point and assume the cost is prohibitive. But the fact is that some of your new machines may already be compatible. Cost is entirely dependent on each situation and facility, but talk to an Industry 4.0 specialist, and you may be surprised to find you can get started for as little as $20,000. This allows businesses both big and small to get up and running while still being mindful of their bottom line.
Inventory Optimization Real-time inventory management is available with scanners connected to inventory management systems. AR devices can assist with picking/kitting instructions, with potential productivity improvements up to 40 percent. Productivity Many companies utilizing AR work instructions have reported 30+ percent productivity on certain operations. Companies using this technology have also realized improved quality over paper instructions for complex tasks. Expedited And Improved Training The utilization of VR and AR in training scenarios means that someone wishing to learn a new operation can simply put on a set of glasses and instantly get guidance. Since this is an experiential activity versus reading a manual or sitting in a class, retention is improved and training time is significantly reduced. Once the training application is completed the trainer also does not have to spend all that time teaching the same class over and over.
Improved Robotics Once upon a time, robots were all caged, preventing them from contacting a shop floor worker. With the advancement in sensor technology, robotics can be used in an open environment, the combination of AI and smart robots results in robots that can adapt to challenges in their environment and make the best possible choice to accomplish a task.
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V. EMERGING TRENDS GLOBAL
Today's supply chain technology solutions address manufacturing needs in a variety of areas, including: Manufacturing Optimization. Logistics Optimization. Sales and Operations Planning. IoT is transforming almost every surface into a sensor for data collection and providing real-time insights for manufacturers. This ability to collect data from so many sources combined with increasingly powerful cloud computing is finally making big data usable. Manufacturers can slice and dice data in ways that provide them with a comprehensive understanding of their business. This enables them to improve production, optimize operations, and address issues before problems arise.
Assistive technologies, such as augmented reality (AR) and virtual reality (VR), will continue to create mutually beneficial partnerships between man and machine that positively impact manufacturers. Due to VR software interfacing seamlessly with computer-aided designs, product developers can use VR to quickly make modifications and additions to products during the product design stage before they go into modeling and manufacturing processes. AR and VR can also
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decrease inspection time and assist in detecting errors in addition to improving workers’ sight line, which enables them to complete tasks faster. For example, by using AR devices such as electronic glasses or goggles, computergenerated graphics can be placed in a worker’s field of vision that provide him with real-time help when it comes to performing a task. AR technology can also be used with cameras and sensors for training. Workers can be shown how to perform a task and use the data feed to correct mistakes, which makes it possible to quickly and effectively train unskilled workers for high-value work.
bottom line that the investment will pay for itself within just a year or two. We can hold out and wait for the technology prices to drop, but competitors might not share that mentality. REFERENCES [1] Industry 4.0 Wikipedia
Manufacturers will benefit from faster, less expensive production as a result of 3D printing. It makes rapid prototyping, which is a highly cost-effective way for product designers to test and troubleshoot their products, possible. In addition, it enables manufacturers to produce items on demand instead of having to manufacture and warehouse them. The expensive and time-consuming process of tooling for manufacturers is already being transformed by 3D printing. Historically the production of molds, jigs and fixtures used in the mass production of heavy equipment took months, was very expensive and typically involved utilizing tooling companies headquartered overseas. 3D printing makes it possible for tooling to be cost effectively completed on-site, in days, and has already been embraced by the automotive and aerospace manufacturing industries.
VI. CONCLUSION As Countries push to remain competitive with manufacturing throughout the rest of the world, it is imperative that all companies—large and small— investigate and embrace Industry 4.0. In most cases, the technology will provide such enhancements to the
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THE THREE-DAY EXPO OF LOGMAT & WAREHOUSING The three-day expo of Logmat & Warehousing was organized by Smart Expos & Fairs (India) recently in Chennai. The Expo, focused on material handling, storage, warehousing, logistics, and transportation, besides a host of other verticals, hosted 90 plus stalls on Warehousing and 70 plus stalls on Packaging. The sectors such as logistics, engineering, auto & ancillary, e-commerce, FMCG, retail, and telecom and white goods have remained the biggest demand drivers. As a result of the high demand, the logistics sector is expected to grow to $215 billion by 2020. A well-attended one-day Conference was also arranged by eCargoLog, for the benefit of the logistics fraternity wherein the stalwarts from the industry took part as speakers and participants. Prominent panelists include Mr. Ravikanth Yamarthy, Head–Training and Assessment, from Logistics Sector Skill Council and, Prof. N Chandrasekaran, of IFMR, KREA University on the topic of skill development for the logistics sector. Mr. M Pandiyan, Associate Director of BSR Affiliates spoke on the GST implications followed by Prof. Dr. K. Narashiman, Director, TVS Center, Anna University and others on warehouse operations.
The last session witnessed excellent presentation by the young group from Intelizest on Re-Imaging Business – Expanzs on Demand Space and fulfilment network by Mr. Ravichandran Nair, Associate Sales Director; IBWMS – IoT Based Warehouse Management Solutions for a smart and Hyper-efficient Warehouse by Mr A. Domnic Regies, Director of Statistics Planning and
LogIZtics – Smarter way to handle Logistics by Mr. B. Manikandan and finally with a concluding remarks, by the Director of Intelizest, Prabhu Karuppasamy. It is reported that the demand for logistics and warehousing space in India outstrips supply, as per the JLL’s latest report, titled ‘Indian Logistics and Warehousing: Tracing the Lifecycle’. The report says that the annual demand of around 32 million square feet has outstripped the supply of 31 million square feet.
In the post-lunch session on How Artificial Intelligence, IoT and Chatbots are transforming the logistics industry, Mr. Prasanna Venkatesan, Director, Digitization and Advanced Technologies, Industry Solutions Group of Oracle spoke at length. Later Mr. Prem Anand, Secretary of Cyber Society India on the subject of Past, Present, and Future of Technologies, on Warehousing.
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