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Alice LaPlante & Maliha BalalaSolving Quality and Maintenance Problems With AI Combining Machine Learning, Deep Learning, and Associative Memory Reasoning to Improve Operations Compl

Alice LaPlante & Maliha Balala

Solving Quality

and Maintenance

Problems With AI

Combining Machine Learning, Deep

Learning, and Associative Memory

Reasoning to Improve Operations

Compliments of

Alice LaPlante and Maliha Balala

Solving Quality and Maintenance

Problems with AI

Combining Machine Learning, Deep Learning,

and Associative Memory Reasoning

to Improve Operations

Boston Farnham Sebastopol Tokyo Beijing Boston Farnham Sebastopol Tokyo Beijing

[LSI]

Solving Quality and Maintenance Problems with AI

by Alice LaPlante and Maliha Balala

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Table of Contents

Executive Summary v

1 Introduction and Primer on Predictive Quality and Maintenance 1

Overview 1

Artificial Intelligence: Clarifying the Terminology 5

More Companies Looking Toward Cognitive Computing 10

2 Complementary Learning and Intel Saffron AI 13

Complementary Learning as the Future of Predictive Quality and Maintenance Solutions 13

Intel Saffron AI: Associative-Memory Learning and Reasoning and Complementary Learning in Action 15

3 Using AI-Based PQM Solutions to Solve Issues in Manufacturing, Aerospace, and Software 19

PQM Issues in the Manufacturing, Aerospace, and Software Industries 19

AI-Based PQM Solving Real-World Issues: Two Use Cases 21

Getting Started with AI-Based PQM Solutions 25

iii

Executive Summary

As artificial intelligence (AI) enters the business mainstream, one of its mostpromising applications is anticipating quality and maintenance problems before

they cause real damage Called predictive quality and maintenance (PQM), these

solutions are being deployed at an accelerating rate, especially in the manufactur‐ing, aerospace, and software industries

But not all PQM solutions are created equal Those based on a combination ofmachine learning, deep learning, and—in particular—cognitive computing create

a truly unique out-of-the-box AI-based PQM solution

This report is organized into three chapters In Chapter 1, we introduce AI-basedPQM and show how today’s market for quality and maintenance applications isevolving In Chapter 2, we show that because none of the various types of AI cansolve all PQM problems alone, applying them simultaneously is the key to suc‐

cess This has led to cognitive computing as a basis for what is called complemen‐

tary learning We also introduce Intel Saffron AI as the only solution applying

complementary learning principles to today’s PQM challenges Finally, in Chap‐ter 3, we discuss using AI-based PQM solutions to solve issues in the manufac‐turing, software, and aerospace industries

v

CHAPTER 1

Introduction and Primer on Predictive

Quality and Maintenance

Overview

Following years of being dismissed as largely “hype,” we’re seeing a growing num‐ber of positive headlines about artificial intelligence (AI): “The artificial intelli‐gence race heats up” (The Japan Times); “Healthcare’s Artificial IntelligenceMarket May Hit $6 Billion” (Forbes); and “Most Americans Already Using Artifi‐cial Intelligence Products” (Gallup) Even the Wall Street Journal is reporting onrecent market advances “After decades of promise and hype, artificial intelli‐gence has finally reached a tipping point of market acceptance,” wrote IrvingWladawsky-Berger in early 2018

Indeed, the artificial intelligence market is expected to grow to $190.61 billion by

2025 from $21.46 billion in 2018, at a compound annual growth rate (CAGR) of36.62%, according to IDC To put that in perspective, in 2018 the average tech‐nology budget for US businesses is expected to grow just under 6%, according toForrester

AI is transforming virtually all industries—from retail, to healthcare providers, tomanufacturing, aerospace, and banking Why? Because AI can deliver results inthe form of insights A report by Forrester forecasts that companies that useinsight to drive their businesses will grow at a 27% annual rate at a time when theglobal gross domestic product (GDP) will rise only 3.5% annually (see

Figure 1-1)

1

Figure 1-1 Revenue forecasts for insight-driven businesses (source: Predictions 2017: Artificial Intelligence Will Drive The Insights Revolution, November 2016, Forres‐ ter)

One segment—and a growing one—of the overall AI applications market is

AI-based predictive quality and maintenance (PQM) PQM is a relatively new tech‐

nology area designed to help companies predict when issues or defects mightoccur in a product, advise on how to identify and fix them, and—the ultimategoal—prevent problems before they cause serious damage AI is significantlyadding value to PQM solutions on the market today

PQM: A Primer

PQM solutions focus on detecting quality issues and improving operational pro‐cesses to address them by accessing and analyzing data, sometimes in real time

PQM is a relatively new merger of predictive quality and predictive maintenance

solutions These separate technology areas previously addressed the two issues—ensuring product quality and anticipating maintenance needs—as discrete, dis‐tinct technologies With PQM solutions, both quality and maintenance activitiesare addressed together rather than as separate issues

The idea behind a PQM solution is that if companies want to gain a competitiveedge, they must prioritize how to allocate their resources, cost, and time when itcomes to both improving product quality and maintaining equipment in a moretimely and efficient manner

Here are some examples of questions that PQM solutions are helping to answer:

• How can we capture experts’ knowledge and skills and streamline themwithin workflows and processes so that they can be shared and accessed byeveryone?

• How can we detect anomalies and failure patterns to determine which equip‐ment and operational processes are likely to fail?

2 | Chapter 1: Introduction and Primer on Predictive Quality and Maintenance

• How can we efficiently triage issues and conduct comprehensive root–causeanalyses?

• How can we optimize spare-parts inventory to reduce inventory costs whileremaining proactively responsive?

• How can we catch and address product quality issues more quickly and effectively and anticipate where they will occur next?

cost-• How can we identify areas to create efficient preventative maintenance pro‐grams to ensure maximum uptime and safety while still maintaining effi‐ciency?

AI-based PQM solutions differ from traditional predictive quality and mainte‐nance ones because they analyze the actual condition of a product rather thanjust using average or expected statistics to predict when quality corrections ormaintenance will be required

The latest PQM solutions harness the data gathered by both the Internet ofThings (IoT) and data from traditional legacy systems Recent research suggeststhat the market for PQM applications will grow from $2.2 billion in 2017 to $10.9billion by 2022, a 39% annual growth rate (see Figure 1-2) Of the top 10 usespredicted for AI in 2021, PQM comes in fifth place, according to IDC

Figure 1-2 PQM market growth to 2022 (source: IOT Analytics)

PQM solutions can be said to have two separate but equal concerns: quality andmaintenance

The longer that companies put off fixing quality issues in products—whether indesign or manufacturing phases—the more costs accrue Indeed, the most expen‐

Overview | 3

sive time to fix a problem is after it’s shipped—when there’s a brand reputationcost exposure added to the costs of recalling the product and fixing the issue.First, consider quality—the “Q” in PQM AI-based PQM solutions allow compa‐nies to work through defects and other quality issues much faster For example,when Intel ships a new chip, there are inevitably bugs reported by OEMs andcustomers Such products are, after all, very complex, with many integrated parts,and Intel must act quickly to resolve any issues that arise.

We can use AI-based PQM solutions to solve quality problems faster, which low‐ers the time-to-market or time-to-resolution while increasing customer satisfac‐tion Not incidentally, PQM solutions don’t just identify the root cause of a single,isolated quality problem, but provide insights into more general issues withindesign or manufacturing, which allows businesses to build better products and,ultimately, increase customer satisfaction and revenues

Next, consider maintenance—the “M” in PQM Intel believes that three of the topdrivers of predictive maintenance include the need to increase uptime, reducerisk, and cut maintenance

Increase uptime

Unplanned downtime is a major cost driver in any industry that must main‐tain large inventories of capital assets For an airline, for example, delayingflights due to unplanned maintenance can cost thousands of dollars eachminute Unplanned shutdowns of oil platforms can run into the millions ofdollars And in manufacturing plants, the costs of disruptions go directly tothe bottom line It is the goal of every organization to eliminate unplanneddowntime in favor of planned maintenance

PQM solutions can help with planned maintenance also, by shorteningmaintenance operations windows

Reduce risk

Businesses strive to comply with safety regulations They also perform pre‐ventive maintenance and take common sense precautions Because of this,the potential for catastrophic accidents to happen is minimized But the risk

is always there The Deepwater Horizon disaster was caused in large part toequipment failure Recently, the engine of a United Airlines flight fell apart inmid-flight Although the aircraft was able to make a safe emergency landing,this incident occurred despite United’s compliance with Federal AviationAssociation (FAA) regulations that are defined to mitigate such risks Whensomething of this magnitude happens, the repercussions go well beyondfinancial

Stop over-maintaining assets

Both the fear of unplanned downtime and the risk of catastrophe occurringlead many businesses to actually over-maintain most of their capital assets

4 | Chapter 1: Introduction and Primer on Predictive Quality and Maintenance

Indeed, many businesses feel that regulatory bodies, such as the FAA for air‐lines and the Federal Drug Administration (FDA) for medical devices,actually require companies within their jurisdiction to maintain assets signif‐icantly more frequently than they need to.

Some predictive maintenance studies report that PQM solutions can reducedowntime by as much as 50%, while reducing maintenance costs between 10%and 40% Manufacturers, for example, can move from a reactive maintenancemodel to a proactive one, giving them insight into when and where machinebreakdowns might occur so that they can keep the manufacturing line going

According to McKinsey, in the manufacturing industry alone, these savings willhave a potential economic impact of nearly $630 billion per year by 2025

Harnessing Dark Data with PQM Solutions

Data is continuously increasing, and businesses are challenged to make sense of itall The vast majority of data is “dark data”—referring to the vast amounts ofuntapped data in the form of human interactions, intelligence, printed content,photos, video, voices, and social media interactions that come in unstructuredforms Notably, IDC estimates that only slightly more than 20% of data is beingutilized today, meaning that 80% is “dark.”

To use this dark data, businesses need to convert this information into a formthat they can understand and use

The AI-based PQM solutions championed by Intel, IBM, and GE harness darkdata from multiple sources to predict potential quality and maintenance issuesbefore they affect customers—and the bottom line In particular, Intel Saffron AIuses several key AI technologies—machine learning, deep learning, and, espe‐

cially, cognitive computing—together in what is called complementary learning to

offer a truly unique out-of-the-box PQM solution

In this report, we interviewed companies from manufacturing, aerospace, andsoftware industries to talk about the key business challenges they face, how AI-based PQM solutions are helping them address these challenges, and how theysee Intel Saffron AI helping them make better decisions, solve problems, and gainlucrative returns

Artificial Intelligence: Clarifying the Terminology

It can be difficult to decrypt all the talk about AI because so many different termsare used—some of them interchangeably—and AI’s capabilities seem to span somany possible scenarios

The best way to think about AI is as a large umbrella of technologies, methodolo‐gies, and algorithms that help humans perform tasks easier, faster, and more effi‐

Artificial Intelligence: Clarifying the Terminology | 5

ciently Under this umbrella resides a large—and growing—collection oftechniques such as machine learning, image recognition, neural networks, speechrecognition, deep learning, natural-language processing, handwriting recogni‐tion, and cognitive computing, among others, many of which overlap or comple‐ment one another to help enterprises resolve challenges.

For example, machine learning focuses on real-world problems by processing—and learning from—large amounts of data Deep learning, which many consider

a subset of machine learning, uses neural networks to be able to sort throughnearly unimaginable volumes of structured and unstructured data to come toconclusions Cognitive computing is a subset of AI that attempts to mimic theway humans think in a way that addresses more complex scenarios for decisionmaking

John Launchbury from the US Defense Advanced Research Projects Agency(DARPA) gives an interesting overview of the evolution of AI in his talk “ADARPA Perspective on Artificial Intelligence.”

At its heart, Launchbury says, AI takes different kinds of mathematically basedformulas (algorithms) to make sense of data and come to a decision on what to

do with it, and in this way creates “intelligent” systems and “smart” things.We’re in what Launchbury calls the “third wave” of AI Today, AI systems havemoved beyond the data-crunching algorithms to human-like cognitive ones withthe ability to explain its reasoning on decisions by making associations based onthe context The ability to form associations autonomously by connecting con‐cepts, observations, knowledge, and senses together Discovering associated pat‐terns for reasoning and inferences is fundamental to both human intelligenceand cognitive computing

In the PQM solutions space, the relevant AI technologies are machine learning,deep learning, and cognitive computing

Machine Learning

Under the larger umbrella of AI, machine learning refers to a broad range ofalgorithms and methodologies that can process large amounts of data so as toidentify issues or trends For example, a machine learning system can learn todistinguish malfunction scenarios of a network router by learning from the train‐ing examples of previous episodes of malfunctions and normal operations of therouter

In other words, it learns from example There’s no need to manually code in

“rules” that it must follow The more data it consumes, the more accurate it willbe

6 | Chapter 1: Introduction and Primer on Predictive Quality and Maintenance

Commonly used machine learning techniques include support vector machines,decision trees, Bayesian belief networks, case-based reasoning, instance-basedlearning, and regression.

Machine learning is experiencing a renaissance within the growth of the AI mar‐ket The “Machine Learning Market - Global Forecast to 2022” report fromResearch and Markets shows that the global machine learning market is expected

to grow from $1.41 billion in 2017 to $8.81 billion by 2022 with a CAGR of44.1% McKinsey estimates that 60% of all current AI spending is on machinelearning

According to one survey, 65% of organizations are already using or planning touse machine learning to help them make better business decisions, whereas 74%

of all respondents called the technology “a game changer” that had the potential

to transform their jobs and industries A full 61% said it was their company’smost significant data initiative for the next 12 months (See Figure 1-3.)

Figure 1-3 Machine learning initiatives are number one for today’s enterprises (source: MemSQL )

Deloitte anticipates that the number of enterprise machine learning deployments

will double between 2017 and 2018, and double again by 2020 However, onedrawback of machine learning systems is that they are “data hungry” and need toprocess large volumes of data—sometimes over a long period of time—beforethey can detect patterns More on this later

Deep Learning

Deep learning is a subset of machine learning that relies on building neural net‐works, which are loosely modeled on how neurons work in the human brain Inthis type of AI, the system extracts digital value from every piece of data it ingests

by asking a series of binary (true/false) questions For example, if trying to pro‐cess an image and determine whether it is the correct face of the owner of a

Artificial Intelligence: Clarifying the Terminology | 7

smartphone, it will ask such things as: “is the hair brown?” “are the eyes blue?” Itthen classifies and weights each piece of data Nodes are arranged in several lay‐ers, including an input layer where the data is fed into the system, an output layerwhere the answer is given, and one or more hidden layers, which is where thelearning occurs by adjusting interconnection weights between the layers to mini‐mize discrepancies between the predictions and the answers.

Deep learning works for large complex datasets on the scale of Google’s imagelibrary or Twitter’s tweets It is not new, but it is rapidly gaining popularitybecause the volume of data that is available is increasing so rapidly, and faster andmore powerful processors can return results in a timely manner

You can apply deep learning to any kind of data, even unstructured data such asaudio, video, speech, and the written word It is being used for a number of real-world issues For example, by using data collected by sensors, self-driving cars arelearning to identify when they come to an obstacle, and how to react appropri‐ately using deep learning British and American researchers recently demon‐strated a deep learning system capable of being able to correctly predict a court’sdecision when given the facts of the case

A new update to the Worldwide Semiannual Cognitive Artificial Intelligence Sys‐

worldwide revenues for cognitive computing systems reached $12.5 billion in

2017, an increase of 59.3% over 2016 Global spending on cognitive computingsolutions will continue to see significant corporate investment over the next sev‐eral years, achieving a CAGR of 54.4% through 2020 when revenues will be morethan $46 billion

Following are the cognitive computing use cases that will see the greatest levels ofinvestment in the near future:

• Quality management investigation and recommendation systems

• Diagnosis and treatment systems

8 | Chapter 1: Introduction and Primer on Predictive Quality and Maintenance

• Automated customer service agents

• Automated threat intelligence and prevention systems

• Fraud analysis and investigation

Combined, these five use cases delivered nearly half of all cognitive computingsystems spending in 2017, according to IDC

A subset of cognitive computing called associative-memory learning and reason‐

ing is also very much based on how humans think First of all, people create

memories Those memories involve entities, where an entity is a person, a place,

a thing, or an event People learn about these entities and create memories Then,they associate these memories to one another When do they see them together?

In what context? How often?

This is how associated-learning and reasoning systems work, too As entitieschange and new data is added, an associative-memory learning and reasoningsystem incrementally adds the new data into memory and builds out more nodesand connections This process of enabling a system to learn on the fly and pas‐

sively develop assumptions about what’s important is called lazy learning or latent

learning.

Cognitive computing systems that use associative-memory learning and reason‐ing unify data at the entity level They create correlations of related data (similarbugs, similar parts, and more) and associate a weight to the similarity Theadvantages of this approach include the following:

• Less data needed

• Less data science involved (model-free)

• Faster and more agile

• More transparent (auditable data)

• Great for individual use cases because data is unified around similar entities(360 views of customers, precision medicine, etc.)

All of these things add up to deliver significant benefits for companies applyingcognitive computing to PQM

According to Keystone Strategy, a Boston-based strategic consulting firm, if 5%

of heavy maintenance costs were prevented via changes to maintenance plans,that would result in $20 million to $40 million of savings for a medium-sized UScommercial passenger airline annually If just 2% of carrier-caused delays wereprevented via changes to maintenance plans, it would yield $5 million in savingsfor that carrier If just 5% of cancellations were prevented due to changes tomaintenance plans, this would yield $23 million in savings for that carrier

Artificial Intelligence: Clarifying the Terminology | 9