man-088 kickstart your operations optimization with end of ......peat moss field equipment vacuum...

WILHELM BIELERTVice-President & Chief Digital OfficerPremier Tech

Kickstart Brilliant Manufacturing with End-of-Line Equipment Projects

PHILIPPE HAMMONDBusiness Solutions DirectorPremier Tech

GE’s Predix

From jet engine to peat moss plant

GE’s Predix

Get the 20/80in 1 month

3 800 TEAM MEMBERS24 COUNTRIES

41 MANUFACTURING PLANTS14 COUNTRIES

IN +80 COUNTRIESCOMMERCIALLY ACTIVE

Our Brands

HorticultureFrom tradition to innovation

Packaging Equipment

Wastewater Treatment

Biotechnologies

3 Groups

Horticulture and Agriculture

Industrial Equipment

Environmental Technologies

The Project

Analytics project in a Peat Moss plant

The Objective

•Connecting packaging plant equipment through Predix

•To better understand operation downtime

Get the 20/80in 1 month

1Connecting operations

2Generating first glimpse of analytics

3Envisioning next steps

Our 1 Month Project

1) Connecting operations

Peat Moss Field Equipment

Vacuum peat harvesterOnly during dry seasonsVacuum up the thin dried layerNot included in the Analytics project

Peat Moss Bag

•3.8 cu ft / 60 lb bags–Variability of Peat Moss and bags

affect the operation efficiency • Temperature• Humidity• Bag size and plastic quality

The St-Laurent Plant

Packaging Process

1- Vertical form, fill and seal baler

2- Robotic bag palletizer

3- Stretch hooder

Packaging Equipment

•Fully automated with PLCs–Allen-Bradley

• Old & New• All Ethernet enabled

–More than 500 tags

•Fanuc Robot–Palletizer

•Connected on the operation network

Connecting the St-Laurent Plant

Predix Field Agent

• Industrial PC running Predix Machine

• Connected to the Operation Network

• Collect data from PLCs– Already generating more than 500 tags

– Record Equipment behaviors from few tags

• No need to retrofit Equipment

• Deployed in 1 day

Temperature & Humidity Sensor

•Raspberry PI–Temperature & Humidity

sensor• Plug and Play board

–Predix Connectivity• Field Agent installed• Prepared by GE Digital

Innovation team• Easy to install - Connect

network and power

–Simple & Low cost solution

Connecting Operations

Data are already generated across the production line,just need to be collected

Additional data dimension, generate improvement opportunitiessimple devises like Raspberry PI are easy to deploy

2) Generating first glimpse of analytics

Choosing the Right Metrics

Digital Twin

Asset Performance Management

Operate

•Objective –Better understand operation downtime

•OEE - Overall Equipment Effectiveness–The standard for measuring manufacturing

productivity

–Percentage of manufacturing time that is truly productive

–Availability x Performance x Quality

OEE - Basically Simple

OEE = Availability x Performance x Quality• Availability is a ratio of unplanned and planned stops

• Performance compare the real vs top production speed

• Quality takes into account defects vs good parts

OEE - Basically Simple

OEE = Availability x Performance x Quality

• Requires collecting 2 data by Equipment–When a Unit is produced

–When a Unit is rejected

OEE - Complex in the Details

Availability• Running doesn't

mean Producing

• Need to integrate the Equipment status

OEE - Complex in the Details

Performance• Depends on the SKU produced – big bags vs small

bags

• Need to integrate the Equipment recipe or pattern

OEE - Complex in the Details

Planned Production Time• Require to integrate the

production scheduled

• If not available from the MES

– Need to be entered manually

– Or less accurate startegy

Day-to-day Production Metrics

Production scoreboard• Number of bags• Today target vs current progress• Today’s total downtime

Generating First Glimpse of Analytics

Devils are in the detailsTeamwork with your domain expert

3) Envisioning next steps

Current State After 1 Month

Understand the pastHistorical & context data

See present conditionCurrent state & performance

Predict the futureEarly warnings & predictions

Visualize50% completed

What is Missing?

Downtime root causes• Equipment faults• Operators reasons for stopping production• Why the equipment is waiting

Analyzing the big losses and be actionable• Generate tasks based on analytics decisions• Decrease equipment downtime• Improve yield to reduce waste• Reduce process inefficiency

Next Phase

Understand the pastHistorical & context data

See present conditionCurrent state & performance

Predict the futureEarly warnings & predictions

Optimize

Redefine the HMI Experience

• Equipment HMI and Cloud unified experience– Advanced metrics & trends

– Cloud HMI

– Mobile and Augmented Reality

– Integrate Work Instructions

• Reduce training time of new operators

• Empower remote users, on-demand support, and collaboration

Integrate Analytics with Execution

Brilliant Manufacturing=

Brilliant Execution Brilliant Analytics+

Integrate Analytics with Execution

– Better manage production orders across sites

– Optimize assets across availability, reliability, risk, and costs

– Reduce raw material and product variability

What’s Next?

Understand the pastHistorical & context data

See present conditionCurrent state & performance

Predict the futureEarly warnings & predictions

Innovate

Innovation Opportunities

Predictive failure of packaging equipment• Predict equipment issues before they occur• Reduce unplanned downtime by maintenance• Will require integration of new sensors on equipment

Prescribe packaging equipment setup• Reduce change over time• Adjust with operation environment changes• Adapt with raw material and product variability

Envisioning Next Steps

Many opportunities for the future

Next step - Making the Analytics more actionable

Achievement

Analytics project in a Peat Moss plant

Get the 20/80in 1 month

• Most of data are already there, start to collect them

• Devils are in the details

• Opportunity is huge, if your able to integrate your business domain expertise

• Visualize your operation is a good start.

• The next step is to make it actionable to optimize.

Summary

1Connecting operations

2Generating first glimpse of analytics

3Envisioning next steps

Conclusion

You can kickstart a complete factory on Predix in 1 month

Think bigstart small

Thank You