QUALITY 4.0 IMPACTAND STRATEGY HANDBOOKGetting Digitally Connectedto Transform Quality ManagementCONNECT:lnsresearch.com
QUALITY 4.0 IMPACT AND STRATEGY HANDBOOKGetting Digitally Connected to Transform Quality ManagementTABLE OF CONTENTSSECTION 1: Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3SECTION 2: Defining Quality 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7SECTION 3: Understanding the 11 Axes of Quality 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11SECTION 4: Summary and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26ACRONYMQUICK REFERENCEVIEW ON BLOGlnsresearch.com
SECTION 1Executive Summary
Executive SummaryPAGEQUALITY 4.0 IMPACTAND STRATEGY HANDBOOK4TABLE OFThe most recent decade has seen rapid advances in connectivity, mo-for Industry 4.0. Quality 4.0 is closely aligning quality managementbility, analytics, scalability, and data, spawning what has been calledwith Industry 4.0 to enable enterprise efficiencies, performance, in-the fourth industrial revolution, or Industry 4.0. This fourth industrialnovation and business models. However, much of the market isn’trevolution has digitalized operations and resulted in transformationsfocusing on Quality 4.0, since many quality teams are still trying toin manufacturing efficiency, supply chain performance, product in-solve yesterday’s problems: inefficiency caused by fragmented sys-novation, and in some cases enabled entirely new business models.tems, manual metrics calculations, quality teams independently per-This transformation should be top of mind for quality leaders, asforming quality work with minimal cross-functional ownership, andquality improvement and monitoring are among the top use casesineffective supplier communication, among others.What are the top IIoT use cases your companyis pursuing today? (N 252, all respondents)What are the top IIoT use cases your company will startpursuing in the next year? (N 249, all respondents)CONTENTSSECTION1 23 429%25%24%Remote monitoringEnergy efficiencyAsset reliability23%23%22%19%Quality improvementProduction visibilityInternet enabled productsBusiness model transformation,e.g. selling capacityAsset and material trackingCustomer access to informationImproving safetyProduction visibility5%10%18%18%15%12%Energy efficiencyInternet enabled productsTraceability and serializationSupplier visibility8%Improving safetyImproving environmentalperformance5%0%21%21%20%19%Customer access to information6%Supplier visibilityImproving environmentalperformanceAsset reliabilityBusiness model transformation,e.g. selling capacityAsset and material trackingQuality monitoring19%17%15%12%Traceability and serialization26%23%22%Remote monitoring15%20%25%30%35%5%0%5%10%15%20%25%30%
Executive Summary (Cont.)PAGEQUALITY 4.0 IMPACTAND STRATEGY HANDBOOK5TABLE OFCONTENTSInterestingly, a vast number of existing quality-centered Industry 4.0systems and compliance. Although it’s an advanced topic, this alsoinitiatives are not being led by quality, but by IT, operations, engi-isn’t a story about tomorrow, since leading manufacturers are al-neering, or sales and marketing. Many conversations with qualityready on their Digital Transformation journey. This eBook providesleaders make it clear that a large portion of them do not possess amanufacturers with the tools and insights required to lead the Qual-clear understanding of Industry 4.0 technologies, their application,ity 4.0 transformation. It communicates the technologies, how theyand their importance. This lack of understanding is preventing quali-transform people and processes, existing concrete accomplishmentsty from effectively leading the charge on Quality 4.0.by peers, and guidance to enable the transition from traditional qual-Quality 4.0 isn’t really a story about technology. It’s about howity to Quality 4.0. Regardless of who leads the Quality 4.0 transfor-that technology improves culture, collaboration, competency andmation, those that apply the technology to greatest effect will be theleadership. It’s also about the digital transformation of managementinnovation leaders of tomorrow.Which roles are planning to use IIoT to monitor and improve quality?SECTION1 23 421%22%Information TechnologyQuality Improvement DataQuality Monitoring Data15%Research andDevelopment13%14%Engineering10%QUALITY 4.0 isn't really a story abouttechnology. It's about how that technologyimproves CULTURE, COLLABORATION,COMPETENCY, AND Y0%5%10%15%20%25%30%
Why Quality 4.0?PAGEQUALITY 4.0 IMPACTAND STRATEGY HANDBOOK6Quality 4.0 is a reference to Industry 4.0. The First (real) Industrialand natural worlds. Several critical technology changes have enabledRevolution embodied three revolutionary changes: machine manufac-this, including advances in data, analytics, connectivity, scalability, andturing, steam power and the move to city living for people who hadcollaboration. As the fourth revolution takes hold, it will impact every-previously been agriculturalists. During the Second Industrial Revolu-thing that we do. It connects people, machines and data in new ways,tion, the production line and mass manufacturing drastically reducedit democratizes technologies that were previously only accessible tothe cost of consumer and industrial products. The Third Industrialthe specialized few, and ushers in transformative capabilities such asRevolution was barely a revolution as electronics and control systemsthose in material science and 3D Printing. For quality, these technol-gradually penetrated manufacturing, allowing greater flexibility andogies are important because they enable transformation of culture,more sophisticated products at a significantly lower cost. The Fourthleadership, collaboration, and compliance. Quality 4.0 is truly notIndustrial Revolution is happening around us right now. It extends theabout technology, but the users of that technology, and the processesdigital impact of the third revolution and merges it with the physicalthey use to maximize value.TABLE OFCONTENTSSECTION1 23 4FROM INDUSTRY 1.0 TO INDUSTRY 4.0DEGREE OFCOMPLEXITYFIRSTSECONDTHIRDFOURTHIndustrial RevolutionIndustrial RevolutionIndustrial RevolutionIndustrial RevolutionThrough the introduction of mechanicalproduction facilities with the help ofwater and steam powerThrough the introduction of a divisionof labor and mass production withthe help of electrical energyThrough the use ofelectronic and IT systems thatfurther automate productionThrough the use ofcyber-physical systemsFirst mechanical loom, 17841800First assembly line, Cincinnatislaughter houses, 18701900First programmablelogic controller (PLC),Modicon 084, 19692000Today DFKI, 2011
SECTION 2Defining Quality 4.0
What is Quality 4.0?Quality 4.0 certainly includes the digitalization of qualitymanagement. More importantly it is the impact of thatdigitalization on quality technology, processes andpeople. LNS has identified 11 axes of Quality4.0, which companies can use to educate,GESESNAOGvides a perspective on traditionalDATAMAinitiatives. The framework also pro-OCcan transform existing capabilities andENEMLENTNOOPMC H DEVELPAPsearch, leaders identify how Quality 4.0Mplan, and act. Using this framework and re-TSTYSANALY TICSTEYQUALITY 4.0 IMPACTAND STRATEGY HANDBOOK8PRPAGEquality. Quality 4.0 doesn’t replacethe framework to interpret theircurrent state and identify whatchanges are needed to move toSCCULTU REthe future state.ALAYCOLIONORATL ABC O M PE T EPEOPLEN CYLE ADERSHTRADITIONALQUALITYBIPQUALITY 4.0IT1 23 4them. Manufacturers should useC ONNEC T I V I T YSECTIONrather builds and improves uponILCONTENTSCOMPLIANCEtraditional quality methods, butTABLE OF
USE CASE 1: Managing Recipe VariationPAGEQUALITY 4.0 IMPACTAND STRATEGY HANDBOOK9TABLE OFCONTENTSSECTION1 23 4Let’s bring the framework to life with some examples. Brewing beerprocess data to solve a batching problem that was causing a majoris a touchy process that must balance the relationships betweenquality issue and the loss of entire batches.live cultures, bacteria, time, ambient and equipment temperatures,The brewmasters thought the problem was the relationship be-ingredients, equipment, elevation, and much more. The inherenttween pressure and temperature; instead it was an issue with the tim-variation can cause quality issues.ing of batch processes determined by natural variances in yeast. TheyOne of the largest craft brewers in the US recently implement-used ML/AI to build a model to alter the recipe and optimize batchesed machine learning (ML), artificial intelligence (AI) and historicalon previously unknown relationships. By establishing a new process,the brewer eliminated lost batches associated with this quality issueand recaptured two weeks of extra capacity per lost batch.TAKEAWAY:Original Pale AleFAVORITESAVEPRINTThe brewmasters applied Quality 4.0 analytics to traditional data andprocesses to drive quality improvement and new competencies.INGREDIENTS FOR 1000 GALLONS1000 lb. pale malt200 oz. Amarillo hop pellets250 lb. honey oats200 oz. Citra hop pelletsdry hop after FG40 lb. honey malt40 oz. hop pellets 8%150 oz. Citra hop pellets 12%DIRECTIONSMash 1000 lb. pale 2-row malt with flaked and honey malts at 152 Ffor one hour.Drain, rinse grains, and dissolve 50 lb. pale maltextract syrup into resulting wort.Top off with reverse osmosis or distilled water todesired boil volume and proceed as above.Original Pale Ale10 lbs. pale malt2.5 lb honey oatshalf pound Malt1 ounce. hop pelletshalf-ounce hops 8%2 lb mashed maltdry hop Gafter FMash 3 lb. pale 2-rowmalt with flaked andFhoney malts at 152 for one hour.Drain, rinse grains, anddissolve 5 lb. pale maltextract syrup intoresulting wort.Top off with reverseosmosis or distilledwater to desired boil asvolume and proceedabove.
USE CASE 2: Supplier Quality ManagementPAGEQUALITY 4.0 IMPACTAND STRATEGY HANDBOOK10TABLE OFCONTENTSSECTION1 23 4Supplier quality management (SQM) is a leading roadblock to achiev-The company also expanded this approach to the supply chain toing quality objectives. Suppliers continue to grow in importance asanalyze data in its existing enterprise SQM technology and manufac-their percentage of end products grows, and quality teams have beenturing execution systems with ML/AI. The ML/AI analyzes incomingworking diligently to adopt mature SQM processes and technology.inspection data, and in some cases data from final testing performedOne large contract manufacturer has a Digital Transformationat supplier facilities. The contract manufacturer can now identify is-strategy that encompasses manufacturing and it has deployed Cloud,sues with their suppliers’ manufacturing processes before suppliersbig data and ML/AI to improve tracking of machine performance. Us-do, which enhances non-conforming material process and impactsing the new approaches, the manufacturer can predict and remedysupplier risk profiles.machine downtime and product quality issues before they happen.As a result, the company increased up yield and reduced manufacturing cycle time.TAKEAWAY:Quality 4.0 is a journey; there’s value in a common data model andanalytics, and Quality 4.0 has an impact on management systemprocesses and culture.
SECTION 3Understanding the11 Axes of Quality 4.0
Datathe importance of evidence-based decision making. However, indus-CAPAs are low velocity, whiletry has a long way to go. As shown in the chart, much of the marketstatistical process control (SPC)continues to struggle with evidence while more mature companiesdata is high velocity, and a glob-have mastered traditional data and are now leveraging big data.al fleet of connected devicesReal time visibility ofquality metrics in.Median adoption byinnovation leadersIn God we trust;all othersbring data.—W. Edwards Demingstreaming data is even highervelocity.All othersCustomer service83%43%Supplier 9%Across all four areas65%22%VERACITY: This refers to dataaccuracy. Quality system data is often low veracity due to fragmented systems and lack of automation.TRANSPARENCY: Consider for a moment the ease of accessing and working with data no matter where it resides or whatapplication created it. Leaders should work to develop a common data model to combine structured business system datalike inventory transactions and financial transactions withLet's break down what data is and how to think about it. Data hasstructured operational system data like alarms, process pa-five important elements to consider:structured, and semi-structured. Structured data is highly organized (CAPAs, quality events). Unstructured data is unorganized (e.g. semantics data, data from sensors and connecteddevices). Semi-structured data is unstructured and has hadstructure applied to it (e.g. metadata tags).TRADITIONAL DATATransparency (low to high)VARIETY: Systems gather three types of data: structured, un-events, with unstruc-Veracity (low to high)proaches such as data lakes.Velocity (low to high)nected devices is many orders greater, requiring special ap-Un-structuredquality events, etc.). However, the volume of data from con-Semi-Structuredtional records (e.g. corrective and preventive action (CAPA),(low to high)VOLUME: Traditional systems have a large quantity of transac-rameters, and qualityBIG DATAStructured1 23 4which a company gathers data.VarietySECTIONments for decades. Many recently updated standards re-emphasizeVolume (low to high)TABLE OFCONTENTSVELOCITY: This is the rate atQUALITY 4.0QUALITY 4.0 IMPACTAND STRATEG