Preparing for Analytics

Extracting Meaningful Data from Physical

Processes

Mark Hamblin, Dynamic Manufacturing Solutions

Introduction

• Today we will discuss:

◦ The different types of manufacturers and their

different data requirements

◦ Obtaining data in low-tech environments

◦ How this data can be used to generate

competitive advantages

Manufacturing Sectors

• Three broad categories of manufacturing:

• Process (including batch/hybrid):

◦ chemicals, refineries, extrusion, etc.

• Discrete, high volume low mix:

◦ food, small parts, assembled goods, etc.

• Discrete, low volume high mix:

◦ valves, tanks, general oilfield, etc.

Data in Process Manufacturing

• Process manufacturers are already collecting

vast amounts of data

• Analytics programs within process

manufacturers are often quite mature

◦ Multivariate Analysis

◦ PID loop tuning

◦ Alarm/Event analysis

◦ SQC/SPC

• The results of the analysis are often fed

directly back into the process

Data in Discrete Manufacturing

• The largest number of manufacturers in

Alberta are discrete manufacturers

• Discrete manufacturers are often more “low

tech” than their process counterparts

• Less automation, much more direct labour

◦ Results in fewer measurement points and fewer

data points over time

◦ “Big Data” is a matter of perspective

• Discrete manufacturers pay less attention to

analytics to their detriment

Value of Analytics in Discrete

• Proper analysis often results in:

◦ Reduced expediting costs

◦ Higher margins and fewer bad receivables

◦ Reduced office overhead

◦ Improved customer service

◦ Lower inventory levels

◦ Improved shop productivity

◦ Improved asset utilization

◦ Improved quality

Existing Enterprise Data

• Whether using Excel or SAP, everyone stores

electronic data

◦ Inventory levels

◦ Purchase orders

◦ Shipping documents

◦ Invoices

◦ Payroll data

◦ Customer and vendor contacts

• The key for manufacturers is to bring in shop

floor data as well

Capturing Additional Data

• To move toward formal analytics,

manufacturers must convert their physical

transactions to an electronic form

• Many are still recording transactions on paper

◦ Handwritten receipt confirmations

◦ Parts issued to jobs

◦ Completion of finished goods

◦ Manual time cards

◦ Inventory counts

◦ Etc.

Improving Data Collection

• How do “low-tech” discrete mfg environments

improve data management?

• Capture the data for the physical transaction

when and where it happens

◦ Reduce delays and the potential for lost

information

• Automate the collection of data

◦ Make it easy to record the information you need

for future analysis

Options for Collecting Data

• Software interfaces to machines

◦ OPC, Modbus, etc.

• Computers at point-of-use

◦ Still require manual input

• Hand-held / Hands-free scanners

◦ Simplify data collection and improve accuracy

◦ Convert physical movement to electronic data

• RFID

◦ Can completely automate some data collection

◦ Can still be costly and technically challenging

Bar Code / RFID Scanning

• Bar code scanning is the most accessible

technology (cost & complexity)

• Practically any transaction can be recorded by

scanning a bar code

• Most ERP/MES packages have bar coding

capabilities built-in

• Multiple hardware options

◦ Including hands-free

Case Study 1 – Quality

• Manufacturer was getting product sporadically

returned with bad welds

• At the “sales” level, no pattern was discernible

◦ different products, different customers, different

delivery dates, even different welders

• The installed MES had been capturing

numerous data points for production activities

Case Study 1 – Quality (cont)

• An analysis of the shop floor data was

undertaken

• Bad welds came from different welders,

regardless of product type

• The common factor was the weld bay used

when producing the product

• A physical investigation of the bay found that

the wind breaks were not sufficient when the

overhead door was opened

Case Study 2 – Routing Analysis

• Manufacturer builds standard product and has

expected times for each build operation

• All labour activities (time, work type,

completions) are captured on shop floor

• Planners are able to do statistical analysis on

actual versus expected times to identify:

◦ Deviations from expected time/cost

◦ Tasks where rework is prevalent

◦ Tasks where reengineering may be required

Case Study 3 – Recalls

• Manufacturer records lot information (heat

numbers) for all produced items

• A major recall for almost 1000 heat numbers

required identifying products and customers

that were affected

• Resulting analysis data set included 20,000

affected items and 50 customers

• MES coded to prevent accidental use or

shipment of bad product

Conclusion

• Analytics can significantly improve operational

efficiency and profitability

• The first step in being able to properly analyse

data is to collect the data

• Alberta discrete manufacturers have a

number of options to improve data collection

Q & A

• Questions?