1. NIST Manufacturing Engineering Laboratory Intelligent Systems Division Theme

2. NIST Manufacturing Engineering Laboratory Intelligent Systems Division Outline  John  Study – validity, goals/results,  Data – reliability, precision, accuracy, Time series Event  Measure – Sensor – validity Why do machines break  Anders – PhD thesis on data filtering,  Nils – formalize application of study  E.g., sustainability – what data is necessary

3. NIST Manufacturing Engineering Laboratory Intelligent Systems Division Study Design  Study design may necessitate decisions about the selection of variables, of categories, and of summary measures, so as to provide information on the associations of interest.  Mandated by contractor  What additional information is required, for its own sake or to test the explanations  List of equipment risks  includes age, vendor, country of origin, duty cycle (heavy/light), complexity( number of axes), utilization, maintenance, breakdown history, power source, What other reliability issues are there?

4. NIST Manufacturing Engineering Laboratory Intelligent Systems Division What are the facts?  To answer this question, we must first ensure that we know what the numbers represent and how they were obtained or calculated.  We should not regard inferences as facts.  We will generally need to summarize the finding; for this purpose we may have to calculate rates, percentages, or other summary statistics.  We should see whether there are associations between variables. TRUE?  If so, we should summarize the features of the association not only in qualitative terms (direction, linearity, monotonicity), but in quantitative one, using suitable measures of their strength (such as the difference between rates or proportions).

5. NIST Manufacturing Engineering Laboratory Intelligent Systems Division What to measure?  Requirements  Important, effective, necessary,  Truly Random, difference between time series and event data  Tradeoff between 100% quality and infinite time  KPI  ISO standard: ISO 22400  Process cycle time, process yield, resource time between failure, resource time to repair, process cycle times, process setup and paused times, resource/process energy consumption  DES  Statistical inference of plant behavior  Faults

6. NIST Manufacturing Engineering Laboratory Intelligent Systems Division Results  Key Business Drivers  Key business drivers are the areas of performance that are most critical to an organization's success  Available To Promise  Requires detailed knowledge of available capacity  Reduced Cycle Time  Major performance indicator with a direct impact on corporate profitability  Supply Chain Optimization  Optimizing the manufacturing link in the supply chain –agile & responsive  Asset Efficiency  Requires detailed knowledge of actual use  Agile Manufacturing  Requires ability to quickly synchronize planning and production

7. NIST Manufacturing Engineering Laboratory Intelligent Systems Division What is a factory and what are the data factors?

8. NIST Manufacturing Engineering Laboratory Intelligent Systems Division

10. Planning

11. NIST Manufacturing Engineering Laboratory Intelligent Systems Division