Where Does The Time Go?

Using Production Downtime to

Improve the Success Rate of

Continuous Improvement Initiatives

Jerry Stevick

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.

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?

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?

What is Lost Production Time?

The following chart shows the impact of downtime on actual production

Planned Production Time

Actual ProductiveUptime

Quality losses

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

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

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

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.

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:

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

Hours Equivalent 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%

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

1st Example: Using Downtime to Target Improvements

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

Plant ABC Uptime = 80.1%Based on 1619 observations

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Plant XYZ Uptime = 74.6%Based on 4460 observations

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2nd Example: Comparing Two Similar Plants

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

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

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

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