Metrics for Operations Process Management White Paper

Metrics for Operations Process Management Some experts question the origin of the quote “you can't manage what you can't measure." Some attribute this quote to Dr. W. Edwards Deming, but others point out that Dr. Deming’s teachings warn us about relying on numbers alone for management decisions. Nevertheless, none question that making informed management decisions does indeed require good performance metrics. In fact, a study conducted by Manufacturing Enterprise Solutions Association (MESA) International and Industry Directions Inc. in 2006, “Metrics that Matter,” found that a manufacturer’s success was largely correlated to how effectively they measured financial and operational performance. Respondents using plant dashboards were more than twice as likely to have improved significantly in cash-to-cash cycle times and total inventory on-hand. At the same time, the study revealed that only a fraction of manufacturers who participated had effective methods in place for collecting data and measuring performance. A Manufacturing Operations Management (MOM) or Operations Process Management (OPM) system, like iBASEt’s Solumina, provides a platform for consistently collecting reliable real-time data from day-to-day operations, and automatically rolling up this data into reliable departmental and enterprise level performance metrics or Key Performance Indicators (KPIs). One of the advantages of having this functionality integrated into the manufacturing system is that Solumina collects all this data as a side effect of performing the manufacturing documentation and verification required anyway for regulatory compliance. There is no extra effort to log data into an additional system just to track metrics or KPIs. Solumina provides metrics to support operations at three different levels:  Executive Management Oversight  Shop Floor Process Control  Continuous Process Improvement Initiatives

Executive Management Oversight The executive management team needs access to reliable information to make informed decisions. Are we comparing apples to apples or oranges? Corporations relying on manual methods to roll up enterprise manufacturing data into metrics are often faced with this question. For example, different analysts might have different views of what “on-time” means or what “end-of-week” means. Thus manual manipulation of data before it rolls up into enterprise metrics can cause misleading results. The Gartner Research 2006 report titled “CFO Finance System Priorities” pointed out that 80% of organizations found their decision making adversely affected by the silos of information which create inconsistent, inaccurate and conflicting sources. Page 2 Copyright © 2010 iBASEt.

   

 

 

In order to eliminate this problem it is necessary to automate the data collection processes and create one enterprise “truth”―a truth acquired through automation with leading edge technology that enables real-time flow of accurate, current and trusted information. Questions on the mind of the management team might change periodically depending on the business climate but usually include questions like the following:  How am I doing? What is our current capacity, status, schedule attainment?  What can I make today? What is current work-in-process and raw material status?  Where should I make it? What is current assignment of resources to production among facilities – internal and outsourced?  Where am I underperforming?  Can I demonstrate compliance? Am I ready for regulatory audits? These questions are similar across multiple industries, but the relevance of certain metrics is different. For example, in the process industry the full utilization of expensive equipment might be a priority; in low volume manufacturing of very expensive products, it might be more important to look at schedule adherence and cycle time for expensive work-in-process. Measures like Overall Equipment Effectiveness (OEE) might be very important to the process industry, but other industries might prefer to drill down into the components of OEE = Availability x Performance x Quality depending on the enterprise priorities. Several different metrics could feed into the components of OEE depending on the industry:  Availability o Ratio of Operating Time to Planned Production Time o Percent of calendar hours that equipment was available for production or equipment up-time o Percent of calendar hours product is not held idle by constraints like availability of parts, skilled resources, or equipment  Performance o (Ideal Cycle Time x Total Pieces)/Operating Time o (Total Pieces/Operating Time)/Ideal Run Rate o Standard Labor Hours/Actual Labor Hours  Quality o Good Pieces / Total Pieces o (Actual Labor Hours – Rework Labor Hours)/Actual Labor Hours

Page 3 Copyright © 2010 iBASEt.

   

 

 

The right metrics allows operations management to:  React before the competition  Take the right supplier or acquisition strategy  Determine whether to enter a new market  Analyze the success of a product launch  Accurately incentivize operations Management Dashboard

Solumina provides a configurable Management Dashboard to accommodate requirements for diverse industries. The organization and display formats for different metrics are configured to the user’s preference. Dashboard charts can drill down to specific locations, departments and work centers.

Figure 1. Example of Solumina Management Dashboard screen.

Page 4 Copyright © 2010 iBASEt.

   

 

 

Management Alerts

Dashboards provide proactive methods to manage operations; however, problems will probably still arise and require prompt action from the management team. In Solumina, Management Alerts provide a mechanism to automatically dispatch alert messages to subscribers of events triggered by reaching a certain metric threshold or logging a special event like a line stoppage.

Figure 2. Example of Alerts configuration in Solumina Management Dashboard.

Page 5 Copyright © 2010 iBASEt.

   

 

 

Shop Floor Process Control Solumina OPM software provides many process control mechanisms including enforcement of inspection and certification verifications, serial number traceability, controlled change management, and rules for approval of deviations. The following process control mechanisms rely on real-time metrics based on data collected and calculated by Solumina in the background. Visual Controls

Lean Manufacturing practitioners emphasize the importance of straightforward simple visual controls to guide operations, avoid mistakes and stay on track. The goal of visual controls is to make the job more intuitive by displaying in easy to interpret graphics any situations that are moving outside of the expected results. Information displayed at monitors on department aisles can continuously display status and highlight issues for quick visual oversight by any supervisor walking through the plant.

Figure 3. Example of Graphical Job Dispatch

Real-time Performance Visibility

Real-time performance visibility is required to support effective program metrics and decision making. Metrics including process cycle time, on-time completions and defect per million opportunities, provide a way to visually see plant floor status and issues.

Page 6 Copyright © 2010 iBASEt.

   

 

 

Exception-Based Supervisory Alerts

Exception-based tools focus attention on conditions that fall outside of an accepted norm or benchmark. Solumina creates alerts for supervisors based on configured rules for escalation of conditions like deviations to schedule or aging of issues requiring disposition. Issues holding operations at the shop floor are immediately flagged in real-time and documented as Holds which are routed automatically to appropriate personnel for action. Statistical Process Control (SPC)

SPC is an effective method of monitoring a process through the use of control charts. Control charts enable the use of objective criteria for distinguishing background variation from events of significance based on statistical techniques. Much of its power lies in the ability to monitor both process center and its variation about that center, by collecting data from samples at various points within the process. Variations in the process that may affect the quality of the end product or service can be detected and corrected, thus reducing waste as well as the likelihood that problems will be passed on to the customer. SPC provides an early detection and prevention system. Out-of-control warnings are displayed in real-time to the mechanic or inspector at the shop floor.

Figure 4. Example of Control Chart in Solumina

Page 7 Copyright © 2010 iBASEt.

   

 

 

Continuous Process Improvement Initiatives Where should we focus process improvement efforts? Six Sigma practitioners use the DMAIC process to systematically determine best areas for improvement and variability reduction in a manufacturing process. Lean practitioners refer to the Toyota problem solving methodology and to kaizen events. Any of these methodologies can be supported with metrics derived from Solumina data.

Figure 5. The DMAIC Continuous Improvement Process

Solumina supports the DMAIC process with a Corrective and Preventive Action (CAPA) system to track process improvement initiatives and metrics that feed into three of the phases in the DMAIC process:  Measure  Analyze  Control Measure

In this phase information about the target process is gathered. Metrics are used to determine current performance and to identify problems and issues. Areas requiring periodic assessment for potential improvements include: Capacity, Cost, Schedule, and Quality.

Page 8 Copyright © 2010 iBASEt.

   

 

 

Analyze

This phase involves identifying the root causes of the defects using appropriate statistical tools. Root cause analysis may start with brainstorming sessions among team members and move on to data analysis that further explores multiple potential causes. Different kinds of charts and diagrams are used in data analysis including histograms, Pareto charts, and scatter charts.

  Figure 6. Example drilling down in Solumina from Pareto diagram to related Discrepancies

Control

This phase involves institutionalizing the improvement methods. Policies, procedures and other management systems are modified to achieve the goals. Performance results are periodically monitored to ensure that the improvements are sustained.  

References:    

“Metrics that Matter”, MESA International and Industry Directions Inc, 2006 “CFO Finance System Priorities,” Gartner Research, 2006 “CPM Demands Data Quality,” Gartner Research, 2004 “Lean Six Sigma,” Michael L. George, McGraw Hill, 2002

  Page 9 Copyright © 2010 iBASEt.