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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?

Mark Hambliin - Preparing for Analytics - Extracting Meaningful Data from Physical Processes  
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