1. Where Does The Time Go? Using Production Downtime to Improve the Success Rate of Continuous Improvement Initiatives Jerry Stevick

2. Purpose of this Presentation Many Continuous Improvement initiatives fail to reach their expected goals - often estimated at an 80% - 90% failure rate. Getting better results requires better definition of targets – targets that provide tangible benefits to the overall business. This presentation shows how improving production uptime drives dollars to the bottom line. This presentation also provides a method to easily set targets and track progress.

3. Many CI Programs Fail to Achieve Expected Gains Many CI programs (quality improvement, employee involvement, lean manufacturing, etc) fail to achieve expectations or struggle and sputter as they try to gain and sustain momentum. Some firms are paralyzed or don’t start because the target is not clear or they are not convinced the CI method will solve the business problem Some firms have false starts because the early results don’t appear to have significant business impact Some firms wander chasing symptoms such as inventory or labor efficiency because the underlying issues are not identified Some firms select an improvement program without really understanding where the improvements will come or their impact on the business Some firms struggle because they fail to create metrics to drive the improvements How can we improve the early results of these initiatives?

4. Solving the “Where Do We Start?” Problem One barrier to successful continuous improvement is defining a targets that generate visible gains for the business. Two good options are: Where does the money go? Where does the time go? All production or operating systems have downtime. Maximizing uptime maximizes the productive output of the system. Employees at all levels tend to understand and support business improvements that are clear and productive. Identifying and fixing causes of production downtime generates buy-in and quick returns because it immediately addresses the core purpose of the organization – providing products and services to customers What exactly do we mean by production uptime?

5. What is Lost Production Time? The following chart shows the impact of downtime on actual production Planned Production Time Quality losses Actual Productive Uptime Equipment losses Speed losses The actual productive output of a complex system is often much less than its managers realize and its traditional metrics show

6. Why Focus on Lost Production Time? The Six Major Losses in Manufacturing - these six losses address all reasons for lost production, not just equipment Equipment losses Maintenance - breakdowns and planned maintenance Changeover - model changes and adjustments Speed losses Speed - equipment does not run at designed rate Idle time - time lost to behaviors, training, meetings, etc. Quality losses Defects - production defects that is not useable or requires rework Yield - production losses inherent to process Time sets a high standard - it is the one resource we cannot rework or reuse - when it is lost, it is lost forever

7. Why is it difficult to track lost production time? Traditional accounting and production measurement systems rarely capture the lost time categories Downtime causes are fragmented and often overlooked Responsibility for addressing downtime categories is split Downtime is often understated – limited to maintenance activities Fire-fighting to eliminate the current downtime issue takes precedence over understanding the overall downtime problem

8. Why is it difficult to track lost production time? Lean Manufacturing uses Overall Equipment Effectiveness (OEE) to track and manage the Six Losses definition of downtime. However, most firms struggle to get good data for measuring OEE. Let’s look for an easier way: Goal: to provide a good estimate of the distribution of work activities that is relatively accurate and easy to implement The Work Sampling Study from Industrial Engineering provides a method for generating a quick snapshot of production downtime.

9. How can we track lost production time? The Work Sampling approach: Based on a number of random observations Categories can be tailored to the processes being studied Large sample size leads to statistical validity Good for documenting non-repetitive activities Basic steps of a Work Sampling Study: Define the objective and how data will be collected Classify activity into categories appropriate for the process Prepare data sheets, sample method, random times Develop procedure for analyzing the data Collect data by random observations Let’s look at two examples:

10. 1st Example: Using Downtime to Target Improvements The objective of this study was to find out where the time goes to gain a better understanding of which improvement methods would add the most value Production facility with rubber molding shop (55 tool locations): Management focus on labor efficiency Difficulty scheduling rush orders No target-based improvement program in place Building ahead for large customers hurt remaining customers Capacity “constraints” prevented 100% satisfaction Utilize sampling study of mold shop downtime: Set up data collection sheets unique to this operation Collect data randomly over 4 week period – 2230+ data points Utilize time estimates to demonstrate impact on the number of pieces built and the capacity constraints

11. 1st Example: Using Downtime to Target Improvements HoursEquivalent Pieces Available hours 15,120 5,670,000 Production hours (85%) 12,852 Std Hours Earned (92%) 11,834 4,437,750 Downtime Loss (15%) 2,268 850,500 Maintenance (5.3%) 300,510 Set-up (5.7%) 323,190 Not scheduled (1.5%) 85,050 Cleaning (1.0%) 56,700 Miscellaneous (1.5%) 85,370 Efficiency Loss (8.0%) 453,375 Scrap / Quality loss (6.3%) 355,020 Plant Effectiveness 72% Early Production loss (15%) 665,662 Actual Plant Effectiveness 59%

12. 1st Example: Using Downtime to Target Improvements Lessons from this study: Management’s singular focus on labor efficiency is unlikely to solve their production and capacity problems Significant downtime from scrap, changeover time, and maintenance point to the need for a more well rounded improvement program Scheduling improvements were needed to capture scheduling conflicts around tools and unique models The “old school” practice of dropping in orders for big customers robbed a large chunk of capacity from the current month While management worried about it’s 92% labor problem, it was unaware that the production effectiveness of the facility was at 72% When early orders were considered, the shop was only 59% effective at producing current orders

13. 2nd Example: Comparing Two Similar Plants The objective of this study was to show how to plants with similar products, in the same organization, might have different profiles Comparison of two similar facilities Two automotive control plants compared (similar products) Large plastic molding operations Define targeted and random data collection Data collected over 3 – 4 week period How did the two plants compare? Set up data collection sheets unique to each operation Collect data randomly over 3 week period Utilize time estimates to demonstrate how different processes, people, and business pressures created different improvement opportunities

14. 2nd Example: Comparing Two Similar Plants Plant ABC Uptime = 80.1% Based on 1619 observations Plant XYZ Uptime = 74.6% Based on 4460 observations

15. 2nd Example: What did this Downtime Study show? Lessons from this study: The two plants, despite common division management and similar products and processes, had different downtime profiles Plant ABC displayed a significant need to focus on equipment downtime, primarily time lost to maintenance Plant XYZ displayed a large loss of time to changeover, another form of equipment downtime, that was double the same downtime category in their sister plant Both management teams seriously underestimated the time lost to the non-maintenance downtime categories. In both cases, there was no significant attack on maintenance and changeover prior to this study. Losses of close to 20%, if cut in half, would add significant profit dollars to the business

16. Lessons Learned from Downtime Studies This approach for documenting lost production time works and it is relatively quick and easy to implement The results of most downtime studies “surprise” the managers of the operations being studied. Downtime reduction (greater productive uptime) represents a significant improvement opportunity Every production system is different The production system and its offline support systems (tooling, changeover, maintenance, etc) are closely linked and interdependent Continuous improvement plans can be linked to production system downtime to target improvements with significant payback

17. How Can We Use Downtime Information to Improve? Improve how we manage the production system Balancing offline support systems to maximize production Increasing capacity by reducing lost production time Targeting the most effective improvements Justify new equipment by considering indirect costs Specify equipment to reduce downtime impact Justify upgrades for monitoring and controlling the process Target continuous Improvement efforts Directly monitor and quantify Continuous Improvement program benefits to create and sustain improvement momentum

18. Summary Understanding production time lost to equipment, speed, and quality downtime is crucial to achieving higher performance levels Collecting data and monitoring downtime periodically can be relatively easy – a quarterly or monthly snapshot can overcome barriers to measurement Downtime data can help us focus our improvement efforts Downtime represents a significant improvement opportunity in most production systems Improvements in uptime flow directly to the bottom line