bringing data science to manufacturing - internet of … · bringing data science to manufacturing...

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Bringing Data Science to ManufacturingPower Tool Maker Increases Visibility & Decreases Complexity with Machine Learning, Data Science & IoT technologies

Coop, Ph.D.Sr. Data ScientistBig Data & Analytics, Digital Accelerator

Stanley Black & [email protected] | www.stanleyblackanddecker.com

Disclaimer: The views and opinions expressed in this presentation are those of the author and do not necessarily reflect the policy or position of Stanley Black & Decker. Examples of analysis performed within this article are only examples. Assumptions made within the analysis are not reflective of the position of Stanley Black & Decker.

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Background and overview

Who am I, who is Stanley Black & Decker, and what will I be talking about?

March 8th, 2017Coop, Ph.D. 2

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Background

• How do we solve problems we only partially understand?

– Data mining

– Image recognition

– Predicting machine failure

• Given enough information, find an approximate solution– Large amount of data

– Set of “problem/solution” examples

Robert (Bobby) Coop, Ph.D. Computer Engineering with a focus in Machine Learning


Coop, Ph.D.

Sr. Data ScientistBig Data & Analytics, Digital AcceleratorStanley Black & [email protected]

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Who is Stanley Black & Decker?Consumer and professional tools, storage solutions, and other products.


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Who is Stanley Black & Decker?

• Stanley Security Solutions

• Hospital & Healthcare

• Fastening Solutions

• Infrastructure Products

• Pipeline Services

But wait, there’s more!


• Growing by acquisition presents many challenges

• Different information systems

• Different levels of technological advancement

• Culture shock

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Stanley Engineered Fastening – Global Automotive

Twenty plants over six countries. Approximately 75% of the cars on the road contain SEF parts.


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Stanley Engineered Fastening

• 6 locations

• Billions of fasteners shipped annually

–Over 1,500 products

• Demanding customers, harsh penalties

–Defect rates

–Delivery on time in full

North America Automotive

7March 8th, 2017Coop, Ph.D.

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Overview

•The goal

•The process

•Case study

•Thinking big

•Thinking small

•Bonus material!

It’s all great stuff!


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The goal

What are we trying to accomplish, and how do we start?

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Smart FactoryOptimizing operations

Where do we start?


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What makes a good project?

• The scenario to avoid

– Talking with the business

– Identifying some data and analytics

– Successfully completing a project

– Nothing changes and everything goes back to the way it was

• What went wrong?

– Lack of a standard project intake process leads to wasted effort

• Potential projects must be able to answer standard questions

– What data will be used for this project, who is the subject-matter expert for the data, and how many hours a week

can the SME contribute to this effort?

– How will success be defined, how will failure be defined, and how will we determine when the project is done?

– Most important question:

Distinguishing between “neat” and “valuable”


“If we finish this project and are wildly successful, how will the day-to-day operation of the

business change?”

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The process

Approaching the problem, planning the solution, executing the plan

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Cross Industry Standard Process for

Data Mining (circa 2000)

• Understand business problem

• Discover, explore, understand existing data

• Iterate! ‒ What questions about the data can the business answer?

• Extract, transform, load, clean data

• Produce a model

• Iterate!‒ Are there additional data transformations that can help?

• Evaluate performance of model

• Iterate!‒ Does the model perform the task required by the business?

• Deploy into production

• Iterate!‒ Find the next problem and do it again!

The importance of structure: If there’s no battle plan then how will you win?


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Case study

Optimizing operations within Stanley Engineered Fastening


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• Increase Overall Equipment Effectiveness

• Suspicion – maintenance downtime causing

inefficiencies

Teaching a data scientist how to manufacture

Important Note: This is not real data. All data and performance information is presented for illustrative purposes only and was synthetically created.


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• Examining the data– Exploring database

– Database relationships

Teaching a data scientist how to use a manufacturing execution system


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• Data exploration and preparation tends to take the majority

of the time for any project

Some data is hard to understand. Other data is harder.


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Visualizations can help ensure that the business and the

data scientists share an understanding of the data

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•Statistical, advanced analysis – Compare sensor machine logs, down-time

events, part defect rates

– Classical and advanced techniques used

•Problem – why wasn’t there

enough data?

Predictive maintenance



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• Data was being overwritten– 1000 log entries per machine (approximately 48 hours)

• Data architects create database which maintains historical state

Recovering lost data


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Evaluate and ITERATE

• Even with accurate modeling, lift was not ideal

• Scheduling is the better goal

Should we be modeling downtime?



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Scheduling, changeover time, next stepsDowntime due to changeovers and scheduling optimization



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Thinking big

Bringing change to organizations


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Leadership and engagement

• Top-level support– Recurring strategic meetings

– Specific assigned roles for research and testing

• Select a flagship location– Test-bed for technology, vendors

– Regular tours from VIP groups- drive progress and reinforce support

• Driving progress within a business unit– Engage

– Educate

– Re-engage

• Driving progress across the enterprise– Data science center of excellence

– Embedding resources in business units

Change can’t happen overnight


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Thinking small

Bringing change to factories


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Change within the factory

• Data collection, operational rigor– How is information collected?

– How is analytics presented?

– How is progress measured?

• Practical audit procedures– Is the system being used as intended?

• Big brother, incentives, and messaging– System usage as basis for reward

– Advanced analytics as a force multiplier

Operational culture



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Thanks for your attention!

Questions, comments?

Bonus material!In-depth tutorials for constructing predictive maintenance systems.

Predictive maintenance: https://goo.gl/0XE7w2

Estimating remaining engine life: https://goo.gl/4x5KHG

https://goo.gl/0XE7w2

https://goo.gl/4x5KHG

bringing data science to manufacturing - internet of … · bringing data science to manufacturing...

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