How Subjective Clustering aids Affinity Diagram in grouping Customer needs in consumer products PDF
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How Subjective Clustering aids Affinity Diagram in grouping Customer needs in consumer products SANDHEEP KUMAR VURUKKARA BOOPAL Master of Science Thesis Stockholm, Sweden 2016 How Subjective Clustering aids Affinity Diagram in grouping Customer needs in consumer products industry Sandheep Kumar Vurukkara Boopal Master of Science Thesis MMK 2016 MF228x KTH Industrial Engineering and Management Machine Design SE-100 44 STOCKHOLM Examensarbete MMK 2016 MF 228x How Subjective Clustering aids Affinity Diagram in grouping customer needs in consumer products Sandheep Kumar Vurukkara Boopal Godkänt Examinator Handledare Anders Berglund Susanne Nilsson Uppdragsgivare Kontaktperson Creaffective GmbH Florian Rustler Sammanfattning Insamling och analys av kundernas behov är viktiga delar i produktutvecklings- och innovationsprocesser. Dessa kundbehov måste vara i en form som lätt kan kommuniceras och förstås särskilt avproduktutvecklare i ett företag. Affinity Diagram är ett vanligt använt verktyg för att strukturera kundbehov. På grund av att metoden bygger på gruppdiskussioner, finns det risk för att enskilda individers åsikter inte tas tillvara. En metod som tar hänsyn till de individuella bedömningarna är Subjective clustering, vilkenhar utvecklats för att stödja Affinity Diagram. Tidigare forskare har tillämpat båda dessa metoder i ett vetenskapligt och industriellt sammanhang och har funnit att det finns 92,5% av koppling mellan Affinity Diagram och Subjective clustering och drog slutsatsen att Subjective clustering stödjer Affinity Diagram. Det saknas forskning om huruvida Subjective clustering stödjer Affinity Diagram för konsumentprodukter. För att undersöka detta har en fallstudie på konsumentprodukter i ett produktutvecklingsprojekt i Creaffective GmbH genomförts. Studien har undersökt hur kundbehov stuktureras både från produktutvecklarensoch från kundens perspektiv. Affinity Diagram och Subjective clustering utfördes i var och en av grupperna och jämfördes. Det konstaterades att det fanns 70% av association mellan Affinity Diagram och Subjective clustering i produktutvecklingsgruppenoch 58% av association mellan Affinity Diagram och Subjective clustering med kunderna som fokusgrupp. Från analysen framgår att Affinity Diagram ensam utgör är lämplig metod för att strukturera kundernas behov för konsumentprodukter. Bakomliggande skäl till detta diskuteras i rapporten samt förslag på fortsatta studier. Master of Science Thesis MMK 2016 MF228x How Subjective Clustering aids Affinity Diagram in grouping customer needs in consumer products Sandheep Kumar Vurukkara Boopal Approved Examiner Supervisor Anders Berglund Susanne Nilsson Commissioner Contact person Creaffective GmbH Florian Rustler Abstract Collection and analysis of customer needs are important parts of product development and innovation processes. These customer needs must be in a form that can be easily communicated and easily understood especially by the R&D personnel. Affinity Diagram is one such tool to structure these data. Because of the nature of the Affinity diagram method, it is prone to biases. An alternative method that exists is Subjective clustering. It has been developed as an aid to support affinity diagram. Previous researcher has applied both these methods in a scientific and industrial context and has found that there is 92.5% of association between affinity diagram and subjective clustering and concluded that Subjective clustering aids affinity diagram. However there has been no research on whether subjective clustering aids affinity diagram in consumer products context. Taking this as a research gap, this thesis is performed, taking the Innovation project at Creaffective GmbH, as a case study. The research is conducted from both Product Development Team’s and Customer’s perspective. Affinity Diagram and Subjective clustering were separately performed with each of the group and then compared. It was found that there was 70% of association between Affinity Diagram and Subjective clustering by Product Development Team and 58% of association between Affinity Diagram and Subjective clustering by customers. It was concluded from the analysis that Affinity Diagram is the only suitable method to structure the customer needs for consumer products. Underlying reasons to the result is discussed in the thesis and further studied suggested, FOREWORD This Master thesis concludes my Master study of Integrated Product Design (Product Innovation Management) at KTH Royal Institute of Technology. Over this process of this Master thesis, there has been a number of people who have been a very important part of this Master thesis, and the reason as to why the study has been completed and stands in its place. First and foremost, I would like to heartily thank Mr. Florian Rustler, founder of Creaffective GmbH, Munich for proposing the Innovation project that served as the right platform to conduct this study and also shepherding the project, guiding me at the right and crucial stages and providing the right contacts. I would also acknowledge the participation of Isabela Plambeck and Daniel Barth in the study and also assisting in the various stages of the project. The study would not have been possible without the participation of Mr. Eashwara Krishnan, Mrs. Subashini Eashwar, Mrs. Supriya Satish, Mr Sarma, Mr. Velpari and Mrs. Bhuvaneswari who are trained trainers at Junior Chamber International (JCI), and also JCI without which I would not have known them. Their participation is definitely invaluable. I must definitely acknowledge the participation of several people who have been the source for various inputs for the study. To start with, Mr. Subramanian, Past National President of JCI India; Mr. Mohammed Nassar, Founder E2E Excite; Baalachandran Gopinath, HRD Trainer, India; Dhananjaya Hettiararchi, HR Trainer, Sri Lanka and word champion in public speaking 2014; Ankur Grover, Founder, Tinker Lab, India; Mr. Harsha, Freelance Trainer, India; Mr. Sivakumar Palaniappan, Career Coach, Masteringmind Academy, India; Mr. Jayaraman Umashankar, founder, Karna communication academy, India; Mr. Jim Clark, Design Thinking trainer, Innogreat, Taiwan; Mr. Chendil Kumar, CK consultants, India; Mr. Randy J Harvey, Keynote speaker, Bassinger & Harvey, US; Dali Han, Bosch China; Ms. Sandhya Sridhar, Mercedes Benz, India; Mr. Naveen Ramkumar, Robert Bosch, India; Mr. Satish Ramachandran, SME Head, Vodafone, India; Mr. Arjun Murali, General Electricals, India; Mr. Krishna Devarajulu, US Bank, US; Mr. Nithin Joseph, Manager, Taj Vivanta, India; Mrs. Grace Meng, Jingling Hotel Nanjing, China. Finally, I would like to express my sincere gratitude to both my supervisors Mats Magnusson and Susanne Nilsson who especially guided me during the thick and thin. My gratitude to my alma mater, KTH Royal Institute of Technology for providing me the required knowledge and making me who I am to make this happen. Sandheep Kumar Vurukkara Boopal Stockholm, December 2016 NOMENCLATURE Abbreviations AD Affinity Diagram B2B Business to Business B2C Business to Consumer HC Hierarchical Clustering JCI Junior Chamber International, an International organisation JTBD Jobs To Be Done KJ Method Also called as Affinity Diagram, founded by Jiro Kawakita OEM Original Equipment Manufacturer PDT Product Development Team PET Poly Ethylene Terephthalate QFD Quality Function Deployment SC Subjective Clustering TELCO Telecommunication TMR Traditional Marketing Research VOC Voice of the customer
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