Manufacturing Analytics as a Competitive Strategy

7/24/2019 Manufacturing Analytics as a Competitive Strategy

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MANUFACTURING ANALYTICS AS A

COMPETITIVE STRATEGY

BY FARID ANANI AND JAY GORAJIA


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Manufacturing Analytics as a Competitive Strategy

www. m ento r . co m / val o r2

What if a manufacturing company could provide information to enable its customers who are looking to

improve their component selection process, ideally choosing parts with the lowest defect rates and

counterfeit rating? What if a manufacturer could provide design-for-excellence (DFx) information about every

revision of a product, how it compared to the previous revisions (not only the last revision) to ensure

violations and waivers were monitored and ensure corrective actions were truly done, and old violations were

not re-introduced in a future revision? How much would design customers value a dashboard with productionstatus, roll throughput yield, and running defect correlation to design best practices for their products? What

if a manufacturer could provide a service to monitor quality metrics per product or program, further

strengthening collaboration with the design customers and allowing them key insight as to how designs can

improve over time, which would also lead to improved customer service and customer retention? All this

could be done by leveraging manufacturing analytics information, perhaps providing opportunity for

additional revenue.

This paper defines a manufacturing analytics strategy blueprint and explores how a successfully designed and

executed manufacturing analytics strategy may be leveraged to improve competitiveness and dramatically

improve the ability to provide collaborative services to customers.

WHAT IS ANALYTICS?

Some industry experts assert that we are in the early stages of our 4th industrial revolution (or Industry 4.0)[1]. This is largely characterized by very smart and well-interconnected production equipment that is expected

to be able to govern the production process on its own. There is no shortage in the amount of available data

coming from each individual piece of equipment to let the factory know every relevant piece of information

about what it is doing, what product it is building, how fast, and what

its materials utilization is, just to name a few. This data is made

available to the other machines in the factory so that decisions can be

made based on its contents. Unfortunately, technology has not fully

caught up to the massive amounts of data available yet. Bala

Deshpande said [2], The role of data in manufacturing has traditionally

been understated.

He also said, Manufacturing generates about a third of all data today,

and this is certainly going to increase significantly in the future. Someof it gets collected and stored for future needs, while other data is

simply ignored because of the lack of knowledge regarding what to do

with it or the lack of tools to effectively access that data. However, we

are not only interested in the meaningful patterns; more importantly, we also want to know to leverage those

patterns to create new growth and improve business. In this case, analytics is a means to an objective.

In a recent article [3], Jim Kilmer (Group Vice President at Verizon Enterprise Solution) said Companies that

make large products or systemslike aircraft or locomotive manufacturershave a lot of data, but in the past

theyve just used it internally or talked to their clients about it. Now they are looking to monetize that data.

The ability to create systems to become revenue streams has really changed the way some CIOs do business.

He added by the same token, that tsunami of data is really overwhelming their ability to gain analytics and

insights. Theyre going to need a good strategic plan for how to capitalize and monetize this emerging trend.

RESISTANCE TO CHANGE

The idea of using the data collected in a new way, for a new purpose, and investing in new analytic strategies

requires manufacturers to change how they think. Human natures natural resistance to change is the obvious

risk of discussing relatively new technologies and topics such as manufacturing analytics in the context of

competitive strategies. According to Everett M. Rogers in Dif fusion of Innovations, the landmark 1962

textbook that popularized the study of how new ideas and technologies spread through societies and peer

Analytics is the discovery

and communication

of meaningful

patterns of data.

WIKIPEDIA


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groups, this is what we know today as the adoption curve, as seen in Figure 1 [4]. Early adopters make up

13.5% of the group who will adopt an innovation relatively quickly.

As with many technologists and entrepreneurs, the

early adopters are viewed as the thought-leaders. In

business, they drive customer demand by being aheadof their competitors and provide new types of value to

their customers as few others can. It is also those that

can demand higher rates for these new services and

values. As they say the early bird catches the worm. To

create new services is first to define what those services

should be, and then set the bar and standard for the

rest of the peer group.

Change is inevitable. Forty years ago, bare-board

manufacturers used bomb-sighting machines to

create drill programs, and designers around the world

were using light tables and tape to lay out tracks. As

technologies became more complex and competition

grew, manufacturers had to be able to handle thosecomplexities, be faster, more accurate, and still make

money. This pushed them to invest in computer-aided

design and manufacturing (CAD and CAM) systems.

Fifteen years ago, a standalone design-for-manufacturing (DFM) system was incredibly difficult to justify

within manufacturing. Few invested in it. Then, the early adopters brought in a DFM solution and built

services around them. They benefited from becoming partners and increased the value they could provide to

their customers, while adding another revenue stream or solidifying future revenue. A 2010 Aberdeen Group

benchmark study said that best-in-class companies are 53% more likely to leverage design-for-

manufacturing tools [5].

In todays competitive marketplace, companies are constantly looking for new ways to dif ferentiate. Again,

change is needed, and it is inevitable.

WHY IS ANALYTICS IMPORTANT?

All manufacturers agree on the need to collect and analyze data to extract actionable intelligence.

Improvement initiatives such as Six Sigma and Lean Six Sigma rely on data collection, analyzing, and reporting

collected data to further define and improve processes. Quality management systems are put in place to

ensure the quality of products and to monitor production.

Some of the reasons why data is collected, and hopefully analyzed, today are:

1. Internal KPIs:The manufacturer has a need to measure and display key metrics that are deemed

important to the situation. Several KPIs typically collected and reported include line utilization, machine

performance, feeder performance, overall equipment effectiveness (OEE), first pass yield, rolled

throughput yield (RTY), performance to schedule (on-time shipments vs. all shipments), and the rate of

successful new product introduction (NPI).2. Customer Retention:Todays customers are not only interested in getting quality products and on time,

but they also want to know that the supplier has a sustainable quality management system (QMS).

Having access to the manufacturers quality and other metrics is a QMS requirement.

3. Compliance:Major technology segments, such as automotive, medical, military, aerospace, and others,

have stringent compliance requirements. Traceability and build records for their products must be

maintained. In the case of aerospace and military, those records must be maintained and reportable

many years after their production. Many suppliers find themselves struggling to provide this evidence

Figure 1: The adoption curve [4].


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without the right infrastructure. In many cases, they have data collection, but because of their disparate

systems or sometimes manual systems, they are challenged to provide accurate data in a timely

manner.

Looking forward, there is another reason to collect datato drive business. Although not obvious, the

tremendous amount of data collected today is ripe with information. Extracting knowledge from thatinformation is key to def ining value, as any data scientist would say. Knowledge about customers data and

buying patterns while correlating manufacturing information would have immense value that could be

monetized. Going through the process of data analytics, including problem formulation, method choice,

solution evaluation, and general strategy formulation should lead to new business models and opportunities

for growth.

PREREQUISITES

This creating value that can be monetized is at the core of manufacturing analytics. Analytics is sti ll a product

of good data collection, but with the end value in mind. Deciding which data should be correlated and what

analytic models to use should be done carefully, because as Figure 2 describes below, garbage in, garbage

out, in both the quality of the data and the analytic model.

REMOVING TECHNOLOGY CONFUSION

When discussing analytics today, there is a natural tendency to

associate this topic with just the infrastructure of Big Data. Big-

data infrastructure is used by retailers to track user web clicks to

identify behavioral trends that improve campaigns, pricing, and

inventory. Utility companies use it to capture household energy-

usage levels to predict outages and to invent more efficient energy

consumption. Governments are using it to try to detect and track

the emergence of disease outbreaks via social-media signals. Big

data is new and unique in collecting large amounts of data to be

processed quickly with complex correlations.

Databases such as Apache Hadoop or other NoSQL databases

with analytics infrastructure such as MicroStrategy and others are big-data technologies. After the volume of

data is reviewed, there are many options. Traditional databases such as Microsofts SQL Ser ver, Oracle, and

others may still be just as effective in leveraging manufacturing analytics for competitive strategies. Lets not

confuse the technology with the goals.

DATA SOURCES

Several different, and currently disparate, systems have to come together into an analytics engine to achieve

the level of analytics needed. Those systems typically would include:

Enterprise resource planning (ERP)

Design for manufacturing

Manufacturing execution system (MES) and/or quality management system (QMS)

Supply-chain integration software

Most ERP systems include the accounting and purchasing of materials and, in many cases, also maintain the

inventory and high-level scheduling. From ERP, information about purchasing trends of components,

availability, lead-time plans vs. actuals, obsolescence, and, most importantly, cost would be available.

A DFM solution would provide risks, cost avoidance opportunities, and constraints adherence. Issues that may

affect the manufacturability of a design would be identified early, as shown in Figure 3. Manufacturers are

able to price their ser vices more accurately and can show cost-drivers in a more granular way. For example, if

Figure 2:Garbage in, garbage out.


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DFM analytics show that a certain design issue is likely to

decrease yields by 2%, then price can be adjusted

accordingly.

For the purpose of putting manufacturing analytics in

place, running DFM on a design-by-design basis is notenough. That information must be parameterized so that,

for every design analyzed, information is stored to run

reports and correlations against. Typical DFM reporting

outputs, such as the one shown in Figure 4, should be

further extracted so that meta-data regarding the origins

of the design and customer are associated with the

components information and board information to

connect the issues found.

Another important system that should be ideally

integrated into the analytics infrastructure are MES and/or

QMS system.

Line utilization, machine performance, feeder performance, OEE, first pass yield, RTY information tied to the

stages in manufacturing, then to design information, components, materials, customers, and revision should

be accessed and fed into the manufacturing analytics engine (Figure 5). Powerful correlation data is available

when integrated, for the use of building new business models by manufacturers. This is the most complex

integration, but it also provides the highest potential value.

Finally, integrating supply-chain sources into the manufacturing analytics would be ideal. Several data

aggregators in the industry provide this information through Web-based connectivity tools for automatic

query and retrieve capabilities. IHS CAPS Universe and Silicon Expert Technologies are a couple of

component data providers, although there are several more.

Figure 3:Compliance to manufacturing guidelines.

Figure 4: Typical DFM report output.


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They provide a lot of detailed data, including:

Electrical part parametric data

Part obsolescence

Part change notification (PCN)

RoHS/compliance Market part availability

Lead time (from vendor partners)

Inventory information (from vendor

partners)

Cost

Also, the Electronic Components Industry

Association (ECIA) maintains a list of partner

companies [6].

STEPS FOR LEVERAGING

MANUFACTURING ANALYTICS

1. UNDERSTANDING VALUE AREAS AT CUSTOMERS SITES

One of the key success criterion to using manufacturing analytics to build new business models requires an

understanding of the customers flow. Where in the OEMs design flow can factories add value?

In a typical PCB design flow, as shown in Figure 6, several stages would benefit from information that would

be provided by a manufacturer.

2. LIBRARY AND PART MANAGEMENT

Early in the product development flow, engineers select components and other hardware that will most likely

be used in the final product. This is normally a selection based on electrical parameters that meet the design

functional requirements. In an inter face, shown in Figure 7, the engineer selects from multiple options that

may have similar function and form.

At this stage, however, vital component information may not be available, and the selection of the component

will then lead to using outdated components, costly components, or components that have a history of

counterfeit or quality issues. The following information would be ideal at this stage:

RoHS status compliance

Cost

Defect rating based on manufacturing actuals

Obsolescence before end of life

Counterfeit ratings Compliance to manufacturing constraints and design guidelines

If a factory puts the manufacturing analytics infrastructure in place to collect, organize, and build analytics

around component-related defects, coded to the vendor, vendor part number, distributor, and related form

factor and functional capabilities, this would provide valuable insight into component usage data and related

yield-related risks. Again, as the various systems become integrated into the manufacturing analytics engine,

adding supply-chain information, the correlation information can be organized and presented to customers,

Figure 5: Manufacturing data collection.

Figure 6:Typical PCB design flow stages.


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as additional value-add services. This correlation package

would be a powerful way to get closer to customers, further

solidifying customer retention, and adding value far beyond

manufacturing products for them. Manufacturers could reach

a new level of partnership, for which OEM designers would

see great value.

3. DESIGN CREATION AND LAYOUT

Leveraging DFM solution integration in a manufacturing

analytics engine can be a powerful business driver for

customer value. As mentioned above, the role of the DFM

solution is to review each design revision for manufacturing

risks to yield, design compliance, and cost avoidance.

However, if a manufacturer would have a way to create a DFx scorecard of the previous revision into the new

revision project to track DFx board-by-board within a product development project, this would be vital

information for a design organizations ability to improve their designs, not to mention improve their internal

capabilities.

In our discussions with OEMs and manufacturers, recurring issues are evident. The DFM solution may haveidentified a violation, and it was asked to be f ixed. In revision 2, it is truly fixed. However, in revision 3, the

violation recurs, perhaps because of a change in the design engineer or a flaw in the design process; either

way, knowing this information would dramatically assist the design organization and add business value.

In addition, extraction of design properties and characteristics such as the ones listed below, would further

improve the intelligence of the data and add value to the correlation of the analytics. The more design

parameters extracted and saved as meta-data, the more correlation data can be created. There should be over

100 design parameters available, such as:

Minimum finish-hole size Layer count

Drill-layer count PCB thickness

Micro-via size Buried-via size

Blind-via size Number of via drills

Number of Pth drills Number of NPth drills

Board-size width Board-size length

Component-count top Component-count bottom

Component count Pin-count top

Pin-count bottom Pin count

Combining design parameter extraction with DFx-result score-carding would further allow an analytics engine

to provide correlations on the problem areas to help predict future issues. Hypothetically, if violation X occurs

when a certain combination of design properties are present, we can model that correlation in the analytics

engine. This is the basis of predictive analytics.

4. ADDING VALUE TO ASSEMBLY, TEST, AND INSPECTION

Todays assembly, test, and inspection equipment can provide large amount of varying data at high speeds

regarding performance, quality, and traceability. Being able to mine this data in real time to obtain actionable

intelligence will reduce defects and rework costs as yields and throughput rates are improved.

How can manufacturers use this data to build additional business value with their customers?

Figure 7:Typical designers schematic interface.


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The answer is in the correlation provided by an analytics engine. Figure 8 shows an example of how this can

be done. When an initial revision of a design is sent to a manufacturer, data correlated from the DFM solution

and QMS can be graphically depicted into a hotspot map for the customer, identifying areas of highest

defects, before a revision spin (ECO/ECN) is sent.

Designers recognize the value inknowing where quality issues are, and

having that information at their f inger-

tips would be highly valuable, to improve

the quality of their designs, and to

manage target costs.

Computrol is a US-based contract

manufacturer focused on low-to-mid

volume production. The companys

president, Charlie Scott, Sr., commented

about how analytics provides value

added to their business.

Computrol uses an analytics strategyengine or business intelligence (BI) for

several different things. The BI dashboard

gives real-time feedback on products that are in WIP. We use BI for weekly SPC data (FPY, % rework and DPM

levels) to spot trends on products that we are currently running in each area of assembly, he said. We track

and trend our top defects over the last 90 days so that we can focus engineering efforts on the biggest issues

first. Operators/inspectors view defect and SPC data from previous runs to know what defects and locations

were problem areas in the past. BI allows us to generate custom reports for anything that we have collected

data on. We have the ability to generate defect and SPC reports that we can send to our customers so that

they can see data specific to their products. BI also tracks specific serial number history. It allows us to see

what has been done to each board, by whom, and when it was done.

Leveraging this data to partner with customers would create higher demand for manufacturing ser vices,

greater customer retention, and most importantly new avenues for business value.

5. ANALYTICS DURING POSTPRODUCTION

Post-production analytics does not get too much attention typically. It is often overlooked unless a

catastrophic event happens resulting in a major recall of products. Most companies are investing their

resources in engineering and designing the next product, and spend less time on extracting value and lessons

learned from existing products already released to the market. This can represent missed opportunities for

enhancing existing and future designs.

Post-production is probably one of the more complex and challenging areas because field returns and repairs

are not handled the same way production is. There is no data being automatically generated and ready for

capturing. However, companies that do not invest in thoroughly analyzing repair data can put themselves at

risk. These results could point to a common latent failure that was not detected or expected early enough.

This is an oppor tunity to:

1. Influence the design, layout, and component selection of the next product generation.

2. Influence designs of other product lines that may be managed by different groups, or worse yet, in

different global regions.

3. Improve the manufacturing process.

4. Improve the test coverage and strategy.

Figure 8: Hot spot map.


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Trends on a particular model, or a certain lot of a specif ic component can be easily identified. Data analytics

for field repairs and returns can aid in a comprehensive and accurate root-cause analysis regarding why

products are failing in the field. It s an easy way to see if a unit has been previously returned and why. The

analytics engine can help determine the true mean-time-between-failures (MTBF) for each product so the

stakeholders can see if the MTBF is within acceptable limits and provide this vital information to program

groups within OEMs. Partnering services in this area is not new, but with the right manufacturing analytics inplace, it would add further business value to the t ype of dashboards and knowledge about products.

CONCLUSION

The intention of this paper was to def ine and quantitatively conf irm how a manufacturing analytics strategy,

when successfully designed and executed, may be leveraged to improve a manufacturers competitiveness

and dramatically improve their collaborative services to their customers. Although more than 20 OEM design

groups and contract manufacturers were contacted, many of whom provided ample qualitative information,

the topic of analytics to this degree may be too new to be confirmed quantitatively.

While many companies have implemented a DFM solution, and have deployed various factory systems such as

MES and QMS systems to collect data on the factory floor for the most part, electronics manufacturers have

not yet f igured out how to fully leverage the massive amount of production data, which is readily available, to

gain significant improvement in productivity (cost-efficiency) and to leverage this data to drive new businessgrowth.

Jaakko Paavola, MES Manager at Salcomp, a Finnish-based contract manufacturer with several plants around

the world, stated that they have collected and stored terabytes of production and quality data from their

production floor over the years. He further

mentioned that due to the lack of an analytics

engine, which is powerful enough and intelligent

enough, much of the data just sits there in memory

spaces. In other words, not providing any added

value or competitive edge.

Paavola added that Salcomp does recognize the

need for analytics and is considering several

options for this purpose in order to improve

visibility and gain a better insight about their

factorys performance.

Manufacturers not only can use analytics internally

to boost business performance. The tremendous

amount of data collected today may be ripe with

information. Extracting knowledge from that

information is key to def ining value. Knowledge

about customers data, and buying patterns while correlating manufacturing information would have

immense value that can be monetized. Going through the process of data analytics, including problem

formulation, method choice, solution evaluation, and general strategy formulation should lead to new

business models and opportunities for growth.

Computrols marketing director, Jon Hanson said, A good analytics strategy engine is vital for a world class

electronics contract manufacturer like Computrol. Not only does the tool help us understand what our driving

factors are in production (good and bad), it helps us share that information with potential and existing

customers. Being able to gather, evaluate and share data easily, helps us to talk the talk, and more impor tantly,

walk the walk that our customers expect from us.

Many contract manufacturers confirmed that manufacturing analytics strategy when defined, designed, and

deployed at a manufacturing organization, with the right integration with data-collection systems,

Companies that capitalize on

predictive analytics are achieving

new breakthroughs in business

process improvements and cost

efciency. These organizations are

using insight and innovation to

strategically position themselves

to capture marketshare. [7]


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2015 Mentor Graphics Corporation, all rights reserved. This document contains information that is proprietary to Mentor Graphics Corporation and maybe duplicated in whole or in part by the original recipient for internal business purposes only, provided that this entire notice appears in all copies.In accepting this document, the recipient agrees to make every reasonable effort to prevent unauthorized use of this information. All trademarksmentioned in this document are the trademarks of their respective owners.

F o r t h e l a t e s t p r o d u c t i n f o r m a t i o n , c a l l u s o r v i s i t : w w w . m e n t o r . c o m

10-15 TECH13380

supply-chain integration systems, and a DFx solution, may be leveraged to create predictive analytics, fortify

partnerships with customers by providing never-before seen correlations of customer data, and develop new

business offering to further improve a manufacturers competitiveness, while dramatically improving their

collaborative services to their customers. This paper outlines many of these high-level approaches, ideas, and

strategies. Furthermore, during our research, OEM design organizations confirmed that such services would

dramatically improve their partnership with suppliers, and improve overall design quality, cost, and design-to-market timeframes [8].

For more information on how the Valor Manufacturing Solutions Suite of tools can help to use analytics

effectively and improve competitiveness, visit us at https://www.mentor.com/pcb-manufacturing-assembly/.

REFERENCES

1. John Donovan, Mouser Electronics, The 4th Industrial Revolution Is Upon Us, ECN, October 29, 2013.

2. Bala Deshpande, Simaphore, How predictive analytics can shape manufacturing of the future, Proceedings of

Predictive Analytics World Manufacturing, October 1, 2013.

3. Maria Montenegro, Verizon, Why Manufacturing CIOs Must Harness Data Analytics, Verizon Enterprise News, June

24, 2015.

4. Rogers, Everett M., Diffusion of Innovations (first edition). Glencoe: Free Press, ISBN 0-612-62843-4, 1962, p. 150.

5. Michelle Boucher, Aberdeen Group, PCB Design: A Guide to Optimizing Design Engineers, 2010.

6. ECIA, http://www.ecianow.org/membership/member-directory/

7. IBM, Business Analytics for Banking: 3 Ways to Win, September 2010, p. 3.

8. This paper was originally published at SMTA International, September 2529, 2016, Rosemont, Illinois, USA.

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Manufacturing Analytics as a Competitive Strategy