big data in product lifecycle management · “big data” in plm are analyzed respectively in...

ORIGINAL ARTICLE

Big Data in product lifecycle management

Jingran Li1 & Fei Tao2 & Ying Cheng2 & Liangjin Zhao3

Received: 26 November 2014 /Accepted: 12 April 2015# Springer-Verlag London 2015

Abstract Recently, “Big Data” has attracted not only re-searchers’ but also manufacturers’ attention along with thedevelopment of information technology. In this paper, theconcept, characteristics, and applications of “Big Data” arebriefly introduced first. Then, the various data involved inthe three main phases of product lifecycle management(PLM) (i.e., beginning of life, middle of life, and end of life)are concluded and analyzed. But what is the relationship be-tween these PLM data and the term “Big Data”? Whether the“Big Data” concept and techniques can be employed inmanufacturing to enhance the intelligence and efficiency ofdesign, production, and service process, and what are the po-tential applications? Therefore, in order to answer these ques-tions, the existing applications of “Big Data” in PLM aresummarized, and the potential applications of “Big Data”techniques in PLM are investigated and pointed out.

Keywords BigData .Manufacturing . Product lifecyclemanagement . Potential application

1 Introduction

A large amount of digital data can be generated by peoplelinked with social networks like Twitter, Google, Verizon,

23andMe, Facebook, and Wikipedia [1]. For example,Facebook processed more than 500 TB of information everyday in 2012. Apart from the social network area, online busi-ness has also witnessed a stunning growth in the last few yearssuch as Tmall (Alibaba), which made over 300 billion RMBin sales on November 11, 2013, the “Bachelor Day” celebrat-ed by young single Chinese. Moreover, in the areas of scienceresearches like biology, the amount of data is increasing withthe exponential growth speed [2]. While these numbers seemincredible, growing volumes of data are the inevitable devel-opment trends in various areas. On the other hand, researchesrelated to those growing data have been conducted in recentyears, like data mining and knowledge management [3–7].However, the utilization of “Big Data” in product lifecyclemanagement (PLM) significantly lags behind other areas, es-pecially for electronic commerce. Even worse, it is commonthat some manufacturers either do not store data or know littleabout how to use these data. This situation makes the differentlinks in the manufacturing chain to not be connected efficient-ly and data will not be generated, transmitted, and stored suc-cessfully. Does it mean that manufacturing will miss “BigData” since manufacturing enterprises are faced with such adilemma? As clearly stated in the technical report fromMcKinsey Global Institute [8], in the PLM area, the benefitsof “Big Data” techniques will permeate the entire manufactur-ing value chain, mainly in research and development, supplychain management, manufacturing, service, and other steps,which makes manufacturing to reduce the development cycle,optimizing the assembly process, increasing yields, and meetcustomer needs. In other words, the future of “Big Data” inPLM is promising.

“Big Data,” a new product of data engineering in the infor-mation explosion age, has caught countless people’s eyes bothin academic and industrial community recently. There havebeen more than 1000 research papers concerned about “Big

* Fei [email protected]

1 School of Astronautics, Beihang University, Beijing 100191, China2 School of Automation Science and Electrical Engineering, Beihang

University, Beijing 100191, China3 School of Advanced Engineering, Beihang University,

Beijing 100191, China

Int J Adv Manuf TechnolDOI 10.1007/s00170-015-7151-x

Data” since 2000. Although the concept of “Big Data” inPLM is quite a new idea, a number of papers related to knowl-edge management and data mining in manufacturing or PLMhave been published in recent years. The following three as-pects are summarized as the existing applications of “Big Da-ta” in PLM.

(1) Data management and scheduling based on “Big Data.”Once data are obtained, the issue about how to managethem efficiently must be considered. “BigData”manage-ment is quite a concern for scientists, and some researchresults have been achieved. For example, vertical ex-change of information through all levels of the factoryis validated in an integrative information model [1] andnew solutions based on “Big Data” have been proposedto build a better data warehouse for more data, morespeed, and more users [9]. Besides, batch task schedulingis facilitated by the dynamic, real-time characteristics of“Big Data” [10]. Moreover, in the area of meta-schedul-ing, efficient and cost effective scheduling algorithms arepresented to solve the “Big Data” problems [11].

(2) Supply chain management (SCM) based on “Big Data.”Different from the traditional concept of logistics, SCMindicates the networks of companies which collaboratetogether. Specific techniques like social media and real-time simulation gaming have been used to strengthen thesupply chain collaboration [12] which calls for the appli-cation of “Big Data” since these techniques must gener-ate a large volume of data. Besides, “Big Data” has en-abled predictive analytics in a Maker Movement SupplyChain [13] and the combination between “Big Data” andadvanced manufacturing techniques has remodeled sup-ply chains into demand chains which may lead to fewerwastes and fast customer response [14].

(3) Application of “Big Data” in mass customization (MC).MC has been deemed as a competitive method whichprovides every customer with personalized productsthrough high process flexibility and integration [15].“Big Data” analytics is regarded as one of the three majortechnological enablers for mass customization, whichhas pushed the development of the Third Industrial Rev-olution [16, 17]. To develop the MC system, the involve-ment of advanced manufacturing techniques (AMTs) isindispensable and fundamental [18]. More specifically,to some extent, it is the integration of a series of infor-mation and process flexibility techniques that makes theMC concept arise [15]. Not only should manufacturerscollect real-time online customization data of every con-sumer, but also they need to establish cooperative allo-cation of all resources through the network.

Although the existing research works about “Big Data” inPLM have played important roles in improving PLM, they are

still far from enough because many promising “Big Data”applications remained undeveloped yet. In fact, there is astrong need for “Big Data” in nowadays PLM to realize theaim of TQCSEFK (i.e., fastest Time-to-market, highest Qual-ity, lowest Cost, best Service, cleanest Environment, greatestFlexibility, and high Knowledge). Thus [19], academic re-searches about “Big Data” in PLM are extremely valuableand pressing.

In addition, the existing works about “Big Data” related toPLM mentioned above is primarily concerned with only onepart of PLM or a product, which have not explored from theviewpoint of the whole lifecycle of product and manufactur-ing.What is more, no specific and detailed application of “BigData” has been figured out in existing works. For example, itis better to divide the process of MC into several steps andresearching what “Big Data” can be utilized in the differentsteps, respectively.

Therefore, to complete the lack of academic studies of “BigData” in PLM, it is necessary to propose a comprehensive andsystematic framework of the detailed applications of “Big Da-ta” in PLM. In this framework, PLM is divided into three partsand the subdivisions continue to be separated into more spe-cific steps accordingly. Actually, not all of the divided stepsare suitable for “Big Data” employment which leaves a heavytask to distinguish the potential ones from the improper ones.Aiming at each potential one, the existing and promising ap-plications of “Big Data” are introduced and analyzed thor-oughly. Thus, the purpose of this paper is achieved which isto provide a roadmap to direct and guide the related researchworks in the future.

The paper is organized as follows. Section 2 briefly de-scribes the concept, characteristics, and applications of “BigData.” In order to enable the applications of “Big Data” inPLM, various data involved in the three main phases ofPLM are summarized and analyzed in Section 3. A frameworkof the potential applications of “Big Data” in PLM is illustrat-ed in Section 4, as well as the detailed explanations for thoseapplications. The advantages and challenges for applying“Big Data” in PLM are analyzed respectively in Section 5and Section 6. At the end of the paper, Section 7 concludesthe whole manuscript and points out the future works.

2 Big Data

2.1 Concept of “Big Data” and its brief history

2.1.1 Concept of “Big Data”

The concept of “Big Data” has not been precisely defined asthere is lots of buzz around it [20]. It has been declared as anew kind of economic asset, like currency or gold [21], whichis strongly competitive in economics. The creator of the “Day

Int J Adv Manuf Technol

in the Life” photography series even suggests that “Big Data”is an intelligent tool to combat poverty, crime, and pollution[21].

A convincing definition of “Big Data” focuses not only onthe size of data in storage but also on other important attributesof “Big Data,” like data variety and data velocity [22]. Forexample, the definition from Wikipedia illustrates that “BigData” is a term for any collection of large and complex datasets which are difficult to be analyzed by traditional data pro-cessing methods. In a more straight definition, “Big Data” justmeans data that it is too big, too fast, or too hard for existingtools to process [20].

2.1.2 Brief history of “Big Data”

The story of how data became big started many years beforethe current fervent concern on “Big Data.” Seventy years ago,the first attempt to quantify the growth rate in the volume ofdata was made, known as the “information explosion” [23].Since then, papers related to information collecting [24], stor-age [25], and processing [26] have greatly contributed to theboom of “Big Data.”

The first article in the ACM digital library to use the term“Big Data” is “Application controlled demand paging for outof core visualization” [27] in the Proceedings of the IEEE 8thconference on Visualization in 1997. From then on, the con-cept of “Big Data” has gradually become accepted by scien-tists and researchers.

In 1989, Howard D, then a Gartner Group analyst, pro-posed “business intelligence” as “concepts and methods toimprove business decision making by using fact-based sup-port systems” [28]. Business Intelligence and Analytics(BI&A) has emerged as an important area of study for bothpractitioners and researchers [29] related to data analyticsproblems. In 2011, the paper entitled “Big Data: The nextfrontier for innovation, competition, and productivity” [8] byMcKinsey Global Institute truly brought “Big Data” beforepublic eyes. At the same year, Science magazine published aspecial online collection: Dealing with Data, which discussedissues of “Big Data” in scientific research.

The academic milestones [8, 27, 30–35] in the brief historyof “Big Data” since 1997 are illustrated in Fig. 1, which

generally introduces what research contributions have beendone on “Big Data.”

2.2 Characteristics of “Big Data”

Properties of “Big Data” have been concluded as “5Vs theo-ry” and the most famous 3Vs are volume, variety, and veloc-ity, which were introduced by Gartner analyst Laney D in a2001 META Group research publication [31].

& Volume refers to the large amount of data. The data stor-age unit in “Big Data” has reached to TB and PB.

& Variety refers to the great number of types of data includ-ing weblog, music, video, picture, geographical position,etc.

& Velocity refers to the high speed of data process, which isthe most distinct feature from the traditional database.

Nowadays, the other 2Vs have been added to the model asfollows.

& Variability refers to the expansion in the range of values ofdata. Because it can cover the full range of human (andmachine) experience, “Big Data” always shows more var-iance than traditional datasets.

& Value refers to the low density and the high overall valueof “Big Data.” Among the large quantity of data, only aquite small part of information is useful and there is a needto evaluate them. Through data analytic methods, valuedinformation can always be obtained and used in businessoperation.

According to the 5Vs model, not only the sheer amount ofdata but also other characteristics of “Big Data” can bringchallenges to “Big Data” management, where the data set istoo large, data values change too fast, and it does not followthe rules of conventional database management and systems[36]. With the need to ensure the real-time or near real-timeresponses for huge amount of data in seconds, advanced dataanalytics methods should be proposed to handle the 5Vs of“Big Data.”

Fig. 1 Brief history of Big Data


2.3 Application of “Big Data”

Although the concept of “Big Data” is extremely hot nowa-days, some people have pointed out that “Big Data” existsonly on paper, and it has been deemed as a theoretical percep-tion which cannot be put into practical applications.

However, countless examples have illustrated that theviewpoint above is wrong and “Big Data” truly has broughtmomentous changes for our daily life. For example, the suc-cesses of Wal-Mart and Amazon have relied a lot on “BigData.” Wal-Mart, the largest retailer in the USA, has a cus-tomer database that contains around 43 terabytes of data,which is larger than the database used by the Internal RevenueServices for collecting income taxes [37]. “Big Data”makes itavailable to store and analyze the large volume of data aboutcustomers which provides the opportunity to gain competitiveadvantages [38].

The major applications of “Big Data” in various aspects areillustrated in Fig. 2. Some of them are summarized as follows.

2.3.1 Electronic commerce

Electronic commerce (EC) is a form of trading which empha-sizes online communication between customers and firms.Here comes “Big Data” to help build relational markets aswell as increasing customers’ abilities to find appropriate in-formation on the Web. According to Liu and Arnett’s surveyof webmasters from Fortune 1000 companies, an analysis ofdata is concluded to be essential for Web site success in EC

[39]. In order to create an effective cost management policyfor EC environment, a complex and extensible metric for dataenquiry [40] is proposed and Hadoop is applied in EC logistic(ECL Hadoop) to decrease the cost of I/O in Map Reduce[41].

2.3.2 Financial trading

“Big Data” is especially important for financial service com-panies because the biggest task for them is making use ofinformation to obtain as many profits as possible. For exam-ple, by analyzing changes in search terms related to finance inGoogle query and Wikipedia pages, stock market moves canbe predicted from the vast new data [42, 43]. It has beensuggested that online data may help decision makers to obtainnew insight into early information gathering. Besides, whenbuilding the internet finance trading platform, there are alsothe needs for “Big Data” processing methods, like providingsubscribers an operative link with financial markets by amulti-purpose data processing system [44]. Not only can thesystem provide supports for necessary financial transactionsor services, but it also may build a remote trading platform tokinds of pre-select traders.

2.3.3 Government

The trend of “Big Data” in government is facilitated by im-proved access to information. In 2009, Washington startedData.gov [45], a Web site that provides the public with a

Fig. 2 Major applications of Big Data


wealth of government data, which increases government cred-ibility to a great degree. Apart from this, with the large volumeof data from thousands of sensors and satellite images, gov-ernment can forecast in advance and take action promptly tominimize property damages and reduce casualties brought bynatural disaster [35]. Moreover, with the increasing needs tobuild smart city, various kinds of data for optimizing the op-eration of the whole city are required [45]. The stages of everycar, building, park, street, and even person are monitored inreal time by countless sensors dispersed around the whole city,which leaves a large quantity of data for decision makers toachieve the optimal scheduling solution.

2.3.4 Health care

Health is particularly well suited to benefit from “BigData” because a huge variety of health data are beingcollected quickly [46]. Online diagnosis repository is asuccessful application of “Big Data” in health care whenthe health data of patients have almost been stored, re-trieved, managed, and shared in electronic format. GoogleHealth and Microsoft HealthVault [47] have made it pos-sible for patients to consult and even receive treatmentonline by the virtue of public clinical history. What ismore, “Big Data” can also help the pharmaceutical indus-try to decrease variability in healthcare quality and esca-lating healthcare effectivity by mining data to see whattreatments are most effective for particular conditions[48].

2.3.5 Telecommunication

Telecommunication companies generate a tremendousamount of data, which include call detail data, networkdata, and customer data. As many communication chan-nels are now available, in order to grow new businessand protect against churn, there is no doubt that tele-communication companies have to get well prepared for“Big Data.” Data Mining has been applied to uncoveruseful information and identify telecommunication fraud[49]. Besides, preprocessing of large input data sets hasbeen confirmed to increase the plausibility and accuracyof the Churn forecasts [50].

From what has been mentioned above, it is apparentthat “Big Data” can truly make big differences on var-ious areas. However, according to survey, it is noticedthat in the field of manufacturing, the use of “Big Data”has not caught the researchers’ and manufacturers’ eyesyet. Thus, it is necessary to study systematically andthoroughly about the existing and potential applicationsof “Big Data” in PLM.

3 Data in PLM

3.1 Brief introduction to PLM

PLM emerged in the early twenty-first century to manage theknowledge intensive process consisting mainly of marketanalysis, product design and process development, productmanufacturing, product distribution, product in use, post-saleservice, and product recycling. As its name implies, PLMenables companies to manage their products across theirlifecycles [51].

PLM is of great significance as it can improve the devel-opment of new products and reduce manufacturing costs bycontrolling the products through their lifecycle.

A product’s lifecycle always includes design, production,logistics, utility, maintenance, and recycle. In the designphase, an idea in designer’s head is transferred into a detaileddescription. Subsequently, a product in its final shape is ob-tained in the production phase. Then, the product is stored inwarehouse and then transported to customer in the logisticsphase. In the phase of utility, customer uses the product whilemanufacturer provides remote service. If something goeswrong, the product enters into the maintenance phase, and ifit can no longer be used, it comes to the end of its life likerecycle or disposal.

These above phases can be divided into three periods: be-ginning of life (BOL), middle of life (MOL), and end of life(EOL) [51]. BOL is the period in which product concept isgenerated, designed, and subsequently physically realized.MOL is the period when products are distributed, used, andmaintained by customers or engineers. EOL is the periodwhen EOL products are recycled by manufacturers or dis-posed by customers [52]. In the following section, the datainvolved in these three phases are summarized and investigat-ed in detail.

3.2 Data in BOL, MOL, and EOL

To achieve good performance in PLM, figuring out what kindof data are involved in PLM is a requisite task before propos-ing advisable advanced analytics methods. It helps “Big Data”techniques solve product-related problems or make certaindecisions based on the tremendous amount of data at differentlevels.

Tables 1, 2, and 3 individually show various kinds of inputand output data in BOL, MOL, and EOL periods.

3.2.1 BOL

Although lots of preparations have to be done before produc-tion, when we focus on the manufacturing and products, theworks have been simplified to some extent which only leavethe most essential steps: marketing analysis and product


design. When it comes to the data in these two steps, thegeneral trend is from multiform to relatively single form.

At the step ofmarketing analysis, the most important task ismeeting customers’ demands, which may exist in kinds offorms. The variety of data forms include comments on blogs,the videos they upload on the Internet, the Web sites theymark, and their related purchasing behaviors. Besides theseunspecific demands, the information fromMOL and EOL likecustomers’ complaints and sales performance of similar prod-ucts can alsomake a big difference in achieving the purpose ofmarketing analysis-providing goals for product design.

At the step of product design, the data involved can betraced from the description of needs to the specific productfunction description and finally to the detailed design specifi-cations like drawings of the product configurations, the accu-rate programming codes for the automated manufacturingequipment, and all kinds of parameters. Moreover, it is neces-sary to make use of the maintenance and failure information tomodify and improve product design constantly. Based on thehistory information of breakdowns and root causes, currentproducts can be designed more efficiently and reliably.

The data during production are more ever-changing as themanufacturing processes are always dynamic which continu-ously produce real-time data. Numerous sensors are installedin the work shop to monitor the parameters of environment,equipment, and the products themselves. While some datamight be stable, others are changing dramatically along withthe flow of product manufacturing.

The data related to products are always in the biggest vol-umes and highest rates of change. When a product is beingproduced, to ensure the product quality is up to standard,especially in Precision Manufacturing, all design demandsshould be obeyed strictly like size tolerance, geometric toler-ance, and surface roughness. Thus, configuration parameterslike thickness and length, location parameters like coordinate,tolerance parameters like concentricity, and even the intensityof material should be monitored at any time to decrease therisk of inferior quality product. In conclusion, in this phase,the data from the stage of product design will be regarded asthe standards of product production that the data from both themonitoring and the testing of products are served to reach thestandards.

Table 1 Data in BOL

Input data Output data

Category Main data Category Main data

Customers’ demands Product function, configuration,packaging, quality, cost, brand,and other related expectation

Designspecifications

Materials list, suppliers list, electronic drawings,computer programming codes, configurationparameters, location parameters, toleranceparameters, intensity of materials, etc.

Maintenance and failureinformation

Main breakdown problems,frequency of maintenance,failure rate, critical componentlist, root causes, etc. [50]

Productioninformation

Assemble instruction, production specifications,production history data, production plan,inventory status, etc.

Cooperative corporationinformation

Alternative supplier information,alternative outsourcing companyinformation, etc.

Table 2 Data in MOL



User manual Product function introduction, installationguide, use condition, precautions, etc.

Product statusinformation

Degree of quality of each component,performance definition, etc.

Productioninformation

Assemble instruction, productionspecifications, production history data,production plan, inventory status, etc.

Usage environmentinformation

Usage condition (e.g., average humidity,internal/external temperature), user missionprofile, usage time, etc.

Maintenancesupportinginformation

Spare part ID list, price of spare part,maintenance/service instructions,etc. [50]

Maintenanceplan

Maintenance engineers, tools, dates, places,costs, failure causes, etc.


When it comes to equipment, things are a little different asvarious kinds of equipment might be applied to produce onlyone kind of product. And as the wear of equipment is a long-term process, prediction is much more involved in it thanmonitoring or testing. However, it does not mean that moni-toring is no longer important in this step. As the data are stillfrom sensors on equipment, estimating equipment wear justputs an emphasis on advanced analytics methods to handlethese data for prediction use.

3.2.2 MOL

In the middle of PLM, as products have existed in its finalform, issues concerned with service have become more sig-nificant and needed to be paid great attention [53]. Afterservitization of manufacturing was devoted to advancedmanufacturing systemwhich established manymanufacturingservice systems like cloud manufacturing (CMfg) service sys-tem, a lot of key techniques have been studied, includingsupply chain management, resource and service optimal allo-cation and scheduling, and service workflow management[54–56].

In the phase of logistics, efficient decision strategies areeagerly needed to solve complex issues no matter in ware-house management or transportation optimization since thetrend of globalization of logistics has exceedingly increasedthe volume of data. The input data of this phase are orderinformation, and what manufacturers need are optimal ar-rangements. How to transform order information into intelli-gent arrangements with the global view is themost crucial taskhere.

In the utility phase, based on the information from usermanual, customer can operate product normally. In the pro-cess, product status information will be generated and trans-mitted back to manufacturer, whichmakes it possible for man-ufacturer to get involved in the utility phase. Moreover, theusage environment information will also be monitored andrecorded to provide instructions for maintenance phase.

In current maintenance models, posterior or breakdownmaintenance is outdated and of low efficiency. By combiningmaintenance supporting information with product status infor-mation, a great deal of malfunctions can be predicted andprevented before happening. The output data of this phaseare detailed plans for maintenance, which include failurecauses and solutions.

3.2.3 EOL

When a product enters its EOL period, volume decisionsshould be made which concern the EOL product recycle ordisposal. On the basis of maintenance history information,product status information, and usage environment informa-tion from MOL period, the degradation status and remainingvalue of individual components can be calculated. With thepurpose to maximize values of EOL products, suitable EOLrecovery options such as recycle, reuse, remanufacturing, anddisposal should be decided considering product status. Withthe help of advanced analytics methods, an optimal schedul-ing project including when, how, where, and what to recyclecan be obtained.

4 Potential application of “Big Data” techniquesin PLM

4.1 Framework of “Big Data” in PLM

In BOL period, the main phases are design and production.Marketing analysis and product design make up the designphase while the production phase involves procurement, prod-uct manufacturing, and equipment management.

In the MOL period, which consists of logistics, utility, andmaintenance phases, “Big Data” presents huge potential inwarehouse management, product transport, product training,product supporting, and predictive and preventivemaintenance.

Table 3 Data in EOL



Maintenance historyinformation

Components’ IDs in problem, installeddate, maintenance engineers’ IDs,list of replaced parts, aging statisticsafter substitution, maintenance cost, etc.

Recycling partinformation

Reuse part or component, remanufacturinginformation, quality of remanufacturingpart or component, etc.

Product statusinformation

Degree of quality of each component,performance definition, etc.

EOL product statusinformation

Product/part/component lifetime, recycling/reuse rate of each component or part, etc.

Usage environmentinformation

Usage condition (e.g., average humidity,internal/external temperature),user mission profile, usage time, etc.

Dismantlinginformation

Ease to disassemble, reuse or recyclingvalue, disassembly cost, remanufacturingcost, disposal cost, etc.


In the EOL period, when the only focus is how to processobsolete products, “Big Data” plays an important role in mak-ing EOL product recovery decision and reverse logistics plan.

All these activities and their according potential applica-tions of “Big Data” are illustrated in Fig. 3.

4.2 Beginning of life

4.2.1 Marketing analysis

There are two parts of the tasks of marketing analysis. First,figuring out who our customers are, and the other is knowingwhat their needs are. Customers have various needs and de-sires, depending upon the characteristics of the customer.Thus, the first question is to identify which kind of customershas higher probabilities to buy the products.

Figuring out who are promising customers For example, asa company to manufacture cars, although the targeted cus-tomers are relatively wide, it is still mean to rank the cus-tomers based on their ages, genders, and salaries. However,even if the promising customers can be focused on middle-aged men with salaries that can afford their lives, when a moreaccurate targeting is needed as there is more than one type ofcar being produced in the company, it is advisable to match

various cars with various customers, respectively. To handlethe large volume of information from the great amount ofcustomers is not an easy task only by traditional analyticalmethods. Here comes the concept of “Big Data”which makesit possible to pick the most matched customers from the vastocean of information.

There are three types of data which can contribute to deter-mining targeted customers group: Historical data like whetherthe person has ever bought a similar product or how his moneyhas been spent, which can be obtained from the sale phase;market research data from questionnaires and studying papers;and the last type of data has emerged with the development ofthe Internet which is from the person’s browsing recordingand preferred Web sites.

All these three types of data are of great volumes especiallythe last one. The combination of these data needs “Big Data”processing technology to help make decisions about targetingcustomers. ATargeting and Allocation of Promotional Spend(TAPS) system has been proposed to help make decisions byusing a combination of historical data, market research data,and the intuition and expert knowledge of a group of man-agers [57].

Discerning customers’ needs for product One of the impor-tant tasks of the PLM is to ensure customers’ satisfaction

Fig. 3 Framework of Big Data in PLM


which means customers’ requirements should be analyzedbefore the product design.

Customers’ needs fall into three broad categories: qualityand function of product, time frame, and cost effectiveness.Different customers have different particular emphases onproducts. Some customers may have the preference for high-quality products while others tend to purchasing low-priceones. The data related to how to find out promising customersstill go for here.

According to Maslow’s hierarchy of customer experience,a successful manufacturer can not onlymeet the basic needs ofcustomers but also anticipate their needs. Thus, the task here isnot simply collecting demands from customers but forecastingcustomers’ unspoken needs by advanced analysis methods.As the source and foundation of forecasting is always fromtheir related purchasing behaviors, correlative recentsearching recordings, and comments on their social networks,there is no doubt that “Big Data” can have a significant influ-ence on customers’ needs discerning.

4.2.2 Product design

Product design is an iterative, complex, decision-making en-gineering process. It usually starts with the identification ofseveral needs, proceeds through a sequence of activities toseek an optimal solution to the problem, and ends with adetailed description of the product. Generally, a design pro-cess consists of three phases: product design specification,conceptual design, and detailed design [58].

A study conducted by Lotter B [59] indicates that as muchas 75 % of the cost of a product is being committed during thedesign phase. Since the design process is vitally important inthe PLM, it is meaningful to research on keeping the cycle ofmanufacturing functioning smoothly and cost effectively.With the fast development of “Big Data” in recent years, var-ious advanced techniques have been introduced to support thedesign phase.

Turing the needs to specific functions Quality function de-ployment (QFD) [60] is a structured methodology to translatecustomers’ needs into specific quality development. To createa comprehensive QFD matrix, there is no doubt that a largeamount of data should be obtained and analyzed with variouskinds of techniques and algorithms [61].

QFD matrices become highly competitive due to the highdensity of product information found therein. Among the var-ious techniques based on QFD, house of quality is only atraditional one which will be gradually weeded out since it isno longer suitable for the high speed of data explosion. As thecustomers’ requirements cannot be listed exhaustively as wellas the product design requirements, let alone evaluation for therelationship between them, it is necessary to propose a newQFD approach to meet the challenges of “Big Data” and to

manage the complex correlations between needs andfunctions.

Presenting solutions to meet the design requirementsDecisions made at the conceptual design stage have sig-nificant influence on costs, performance, reliability, safe-ty, and environmental impact of product. However, thedesign requirements and constraints during this earlyphase are usually imprecise, approximate, and even un-practical. Aiming at presenting the appropriate solutionsto meet the design specification in conceptual design, var-ious techniques and tools have been put forward based onthe Internet and Web techniques [62]. For example, shar-ing and reusing distributed design knowledge and infor-mation for product family design and platform-basedproduct development [58, 63], establishing agent-basedknowledge management system for decision support ofmodular product collaborative design [63], implementingWeb-based decision support tool to better support themultiplicity of contexts required [64], and developing cre-ative design tools (CDT) to provide designers with flexi-ble creative design environment to enhance their creativedesign thinking [65] all present the potential of “Big Da-ta” in PLM.

All these methods mentioned above are relied onknowledge database and knowledge management sys-tem. However, in the future, when design repository[66] becomes bigger and bigger, “Big Data” techniqueshave to be introduced to handle the large number ofdiverse data.

Making final decisions on details of product Detailed de-sign of product is the last design activity before theproduction begins. The hardest design problems mustbe addressed by the detailed design; otherwise, the de-sign is not truly complete. Compared with source code,detailed design is still abstract but should be specificenough to ensure that translation to source is a precisemapping instead of a rough interpretation.

A template for detailed design would not be of muchuse since each detailed design is likely to be unique andquite different from other designs. What are actuallyhelpful are examples in regard to guidance on detaileddesign. There are countless examples which can be usedfor reference, although only a small part of them arevaluable. In other words, the task here is to find outthe most relevant example as detailed as possible togive comprehensive instruction for the new product.

There are lots of detailed design contents like Struc-tural Chart, Control-Flow Model, Class Diagram, andCollaboration Diagram. Each content has a data dictio-nary to correspond to and the data dictionaries cover allkinds of information which have great effects on


product design. Only by “Big Data” techniques can thelarge volume of details be compared effectively and thebest example be proposed for the new product.

4.2.3 Procurement

In the procurement step, the main task is to choose qualifiedsuppliers based on the Bill of Material and other consider-ations like price and reputation. The choosing process is relat-ed to diverse factors, and it requires the handling of advanceddata analytics. For a number of factories, not all componentsare produced by themselves because of the lack of particulartechniques or equipment. At that time, determining an advis-able outsourcing policy is significant.

Choosing qualified suppliers In most cases, suppliers arechosen under uncertain circumstances. Thus, effectivemethods to evaluate different cooperative suppliers are of sig-nificance. Especially in the world of global cooperation, alarge amount of data about suppliers’ history performance,reputation, and other influence factors are required as well asvarious advanced data analysis methods. For example, a com-bination of the TOPSIS method with the analytical hierarchyprocess (AHP) or FUZZY is examined to be useful to choosesuppliers under uncertainty [67].

Determining outsourcing policy Nowadays, tide of globali-zation, rapid technological evolution, and need for cost reduc-tion have motivated quite a part of companies to turn tooutsourcing. In the current environment, the objective ofoutsourcing is to make production not only more economicbut also more strategic, technological, and social [68]. Thus,determining the outsourcing policy is a crucial step in PLMwhich requires the involvement of “Big Data” techniques. Forexample, analytic network process (ANP) and the balancedscorecard (BSC) are combined to build a cohesive decisionmodel for outsourcing strategy which has been confirmed asrobust [69].

4.2.4 Product manufacturing

As the core in BOL, and even in the whole PLM, productmanufacturing definitely generates vast data, like productionspecifications. Product quality monitoring and simulation aretwo activities that have close relationships with “Big Data”processing.

Monitoring product quality A product test reveals a prod-uct’s performance at a single point in time, but products arecontinuously flowing from suppliers to factories to ware-houses to retailers and finally to customers. And product qual-ity monitoring system is needed at all phases of product

lifecycle. In this part, only product quality monitoring in fac-tories are discussed about.

Future manufacturing systems will need to process largeamounts of complex data which are provided to workers formaintaining the proper function and desired production per-formance due to a rising demand on visibility and verticalintegration of factory floor devices with higher level systems.For example, a context-aware industrial monitoring systemthat integrates context data and existing plant informationcan provide only the most relevant information for users dy-namically [70]. Complex event processing has been imple-mented in shop floor to monitor for event-driven manufactur-ing processes [71]. Besides, with the help of RFID techniques,huge amount of RFID-enabled production data have beengenerated which call for the participation of “Big Data” tech-niques to monitor and track the product quality in real time[72].

Simulation and testing of product Product testing is an in-dispensable phase for product manufacturing especially forthose complex big products which need to be assembled byvarious small components since every fault of component isfatal for the product’s normal operation.

Test data generated by automatic test equipment (ATE)manufacturing environments is no exception to the increasedvolume of data during the product lifecycle. The challenge ofthis enormous amount of test data is how to provide peoplewith effective ways to make decisions from it. For example, inthe complex and critical aerospace industry where data visu-alization reaches its limit because of huge data volume, pre-dictive algorithms on “Big Data” from manufacturing testenvironments are proposed [73]. “Big Data” techniques canbe applied to answer new questions about the correlation oftest data with the number of profits in the future.

4.2.5 Equipment management

Equipment management is an activity which concerns closelywith the overall production process which influences productquality and energy consumption. Among the works in equip-ment management, wear estimation and energy conservationare main potential application areas.

Estimating equipment wear “Big Data” techniques havegradually entered into the equipment wear estimation phasebecause of their strong abilities to collect, integrate, transport,process, and analyze dynamic and real-time data from increas-ing number of sensors on equipment; for example, variousadvanced monitoring and estimation models including kindsof algorithms like integration of artificial neural networks(ANN) and fuzzy logic [74], the least squares version of sup-port vector machines (LS-SVM) and singular spectrum


analysis (SSA) [75], Adaptive Neuro fuzzy Inference system(ANFIS) [76], and neural network-based sensor fusion [77].

Increasing equipment energy efficiency The whole life cy-cle of manufacturing not only involves the flow of materialsalong the cycle but also the energy in it. Among the disparatekinds of energy spent along the cycle, the energy consumptionof equipment might account for a large part. Thus, increasingthe energy efficiency of equipment is an issue that should beaddressed as soon as possible.

There are generally two ways to decrease the equipmentenergy consumption. First, improving the principles and con-figurations of equipment, and second, proposing energy man-age methods for equipment operation. No matter in whichsolutions, there are promising application areas for “Big Da-ta.” Particularly for the second one, in a workshop whichincludes hundreds of different types of machines, it is truly acumbersome job to arrange which machine should operate atwhat speed at what time. With the help of “Big Data,” theoptimization of equipment operation procedures can be rela-tively easy to achieve.

4.3 Middle of life

4.3.1 Warehouse managing

Warehouse management is a vital part of logistics since thebenignwarehouse operation can ensure the smooth circulationof goods from manufacturer to retailers. However, along withthe expanding of trading areas, warehouse management hasbecome a complex structure which should consider the rela-tionships between decision factors globally. At the same time,the conventional approaches cannot continue to undertake theheavy task which leads to the emergency of “Big Data” appli-cations in warehouse management.

Order process For some factories whose operation model ispull production, order processing might happen before prod-uct manufacturing. In that case, as customers’ expectations forproduct have an exponential growth in the volume of infor-mation, the order process is a serious problem which requires“Big Data” analytic methods [78]. For put production facto-ries, order process seems much conventional which involvesthe work of extracting useful information to appropriate for-mats. However, since the orders may be generated at anymoment, processing them more timely and accurately is notas easy as it looks in “Big Data” time.

Inventory management In order to meet challenges alongwith global trade, inventory management should be improvedto a more intelligent level. To balance the need for productavailability and the need for minimizing stock holding costs,an intelligent inventory should be built to identify inventory

requirements, provide replenishment techniques, and trackproduct status. For example, a decision support system apply-ing modified fuzzy neural network (EFNN) has been pro-posed for managing spares inventory in a central warehouseto achieve the optimal performance [79]. The overall optimi-zation of storage assignment methods, routing methods, orderbatching, and zoning still waits to be explored [80]. “BigData” techniques are among the most potential methods todeal with it.

4.3.2 Product transport

Transportation system has changed dramatically since thehappening of “Smart City.” With the help of volume sensors,transport networks can be easily established based on the in-formation transmitted from these sensors, which provide pre-requisites for the applications of “Big Data” techniques inproduct transport phase.

Tracing product Generally speaking, customers tend to haveknowledge of where their products are and when their prod-ucts will arrive. Thus, a query system should be established topresent the information about product status as well as thetruck locations. Because the data in the systemwill be updatedalong with the transport process, “Big Data” techniques areneeded to support the operation of this system.

Green transport planning In today’s environmental-friendly society, reducing transport-related emission has be-come one of the most important considerations when makingtransport plan. For example, some companies would like toseek opportunities to share transportation with other compa-nies that deliver products to the same regions. In order toachieve effective sharing which can both decrease emissionand ensure transport efficiency, a decision system should beproposed based on the external information from other com-panies. It is definitely a tough task as the data have almostdoubled.

4.3.3 Customer service

In recent years, “service” is an importance form of productwhich is sold independently or with other actual products. Insome degree, the quality of customer service plays a signifi-cant role in customers’ satisfaction. As for a company, thereare various kinds of products manufactured, assembled, andtransported all around the world every day. It is totally a hardwork to establish and maintain good relationships with cus-tomers from different backgrounds. Thus, “Big Data” tech-niques should be introduced to customer service process asan effective and efficient method to increase customers’ satis-faction when using the products.


Training Increasing with the complexity of operating ma-chines or software, it is not uncommon that products some-times cannot perform well because of the inaccurate behaviorsof customers. Although every matter of attention has beenexplicated in user manual, training is always needed when itcomes to products with high precise, value, or risk.

As mentioned above, because of the wide spread of prod-ucts, it may be impossible to dispatch instructors for eachcustomer. Thus, periodically arranging classes for customersare necessary and cost efficient since traditional one-to-onemodel has become inconvenient and unprofitable. At thattime, training has been transformed to a planning problem likeorganizing school. Teaching buildings, equipment, faculty,and class schedules all should be considered and a largeamount of data will be analyzed to make correct decisions.In the other hand, the whole training planning ought to be ableto adapt to the changes like sales performance which meansonly knowledge management with high speed like “Big Data”can be applied.

Online enquiry Enquiry is a kind of supplement to trainingand it usually has two channels as phone and Internet. Enquiryon the phone is comparatively an old but still popular methodbecause of its timeliness while online enquiry often meansleaving a message which means it is not suitable for emergen-cy situation.

However, with the development of business intelligence,online enquiry is an indispensable trend and ensuring quickresponse to customers’ enquiries is a critical characteristic forits wide use. In some cases, auto reply has shown its potentialby figuring out key words in questions and searching for theaccording answers for them. This process is complex whichinvolves pattern recognition, machine learning, and relatedtechniques in database. All of them have close relationshipsto “Big Data.”

4.3.4 Product support

In traditional utility phase, it seems that manufacturer has littleto do with products. However, with the development of RFID,every product has its own sensor and its status information canbe generated and transmitted to manufacturer. It is applicablefor manufacturer to provide product support by remote mon-itoring and inspection.

Product quality real-time monitoring The service life of aproduct is always calculated by year which means that mon-itoring a product through its life span must generate an amaz-ing number of data. However, to analyze product degradationpatterns, it is necessary to monitor product status continuous-ly, which needs “Big Data” techniques to handle the sensordata.

Product regular inspection In traditional mode, only pre-cious products can have regular inspection, and this inspectionis usually in the form of on-the-spot service. Nowadays, withthe rapid development of the Internet and remote service,product regular inspection can be done evenwithout botheringcustomers by remote sensing. Based on the information fromregular inspection, customized suggestions can be given tocustomers for normal usage. Thus, the process of transforminginspection information into meaningful advice needs the con-tribution of “Big Data” techniques.

4.3.5 Corrective maintenance

The importance of maintenance has increased because of itsrole in keeping and improving system availability and safety,as well as product quality [81] and manufacture service [82].In people’s daily life, corrective maintenance is still the mainmaintenance mode nowadays. However, even for this tradi-tional maintenance mode, it is no longer limited to returningproducts back to factories or sending the maintenance person-nel to customers.

Establishing the e-maintenance system The developmentof communication and information techniques has allowedthe popularity of e-maintenance, which integrates existingtele-maintenance with Web service and modern e-collabora-tion. Not only information but also knowledge and e-intelligence are shared and exchanged to facilitate reachingthe best maintenance decisions [81].

Some researchers have focused on applying “Big Data”techniques to construct e-maintenance systemwhich has maderemarkable headway. For example, a new e-maintenance sys-tem has been proposed to achieve near-zero-downtime perfor-mance on a sharable, quick, and convenient platform throughintegrating the existent advanced techniques with distributedsources [83].

4.3.6 Preventive and predictive maintenance

The RFID technology and Internet of Things (IoT) have madeit totally possible to track the products through its birth todeath and to link the products to their manufacturer. Thus,the “Big Data” appearance can provide opportunities for anew maintenance generation like preventive and predictivemaintenance. Both of them are different from corrective main-tenance because actions will be taken to prevent the failurebefore it actually occurs in these two maintenance modes. Inother words, as maintenance has gradually developed towardsthis direction, the boundary between utility and maintenanceis no longer clear and maintenance happens as long as prod-ucts are being used.


Fault detection and degradation monitoring As is men-tioned in the product supporting phase, RFID techniques havemade it possible to monitor product real-time status. From theinformation of the utility phase, the degradation pattern can berecognized, like material aging mechanism in specific envi-ronment. However, how to project an ideal preventive main-tenance program which can prevent all equipment failuresbefore occurring is still a tough job considering the strikingamount of data. Transforming the status information to de-tailed maintenance plan requires “Big Data” techniques toanalyze and optimize.

4.4 End of life

4.4.1 EOL product recovery decision

With the high-speed upgrade of current products, especially inelectrical and electronic product market during the last fewdecades, much focus and effort have been placed on the wasteof these products. In order to reduce their negative impacts onenvironment and human, in EOL period, the wastes need to beproperly handled, processed, disposed, and, if applicable,remanufactured, recycled, or reused [84].

Predicting remaining lifetime of parts or componentsWhile a product cannot be used any longer, it does not meanthat every component of it is useless. In most cases, the re-maining value of parts is worth to be predicted which helps todecide what to recycle. The predicting process is not an easywork which involves the maintenance history data and thecomponent ID from the BOL and MOL periods.

Product recovery optimization After figuring out the re-maining lifetime of each component, dismantlers can doEOL product recovery optimization with the objective ofmaximizing values of EOL products considering product sta-tus. At this phase, “Big Data” techniques can be applied todecide suitable EOL recovery options such as recycle, reuse,remanufacturing, and disposal [52].

Enhancing the resource-saving recycling activities One ofthe main purposes of recycling is to reduce the wasted prod-ucts’ harm to the environment. Thus, it is necessary to ensurethat the recycling process itself is energy saving and environ-mentally friendly. Aiming at achieving this goal, “Big Data”techniques should be applied to establish an intelligent deci-sion support system (DSS) to enhance and further resource-saving and recycling activities associated with minimizingenvironmental impacts and resource consumption duringrecycling. For example, backtrack-free search technique anda flexible and efficient database query have been proposed inan intelligent DSS to guarantee synchronizing recycling activ-ities [85].

5 Key advantages of applying “Big Data” in PLM

The use of “Big Data” will become the basis of competitionand growth for individual manufacturers. “Big Data” tech-niques can be employed in manufacturing to enhance the in-telligence and efficiency of design, production, service pro-cess, and other kinds of aspects, which are briefly described asfollows.

(1) Enhancing the quality and innovation of product designand achieving socialization design.

By applying “Big Data” techniques in product designphase, the comprehensive quality of product design canbe improved, as well as innovation. Through a generalsurvey of the world top 500 enterprises, it can be easilyconcluded that they all have superior design capability.The common feature for these companies is that theyusually search for feedbacks of the users on the Internet.In some circumstances, like company’s online commu-nity, users are even permitted to join in the design phase.Furthermore, these feedbacks can be quickly involvedinto product design with the help of “Big Data” analysiscapability. Since the customers have participated in thedesign phase by themselves, the final products are morelikely welcomed by them. For example, Xiaomi Compa-ny in China (http://www.mi.com/) is a typical successfulrepresentative.

Besides, when it comes to high-end manufacturing,excellent product design projects require long-term ac-cumulations of design data. For instance, products madefrom the samematerials can be totally different in qualityand duration of life. The variances in quality are mainlycaused by the diverse material ratios and process tech-niques, which are mostly relied on the storage of data andexperience. Thus, if the “Big Data” concept and tech-niques can be applied in the whole design phase ofPLM, all designers can treat the design tasks as the em-ployment of data rather than dodging responsibility. Inother words, the management ability decides the level ofdata realization and application.

What is more, applying “Big Data” techniques inproduct design phase can enhance the socialization de-sign. Before the boom of “Big Data,” product design wascompleted by employees from a single company. How-ever, designers all around the world can be accessed bythe Internet nowadays without the limitation of region.Project managers can outsource the design work to moreskilled and professional designers, like throughwitmart.com.

(2) Improving the accuracy, quality, and output ofproduction.The second advantage of “Big Data” in PLM is locat-

ed in the production plant since “Big Data” techniques


http://www.mi.com/

can help improve the accuracy, quality, and output ofproduction. In fact, the information degree of Chineseadvancedmanufacturing is actually high, where machinetool in production line is almost automated. From themoment raw materials are sent into production plant tothe moment final products are obtained, manual labor isnot necessarily required. Every computer numerical con-trol (CNC) machine is just like a computer, and there aredozens of quality control points along a production linewhich will generate a huge amount of data per minute. Ifa company has many factories around the world, thecompany itself is a standard “Big Data” application mar-ket.

Although “Big Data” seems new for manufacturingcompanies, it is not that terrifying since company man-agers can start small. At the beginning, data can be col-lected as the dimension of individual machines. Then, bycombining with pre-established models, “Big Data” ap-plicationmay be gradually formed. Through the accumu-lation of a month or a year, it can be analyzed that whichfactors might have influence on quality. Based on theseanalysis results, products and manufacturing processescan be improved reasonably.

(3) Providing accurate, high-quality, personalized productservice.

By using “Big Data” techniques, it can provide accu-rate, high-quality, personalized product service for users,including consulting, after sale, maintenance, and safetyservice. In recent years, it has been reported that servicehas played a more important role than manufacturing oneconomy, which seems to show that service andmanufacturing are opposite to each other. However, ser-vice and manufacturing are actually not that contrarysince the two parts have both been incorporated intoPLM.

In the MOL period, since product use status is alwaysbeing monitored and transmitted back to factory, cus-tomers may no longer worry about sudden breakdowns.Through the real-time monitoring for products, effectiveand timely maintenance suggestions can be provided by“Big Data” analysis to make users acquire more value.For example, General Electric Company has applied var-ious sensors on its jet engines and rigs for preventivemaintenance. Moreover, during the EOL period, thequality of service is worth to be emphasized and “BigData” can exert a positive effect on it by making cus-tomers feel convenient and comfortable with a reason-able recycle plan.

(4) Accelerating the integration of IT, manufacturing, andoperation system to enter the age of intelligentmanufacturing or Industry 4.0.

Based on the technological concepts of cyber-physical systems (CPS), IoT [86], and Internet of

Services [87], Industry 4.0 is an integrated term for tech-nologies and concepts of value chain organization, whichfacilitates the development of Smart Factory. CPS canbuild virtual copy of the physical world [88] by monitor-ing physical processes over IoT, in which CPS commu-nicate and cooperate with each other and humans in realtime. The core of Industry 4.0 is data, and the integrationof IT, manufacturing, and operation system is the methodto acquire data more timely, quickly, and flexibly [89].

Take an ideal intelligent production plant as an exam-ple; all manufacturing devices, components to be proc-essed, and loading robots are equipped with CPS, whichhave wireless Internet capability. Not connected by thecentral controller unit, components are directly related toprocessing equipment. Moreover, independent transpor-tation cars can send components to loading robots ac-cording to the underground sensing lines. All informa-tion including production and sales files required forcontinuing processes will be carried on by the compo-nent itself.

(5) Accurately predicting product demands.Figuring out customers’ needs accurately and quickly

is an effective means for manufacturers to increase cus-tomers’ approval with the loyalty. With the assistance of“Big Data,” marketing can be more precise and specificthan ever, which presents great opportunities for “Cus-tomization.” Not only explicit needs will be met but alsodiverse latent demands can be predicted and fulfilled bymanufacturer through “Big Data” techniques.

Except for customer’s needs, the performance of sim-ilar existing products may also help predict demands fornew products. Thus, how to figure out and analyze theperformance of existing products is a problem thatshould be considered carefully because it is not only asimple survey but also a series of data analysis taskswhich involves “Big Data” techniques.

(6) Accurately predicting supplier’s performance.Employing “Big Data” techniques into suppliers pick-

ing is necessary since data are the best proofs forpredicting suppliers’ performances. In fact, there usuallyare more than one or two parameters to evaluate sup-pliers’ performances. Price, quality, popularity, region,environment, or even politics all need to be consideredif accurate predictions about supplier performances arehoped to be made. Besides, the evaluations of kinds ofsuppliers are in different forms, like work reports, cus-tomers’ feedbacks, news reports, Internet reviews,peer assessments, and so on. When choosing a sup-plier, factors above are not equally important sincedifferent fields have different emphases. How toassign weights to different factors is relied on thepast data accumulation and “Big Data” techniquesinstead of conjecture.


(7) Providing manufacturing equipment with intelligentsensing, management, and maintenance.

Asmentioned in Section 4.2.5, “Big Data” techniqueshave great potentials in equipment management, espe-cially in estimating equipment wearing and increasingequipment energy efficiency. As for advancedmanufacturing, machines are quite significant since few-er manual labors are used. In other words, the healthstatus of equipment decides whether the production pro-cess can operate normally. The situation of equipment islike the quality of products, which should be real-timemonitored in case of sudden breakdown.

(8) Supervising and controlling energy consumption anddischarge.

In today’s environmentally friendly society, energyconservation and emission reduction are two importanttasks in various areas, including manufacturing. Fromwhat has been discussed above, “Big Data” techniquescan have a positive effect on supervising and controllingenergy consumption and discharge. For example, in the“procurement” step during BOL as well as the “producttransport” step during MOL, “Big Data” can help makeintelligent decisions concerned with the shortest dis-tance, the minimum fuel consumption, and the maxi-mum profit. By making use of the sufficient data, thegreenest transport path can be planned to cause thefewest wastes.

Additionally, in the step of warehouse management,efficient order processingmechanismsmainly rely on theadvanced “Big Data” techniques. If all data can be man-aged shapely and intelligently, there may be a huge re-duction of energy costs about warehouse. Off course, therecycle phase also illustrates strengths of “Big Data” onreducing wastes and energy since the original purpose ofrecycle is just energy conservation and emissionreduction.

6 Challenges ahead

As a new concept, “Big Data” has met knotty and profoundchallenges like collection, storage, transfer, and security ofdata [90]. When “Big Data” is applied in PLM, more specificchallenges have emerged which seriously hold back the po-tential applications.

(1) Data collection in PLM.When it comes to “Big Data,” the first problem is how

to collect useful data in effective and time-savingmethods. Maybe several years ago, questionnaire surveywas the best way to figure out what interests customersmost. However, in the “Big Data” age, with the

widespread development of the Internet, customers’needs can be analyzed by their behaviors on the Internet.For example, Google search records will be saved as thebase for future product promotion. When data are con-nected to the Internet, the volume of them must be quitehuge. Data collection is not an easy work as it seems.

In addition, the other kind of data collection is aboutsensing product or equipment quality in real time, whichmay result in more data in a quicker way. The capabilityof sensor is really important for “Big Data” since if nouseful data can be collected, all advantages discussedabove are like castle in the air. Thus, because of its foun-dation role in “Big Data,” data collection is the primarytask in which properties of kinds of sensors and collec-tion methods should be carefully considered.

(2) Data storage and transfer in PLM.It is quite a hard and serious job to ensure intact data

storage and transfer in PLM since any small lack maycontribute to severe mistakes. “Software as a Service”(SaaS) is proposed to handle the problems in storingand transferring large volumes of data. When data aregenerated where large-scale storage is unavailable,Globus Online, a type of Dropbox, provides powerfulstorage capacity to transfer the data [91]. However, trans-fer protocols associated with large, unstructured data setsstill have challenges to get over to ensure the speed andsafety of it [92]. When it focuses on “Big Data” in PLM,the data forms in BOL have already been incredibly var-ious. As different kinds of data flow along the lifecycle, itmay be stored and then transferred to any form, likenumber, picture, chart, light, temperature, and so on.

(3) Data process based on manufacturing knowledge andexperience.

Although “Data Mining” (DM) or the so-calledKnowledge Discover in Database (KDD) is an areawhere a lot of researches have been conducted, its appli-cation in manufacturing is still hard work since combin-ing manufacturing laws and knowledge with DM is ac-tually innovative and challenging. In fact, data processwith the features of manufacturing is more thanemploying existing data process methods to manufactur-ing cases. To get the desired results which can serve fordesign, production, transportation, maintenance, and re-cycle, data process in manufacturing should be deliber-ated with the involvement of manufacturing specializedlaws and knowledge.

(4) System, service platform, and tool of “Big Data” inPLM.

One of the obstacles that seriously impedes the wide-spread of “Big Data” in PLM is its high threshold. Acompany, especially a small company, does not havethe resources, like money or time, to develop a systemor a platform for certain data. The products manufactured


by various companies are different, and it is not reason-able to apply the “Big Data” system which is successfulin one company to another indiscriminately. Thus, it isquite a challenging job to develop a “Big Data” systemthat can be employed by different companies just withsome small changes for adapting to their according fea-tures. In addition, systems, service platforms, and toolsof “Big Data” in PLM are urgently required.

(5) Security of PLM data.To make sure that “Big Data” techniques can always

operate normally, security of PLM data is a challengethat must be overcame. To prevent the leak of data, se-curity systems with advanced encryption algorithmsused in financial trading can be borrowed in manufactur-ing area [93]. Like in financial or high-tech company,data equal to treasure. Even when a small part of datais stolen by competitors, there is a high chance that thewhole design of a new product will be obtained by them.Besides, the safety issues from external partners or com-petitors and the lack of trust between data analysts anddecisionmakers can also bring about great challenges for“Big Data” in PLM. Only by understanding the mean-ings of all data will decision makers propose intelligentand practical ideas. Thus, if data analysts conceal parts ofdata because there are not enough methods to ensure thesecurity of data, the whole manufacturing company maysuffer unnecessary damages.

(6) Data visualization.Apart from challenges mentioned above, data visual-

ization is another urgent challenge for “Big Data.”Whenthe results of data analytics emerge, converting them intoeasily comprehensible forms is valuable and necessary.The size and diversity of manufacturing data nowadayshave presented major challenges to visualization tools[94]. The great difficulties to analyze “Big Data” havedistinguished decision makers from data analysts clearly,which means the communication between them is be-coming tougher than ever. To some degree, the final re-sults from data visualization may be the things that deci-sion makers care most rather than the original data. Thus,proposing appropriate data visualization tools is of greatvalue to get well prepared for the “Big Data” age.

7 Conclusions and future works

In this paper, the first innovative contribution is revealing thefact that “Big Data” can be and is being applied intomanufacturing. Then, based on this fact, the paper has thor-oughly investigated that in which specific manufacturingphases “Big Data” can be employed. The application potentialof “Big Data” is enormous, and “Big Data” techniques can

accompany with the whole lifecycle of product. In BOL peri-od, “Big Data” can discern customers’ needs and then takeadvantages of these needs to instruct product design intelli-gently. Besides, products’ quality and equipment’ wear pat-terns can both be monitored by the combination of “Big Data,” IoT, and cloud capacity [95]. In MOL period, the center ofattention has been transformed from products to service forcustomers, and the data are always acquired by RFID tech-niques. Based on these remote sensing data, decisions relatedto logistics, utility, and maintenance should be made whichrely on the participation of “Big Data,” as the data generationprocess is long and real time. In the end part of PLM, the maintask here is to cope with the data obtained before and buildingdecision support system with the help of “Big Data.”

In the future, the primary task is addressing the problemabout how to apply “BigData” in each specific phase in detail.What hardware facilities are required to construct and whatadvanced algorithms are needed to be improved are bothworth to be studied [96, 97], as well as the specific enablingtechniques andmethods for realizing the better combination of“Big Data” with multiple techniques like IoT, cloud capacity,and distributed processing techniques [98].

Acknowledgments This work is financially supported in part by NSFCproject (No. 51475032), the Beijing Youth Talent Plan under Grant29201411, Beijing Natural Science Foundation (4152032), and the Fun-damental Research Funds for the Central Universities in China.

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