Maufacturig White Paper Maagig Kowledge from Big Data Aalytics i Product Developmet
About the Authors Tushar Rajpathak Seior Cosultat, Maufacturig Iovatio ad Trasformatio Group, TCS Tushar has over 18 years of experiece i the automotive, idustrial machiery ad process domais. He has assumed multiple roles i the areas of product desig ad applicatio egieerig, across the busiess cosultig, projects delivery ad program maagemet fuctios. He has hadled ad oversee assigmets of high strategic importace across both horizotal ad vertical value chais, leadig to improved competecies ad reveue streams for the orgaizatios. Tushar has a Master s degree i mechaical egieerig from IIT, Kapur, i Idia ad is a certified Six Sigma specialist. He has completed his Gree Belt certificatio from TRW Automotive, USA (a Tier1 auto major), ad has a Black Belt certificatio from Mahidra Satyam. Atul Narsigpurkar Cosultat, Maufacturig Iovatio ad Trasformatio Group, TCS Atul has over 18 years of experiece i the automotive, idustrial machiery, electromechaical, ad sesig & cotrol domais. He has hadled multiple roles i the areas of ew product itroductio, product developmet, egieerig services, busiess process cosultig ad project maagemet. He has experiece i product desig ad developmet ad NPI, ad has maaged a umber of NPI programs. He has also maaged several strategic cosultig assigmets i the product developmet ad lifecycle maagemet domai for major automotive OEMs. Atul has a master s degree i Mechaical Egieerig from BITS, Pilai. He is a certified Six-Sigma Gree Belt.
Successful ew product itroductio is the key to busiess success. I these recessioary times, it has become icreasigly importat for all orgaizatios, big or small, to develop ad itroduce ew products more effectively. This ca be doe through a agile ad successful New Product Developmet (NPD) process that focuses o retrievig ad usig iformatio ad kowledge cotiuously to tackle challeges. Orgaizatios have recogized the importace of kowledge maagemet i New Product Itroductio (NPI). Typically, a NPI process uses a combiatio of data, iformatio ad kowledge as iputs durig the various stages. I recet years, orgaizatios have realized the eed to tap ito the data geerated from various sources, ad use it i their NPD processes, sice such data offers huge potetial to improve product performace, brig about efficiecy i the product developmet process, cotai costs, ad ehace customer experiece. I order to beefit from this data, orgaizatios must have a well-defied strategy to collect, store, sythesize, ad dissemiate it i the form of kowledge required for various busiess fuctios. For example, product ideas, customer behavior patters, Voice of Customer (VoC) data, Quality Fuctio Deploymet (QFD) data, ad product treds from social etworks ad listeig platforms, ca help desig product strategy ad portfolio. Warraty, quality ad testig data, data from Computer Aided Desig (CAD) systems ad maufacturig process data ca help i product desig ad validatio, as iformatio fed back ito the NPD process. Orgaizatioal kowledge for developmet of products is ot available i a simple format, but is geerally i large volumes, ad is dispersed across the eterprise. This paper explores a approach to maage kowledge from aalytics ad Big Data i the NPD process for the maufacturig idustry, particularly i the cotext of the automotive domai.
Cotets Itroductio 5 Data, Iformatio ad Kowledge 5 Kowledge Maagemet is Itegral to Product Developmet 6 Emergig Treds 7 How Big Data ca help i NPD 8 Utilizig Product Data 9 Listeig to the Voice of the Customer 10 Tappig Iteral ad Exteral Stakeholders 10 Big Data i Actio 10 Future Prospects: The automotive idustry leadig the way 12 Coclusio 13
Itroductio The rapid rise of emergig ecoomies, icreasig demad for products that ca cater to the chagig prefereces of both local as well as global markets, ad the growig importace of iformatio techology throughout a product s lifecycle, has led to the commoditizatio of products. The cosumer is kig ad drives the success or failure of corporatios. Additioally, i order to have a tailored product portfolio to cater to differet markets, orgaizatios eed to have a clear uderstadig of customer requiremets i differet segmets ad must desig products which meet these expectatios. Addig to the complexity is the cut-throat competitio i the global market, which brigs together similar products ad thus reduces the scope of differetiatio. Owig to these treds, time to market, first time right, ad cost competitiveess, are emergig as key differetiators for Origial Equipmet Maufacturers (OEMs) operatig i highly competitive idustry segmets such as automobiles, cosumer goods ad cosumer electroics. Today, the race is ot just about creatig ew products, but doig so faster tha the competitio. Hece, orgaizatios eed to leverage curret ad past marketig iformatio, desig data, maufacturig operatios data, ad testig ad service data, to build a costatly evolvig kowledge repository that they ca tap ito to create ew products quickly. Data, Iformatio ad Kowledge It is importat to make a distictio betwee data, iformatio, kowledge ad wisdom i cotemporary kowledge literature (see Figure 1). Data is a set of discrete ad objective facts about evets. Iformatio is a message, usually i the form of a documet or audio-visual commuicatio. As with ay message, it has a seder ad a receiver. Iformatio is meat to chage the way the receiver perceives somethig, ad to impact their judgmet ad behavior. Kowledge is broader, deeper, ad richer tha data or iformatio. Kowledge emerges from the applicatio, aalysis, ad productive use of data ad/or iformatio. Wisdom Kowledge Explict vs. Tacit Iteral vs. Exteral Idividual vs. Collective Iformatio Data Figure 1: Kowledge Maagemet Pyramid 5
The core of the product developmet process lies i kowledge ad its reuse. Kowledge, whe maaged effectively, ca help reduce NPI project time, improve product quality, ad icrease customer satisfactio. I a kowledge-based orgaizatio, it plays a crucial role i guidig the orgaizatio s actios ad establishig a [1] sustaiable competitive advatage. Typically, the NPI process uses a combiatio of data, iformatio ad kowledge. Wisdom o the Kowledge Maagemet (KM) pyramid comes from the expertise ad isight of a few idividuals i the orgaizatio, ad is usually maifested as best practices ad lessos leart artifacts.[2] Kowledge Maagemet is Itegral to Product Developmet The covetioal product developmet process (Figure 2) starts from strategizig the product to desigig, validatig ad testig it, ad fially eds with the product beig phased out of course, after a desigated period of after-sales service. Product Strategy ad Plaig Cocept, Prototypig ad Defiitio Product-Desig ad Aalysis Product & Process Verificatio & Validatio Maufacturig, Lauch, Service ad Phase Out Figure 2: Product Developmet Lifecycle Durig the product developmet process i the maufacturig idustry, several types of data are geerated, ofte i large volumes, which may be processed ad the used as a reusable kowledge base, ad to provide real time itelligece to the product developmet teams. I some cases, data such as customer isights (structured ad ustructured), competitive itelligece ad product performace data, etc. ca prove to be of great value for the overall success of the product. Despite the volume of research available o kowledge maagemet, little work has bee carried out i the area of itegratio of kowledge from the above metioed data elemets, ito the product developmet process. Hece, maagig this kowledge sourced from various data sources ad usig it i the product developmet process, offers great potetial for improvig process efficiecies. [1] Shai, A.B., Sea, James A., Kowledge Maagemet ad New Product Developmet: Learig from a Software Developmet Firm (Oct 2000), accessed Aug 1, 2013, http://ceur-ws.org/vol-34/shai_sea.pdf [2] Maagemet, Kowledge ad Learig, Iteratioal Coferece 2012, Peter Meza, Product Maagemet ad Kowledge Maagemet (2012), accessed Aug 1, 2013, http://www.issbs.si/press/isbn/978-961-6813-10-5/papers/ml12_163.pdf 6
Emergig Treds i Product Developmet The maufacturig idustry, particularly the automotive segmet, is rapidly evolvig due to the digital wave that is takig over idustry. Based o our observatios of the emergig ladscape, we believe that covetioal product developmet processes are pavig the way for ewer ad more robust ways to esure customer acceptace of a product log before it is lauched that is, while it is beig desiged. Table 1 lists a few examples. Treds Examples/ Implicatios Crowdsourcig ad social media playig a big role i collaborative product developmet Fiat Mio is the world's first crowd-sourced car[3], developed based o ew ideas sourced from users via a social media platform. While this tred will chage the way products will be desiged i the future, orgaizatios will also have to collate, classify, ad assess large amout of data, ad feed it back ito the product developmet process. Big Data aalytics closeloopig with kowledge base May product developmet orgaizatios, icludig auto OEMs, are critically aalyzig Big Data (product failure data, service data, warraty data, historical desig data, materials data etc.) to extract iformatio patters tha ca be fed back ito the product developmet process. CAD/CAE/PLM techologies ow have capabilities to itegrate various extracted kowledge elemets ito the product desig process. Iteral ad exteral collaboratio for kowledge sharig May leadig auto OEMs i North America ad Europe have developed collaboratio platforms with features icludig blogs, wikis, chats, commuities of practice, expert corers ad alerts amog others to allow employees to share kowledge through discussio threads, idea votig etc. While collaboratio platforms are becomig extremely popular withi orgaizatios, i some cases they are also beig exteded to exteral users with appropriate security features. Such collaboratio platforms will allow fuctioal teams to work i collaboratio rather tha workig i silos. Orgaizatios will eed to have busiess processes ad supportig ifrastructure i place to eable such collaboratio platforms ad process the iformatio shared o these platforms. Use of telematics i assimilatig kowledge for product developmet Sesig devices itegrated with remote moitorig devices are beig used to gather itelligece o product performace, health diagostics, usage patters etc. Maufacturig orgaizatios are usig these techiques to establish a correlatio betwee field test data poits to be simulated i the lab ad the build these correlatios i mathematical models. Maufacturig orgaizatios must work o itegratig the kowledge ad data geerated i this way ito the core product developmet process to eable faster ad right decisio makig. Bakig kowledge ito the product developmet process Maufacturig orgaizatios the world over are realizig that tacit kowledge available amog curret employees should be formalized ad captured for posterity. Global auto OEMs are gearig up to put busiess processes ad the required ifrastructure i place to capture this tacit kowledge i terms of best practices/lessos leared etc., ad close-loop the kowledge maagemet process by itegratig these best practices with CAD/PLM tools. Table 1: Emergig Treds i Product Developmet ad Kowledge Maagemet Processes [3] Wired, Fiat releases details of first ever crowd-sourced car (Aug 2010), accessed Aug 1, 2013, http://www.wired.co.uk/ews/archive/2010-08/18/fiat-mio 7
How Big Data ca help i NPD A orgaizatio geerates ad stores a eormous amout of data o a regular basis. This is the result of the desig, aalysis, testig ad service operatios of products, ad is ofte referred to as Big Data. Before we aalyze how this data is trasformed ito kowledge, let us uderstad it further. Big Data is usually defied by three dimesios volume, velocity ad variety. Recetly Oracle itroduced a fourth dimesio value. These four Vs[4] may be elaborated further as follows: Volume: Machie-geerated data is produced i much larger quatities tha o-traditioal data Velocity: This refers to the speed of data processig, or the low latecy rate at which aalytics must be applied to the data, ad looped back to the origial sources of data to actio.. Social media data streams while ot as massive as machie-geerated data produce a large iflux of opiios ad relatioships valuable to customer relatioship maagemet Variety: This refers to the large variety of iput data (customer isights, competitive itelligece, treds, bechmarkig data, stadards, materials, etc.) which i tur geerates a large variety of data (CAD/CAM/CAE data, drawigs, documets, test data, product failure data, product ad process performace data etc.) as output. Value: The ecoomic value of differet data varies sigificatly depedig upo both the source ad its ed use The rapid use of electroics ad IT i products durig the last two decades has resulted i may orgaizatios sittig o a eormous amout of data direct ad idirect, ad/or past ad preset. Most IT systems used for product desig, aalysis, egieerig, testig, ad validatio processes geerate a large volume ad variety of data. The icreasig proliferatio of computers ad embedded electroics i the maufacturig idustry, particularly i the automotive segmet, has further resulted i the geeratio of a huge amout of structured ad ustructured data. For istace, use of computers ad sesors i moder vehicles geerates eormous data while a vehicle is i use. The emergig challege for orgaizatios is to derive meaigful isights from available data ad re-apply it itelligetly. Kowledge maagemet plays a crucial role i efficietly maagig this data ad deliverig it to the ed users to aid i the product developmet process. This ivolves collectio of data from direct ad idirect sources, aalyzig ad sythesizig it alog with relevat eterprise data, to derive meaigful iformatio ad itelligece, covertig it ito a useful kowledge base, storig it ad fially deliverig it to ed users. To derive the maximum beefit from Big Data, orgaizatios must modify their IT ifrastructure to hadle the rapid rate of delivery ad extractio of huge volumes of data, with varyig data types. These ca the be itegrated with the orgaizatio s eterprise data ad aalyzed. I fact, orgaizatios, especially those with legacy systems, eed to have a clear uderstadig of their past data ad how the same ca be merged ito their curret IT systems. [4] Oracle, Big data for the Eterprise (Ju 2013), accessed Aug 2013, http://www.oracle.com/us/products/database/big-data-for-eterprise-519135.pdf 8
Accordig to Yua, Yoo, ad Heledar,[5] kowledge areas are classified ito four types, collectively referred to as MH-T-P: Market kowledge, Huma kowledge, Techology kowledge, ad Procedural kowledge. Based o these four kowledge areas, Table 2 depicts the mappig with elemets of Big Data i the product developmet process: Kowledge Type Market Kowledge Volume Customer Data Competitor Data Velocity Direct Iteractios Social Media Surveys Variety Huma Kowledge Experiece Based Collaborative Real time decisio makig Techology Kowledge Procedural Kowledge Stadards, Usage Materials Field data Desig Kowledge Aalysis Materials Std. Verificatio, Testig ad Validatio Kowledge Safety Real time data acquisitio Desig Kowledge Std. Materials Library Value Market Aalysis Demographic Data Bechmarkig Data Treds High Value Customer Data Competitor Data Skill Based Experiece Based Tacit Kowledge Heuristic Cost Reliability Packagig Ergoomics, etc. Patets Desig CAD/CAM/CAE Aalysis Maufacturig CAD/CAM/CAE Data Best Practices Test ad Validatio Service Data Maufacturig Process Data Table 2: Kowledge Areas Mapped to Elemets of Big Data Utilizig Product Data Most maufacturig compaies have IT systems to maage the product data geerated via CAD, egieerig, maufacturig, ad product developmet maagemet tools. However, the large datasets geerated by these systems have remaied utapped withi the respective systems to a great extet. These datasets, if itegrated with oe aother ad to the other eterprise systems, ca create a sigificat Big Data opportuity that the maufacturer ca haress to create value. For example, PLM could provide a platform for desig collaboratio betwee iteral ad exteral stakeholders. OEMs ca collaborate with their suppliers ad leverage the latter s skills ad kowledge to help develop products faster ad better. This way of likig useful kowledge obtaied through Big Data aalysis with rules, logics etc. ca help faster ad right-first-time decisio makig, cotai cost, improve reusability ad most importatly reduce product developmet cycle time. Product data provides iformatio about how a specific compoet was desiged ad what challeges were ecoutered. Extractig this iformatio i a usable format is the first step i the process of makig this kowledge available for reuse. The secod step is to covert or preset this kowledge i the form of busiess or techical logic/rules which ca be used by desigers, plaers, etc. durig the executio of a project. For example, orgaizatios that use cofigurators extesively fid that they ot oly help i the desig of ew parts ad assemblies, but ca also promote stadardizatio by harvestig old parts from existig databases. Data from the PD value chai ca be a eabler i the executio of such projects. [5] Yua, Q. F., Yoo, P. C., & Helader, M. G. (2006). Kowledge idetificatio ad maagemet i product desig,. Joural of Kowledge Maagemet 9
Listeig to the Voice of the Customer Orgaizatios are realizig that they also eed to tap ito the customer s voice as part of the requiremets gatherig process i ew product developmet. However, may maufacturers are yet to systematically extract crucial isights from the icreasig volume of customer data to refie existig desigs ad help develop specificatios for ew models ad variats. Best-i-class maufacturers are able to capture the data geerated from warraty claims, quality testig ad diagosis, as additioal feedback to the system. They supplemet these efforts with additioal aalysis ad correlatio of the feedback sources to develop better products. New sources of data that maufacturers are startig to mie iclude customer commets o social media platforms ad sesor data that record the actual product usage. I order to develop better products, obtai comprehesive data about customers ad gai holistic isights, maufacturers, distributors, retailers ad other players must work together ad itegrate their respective data sets with each other. For example, through a appropriate aalysis of such data, a telecom equipmet maufacturer improved its gross margi by 30 percet i 24 moths, elimiatig uecessary costly features ad addig those that were of a greater relevace to the customer, ad for which the customer was willig to pay a higher price. Tappig Iteral ad Exteral Stakeholders Makig Big Data readily available to the relevat stakeholders i a timely way ca create tremedous value. Compaies ca use Big Data aalysis to improve the performace of their R&D fuctio. Itegratig data from the R&D, egieerig, ad maufacturig uits, ca eable cocurret egieerig techiques that ca greatly reduce the waste caused from havig to rework desigs, ad thus accelerate time-to-market. To drive iovatio ad to develop products that address emergig customer eeds, maufacturers are icreasigly relyig o outside iputs gathered via iovative chaels. Some maufacturers are ivitig exteral stakeholders to submit ideas for iovatios or eve collaborate o product developmet via web-based platforms. Cosumer goods compaies such as Kraft ad Procter ad Gamble ivite ideas from their cosumers, ad have collaborated with exteral experts, icludig chemical research agecies, academics ad idustry researchers, to develop ew products. But as successful as these ope iovatio iitiatives ca be, oe key problem remais how to efficietly extract the truly valuable ideas from the potetially large umber of iputs these programs ca geerate. The challege for Big Data aalysts is to develop techiques ad algorithms that are itelliget eough to read ad aalyze this data to extract right iformatio to aid i the product developmet process. Big Data i Actio For decades, compaies have bee makig busiess decisios based o trasactioal data stored i relatioal databases. Beyod that, critical data, however, is a potetial treasure trove of o-traditioal, less structured data: weblogs, social media, email, sesors, ad photographs that ca be mied for useful iformatio. Decreases i the cost of both storage ad compute power have made it feasible to collect this data. As a result, more ad more compaies are lookig to iclude o-traditioal yet potetially very valuable data i other words, Big Data alog with their traditioal eterprise data i their busiess itelligece aalysis. 10
The use of Big Data ad its aalysis i product developmet is very closely drive by the available techologies i the orgaizatio, ad the tight itegratio betwee hardware ad software ad other data geeratio mechaisms. A Big Data strategy requires the ability to sese, acquire, trasmit, process, store ad aalyze the data to geerate kowledge that ca be stored i a repository for later use. Product Strategy ad Plaig Cocept, Prototypig ad Defiitio Product-Desig ad Aalysis Product & Process Verificatio & Validatio Maufacturig, Lauch, Service ad Phase Out Portals PDM PLM CAD CAE Simulatio Sys CAM ERP MRP CRM MES Tacit Kowledge Experietial Kowledge Market Research Competitio Data Customer Data Reverse Egieerig Tear Dow & Test Specificatios Bechmarkig Data Desig Data CAD & CAE Data Process Verificatio Test & Validatio CAM, Simulatio MRP ad ERP Data MES Data CRM (Warraty etc.) Data Storage Aalysis ad Kowledge Dissemiatio Product Strategy ad Plaig Cocept, Prototypig ad Defiitio Product-Desig ad Aalysis Product & Process Verificatio & Validatio Maufacturig, Lauch, Service ad Phase Out Figure 3: Eterprise View of Big Data Sources & Kowledge Maagemet A good approach to aalyze Big Data ad the cosequet kowledge maagemet is show i Figure 3. A typical NPD process iterfaces with eterprise-wide systems like ERP, CRM, PLM, etc., to retrieve iformatio that may be used further. This iformatio is explicit ad structured. Iformatio ad data are exchaged o a cotiuous basis with these systems as the product is beig realized. The ucovetioal, ustructured iformatio comes from several sources like simulatio, sesors, blogs, warraties, customer experiece, etc., ad it should be haressed. While a small part of this iformatio flows back ito the eterprise systems, attempts should be made to capture this i a cetral repository, typically a sigle data warehouse. A deliberate attempt must be made to keep the data together so that the data ca be combied to create iformatio which ca be aalyzed to geerate kowledge that loops back to the kowledge repository ad thece the product developmet process. 11
Some of the key beefits of a data-drive approach usig a sigle source of iformatio are: A sigle source of data drives may decisios cocurretly. For example, cotiuous availability of market data ad voice-of-customer data ca be used to improve the features ad desigs of curret ad future products. A data aalysis approach helps orgaizatios stay ahead of potetial problems, as real-time quick resolutio is possible. For example, a warraty or a field issue which is drive immediately to the database will alert product desigers to product issues which ca be elimiated while a batch is still i productio. Real- time performace data from sesors pertaiig to egie performace or driver behavior ca help product developers capture potetial performace issues or add ew features to the vehicle. Product developmet is typically a stage-gate process ad is ofte well itegrated with other busiess fuctios, utilizig iputs from customers, service, suppliers, after-market service, etc. Big Data usage i product developmet offers ew ways to itegrate data geerated from these fuctios with product desig, maufacturig, quality, warraty, diagostics, vehicle ad software specificatios as a sigle uit. We evisage a icreasig usage of systems that ca hold this data ad ru aalytics o the same to derive iformatio which becomes part of the kowledge pool. Future Prospects: The Automotive Idustry Leadig the Way The automotive OEM world is already talkig about vehicles fitted with a umber of sesors to record every miute of the performace, both of the vehicle ad of the driver. These vehicles ca provide product developmet teams with performace ad failure data i real-time from the field or from the testig facilities ad covert it ito useful iformatio which ca become part of the kowledge repository for future product developmet. I fact, solutios that itegrate the etire populatio of cars ito a sigle data base are already becomig a reality. For example, uderstadig the expected ad actual performace of two similar vehicles performig i Chia ad Europe uder differet drivig coditios will help desig the products to suit the respective eeds. The world s leadig OEMs are pioeers i the use of data ad iformatio i various aspects of their vehicle developmet program ad busiess operatios. Beig large orgaizatios that have embraced IT ad Big Data log ago, they are usig the iformatio to improve product desig, quality, cost reductio ad customer satisfactio through data-drive decisio makig ad processes. 12
Coclusio While the aim of this paper is to provide a holistic ad ed-to-ed view o how Big Data ca help i kowledge maagemet to improve the product developmet process, here are some high level takeaways for maufacturig orgaizatios plaig to embark o this jourey: Set a clear visio for kowledge maagemet i your orgaizatio Perform a thorough assessmet of your curret status, pai areas ad future requiremets Devise a well thought out log term strategy for kowledge maagemet Prepare a roadmap to achieve the visio ad successful implemetatio of strategies Implemet kowledge maagemet as a busiess trasformatio program with the right amout of focus o busiess processes, techology, ifrastructure ad orgaizatio chage maagemet. 13
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