Leveraging Technology to Improve OEE in Manufacturing

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Chapter 1

Performance Measurement Needs of Manufacturing Management

Overall equipment effectiveness (OEE) is an essential component of every manager’s array of tools used to control a production operation. This KPI strategy can ensure that processing line effectiveness is monitored and CI strategies, cost optimization, and customer quality demands are met successfully.

Several performance KPIs are used to help managers get realistic data about production line status and performance. The OEE metric’s ability to benchmark performance and track progress at various critical points throughout the process is very useful. Using this measure, it becomes easy to quickly identify unusual downtime penalties, below-standard processing efficiencies, and low quality standards. This, in turn, allows immediate action to be taken to mediate the condition and get production back on track.

What Is OEE?

OEE is a comprehensive calculation used to gauge the effectiveness of a machine, production line, or factory. The metric encompasses several important manufacturing activities. These activities include availability, performance, and quality, which are key elements of most manufacturing processes. All three activities are part of OEE, so managers can get a quick, numeric look at current process line productivity. Comparisons of line performance between time periods show productivity levels and trends. Comparisons of OEE scores between similar processing lines or machines across the factory can be used to evaluate relative performance. OEE can be calculated to cover any period and historically has been calculated manually at the end of the day or the week. Employees collect the required data for these variables on the line and then convert the data into the all-inclusive OEE.

More specifically, OEE is the overall equipment effectiveness of a defined production process during a specific operative period or mode and during which all activities related to production, personnel, and inputs are accounted for. The defined production process is the start and end boundary under review such as from depalletizing to palletizing or from receipt of materials into finished goods warehousing and out through shipping.

Availability

Availability is a number equal to the amount of time available to the production line to manufacture a product. Overall availability is the time the production line is running as a percentage of planned production time.

To calculate the availability value, use the following equation:

  • Availability = runtime / planned production time, where
  • Runtime = planned production time - stop time, where
  • Planned production time is scheduled production time, and
  • Stop time is any measurable stop or delay in production

Performance

The value for performance takes into account the speed at which the production line is running. This value is a comparison of the fastest cycle time the production line can achieve under ideal circumstances compared to the actual cycle time. The number is a percentage based on total production time and counts.

Performance can be calculated using the following equation: 

  • Performance = (ideal cycle time x total count) / run time, where 
  • Ideal cycle time is the fastest cycle time under optimum conditions, and
  • Total count is the total quantity produced including reworked and scrapped, and
  • Run time is the actual line run time

Quality 

The quality figure used in the OEE calculation is similar to first-pass yield. This value is a percentage of “good” parts (parts that pass through production and do not require rework) as compared to total parts produced, including reworked and scrapped parts. Quality is the last of the three key indicators making up OEE and is calculated using the following equation:

  • Quality = good count / total count, where 
  • Good count is the number of good parts that pass through the production cycle the first time (no rework), and
  • Total count is the total number of parts produced including good parts, scrapped parts, and reworked parts.

 The three values above are then multiplied together to develop a value for OEE: 

OEE = availability x performance x quality and is expressed as a percentage of overall equipment effectiveness

"Tracking OEE metrics allows companies to not only improve productivity but also increase financial results."

Chapter 2

OEE as a Financial Measure

Typically, OEE is used as a performance measure focused on the manufacturing floor. However, the measure can also be used in gauging the impact of improvements on a company’s financial position. In this case, OEE should be calculated across an entire plant or for a financially based operating unit.

The following example illustrates how even small changes in overall OEE can increase profitability.

Financial Implications of Changes in Global OEE

A manufacturer produces widgets at the rate of 28,908 units annually.

The plant is producing widgets at a production rate of one widget every ten minutes. On an annual basis, theoretical production is calculated to be 365 days x 24 hours per day x (60 minutes per hour/10 minutes per unit) or:

Theoretical Production = 365 days per year x 24 hours per day x 6 units per hour = 52,560 units (widgets) per year

The price of each unit is $2000 and has a variable cost of $ 15.00 per unit. This results in a margin of $ 5.00 per unit.

The OEE for this manufacturer at his production level is 55% with components as follows:

  • Availability = 75%
  • Performance = 82%
  • Quality = 90%
  • Making the OEE = 55% (.75 x .82 x .90 = .55 or 55%)

Using a variety of improvement techniques, this manufacturer increased its OEE components as follows:

  • Availability = 81% (an increase in the value of 6 percentage points)
  • Performance = 88% (an increase in the value of 6 percentage points)
  • Quality = 90% (no change from the original case)
  • OEE then becomes = 64% (.81 x .88 x .90 = .64 or 64%)

With these improvements this manufacturer now makes 33,638 widgets annually (.64 x 52,560 = 33,638)

The impact on profitability is shown in the table below.

Table I – Calculation of Improvement in Profits with Improvements in OEE

A profit increase of 62% was realized with a 6% increase in availability and performance while holding quality level.

Chapter 3

Calculating EBITDA

Considering the above scenario, an additional consideration should be the OEE impact on earnings before interest, taxes, depreciation, and amortization (EBITDA) change potential. 

EBITDA is a financial measure of a facility’s operational profitability and potential cash flow, and a measured change in EBITDA caused by OEE variation can quickly show net profit increase. Net profits take into account non-cash items such as depreciation, as well as taxes and interest. Many manufacturers, however, incur substantial charges for non-cash items such as depreciation resulting from large investments in plant and equipment.

Because normal earnings calculations include depreciation and other charges, earnings calculated traditionally may not represent the true performance of the plant, and, even when performance is excellent, it may show a loss. Improving OEE values, on the other hand, can have a positive impact on EBITDA.

In the above example, EBITDA would be calculated without including depreciation ($25,750.00) and a portion of “Other Fixed Costs” (assume $5,000.00 interest payment on a note), resulting in an EBITDA of $87,642.00. This number is significantly better than the traditionally calculated earning of $56,890.00 shown in the example above.

Note: Older and fully depreciated equipment can get an appraisal value increase when an IIoT betterment is deployed. For example, adding TileConnect smart sensors to older production equipment can not only increase its asset appraisal value but also generate data that can increase the equipment’s production performance.

Chapter 4

How Does OEE Work?

OEE provides valuable insight into how effectively a manufacturing operation is running and what percentage of your planned production time is truly productive.

High OEE values often represent ideal production circumstances, and 100 percent OEE is usually a theoretical objective. World-class OEE figures are considered to be around 85 percent, as a benchmark. Numbers in the 60 percent range are relatively common but indicate improvement is possible. Values in the 40-50 percent range are also common but reveal that substantial improvements can and should be made.

Generally, for most employees at an operational level, OEE is an abstract metric. Therefore, it is usually presented as its basic components, for which specific, relatable objectives have been set.

For manufacturers and processors interested in tracking and improving production line results, Worximity Technology, Inc. offers a complete suite of IIoT performance monitoring systems. Worximity’s proprietary, Wi-Fi enabled TileConnect sensors collect data in real-time and send it to a cloud infrastructure for processing. Then, using Worximity’s Smart Factory analytics, performance metrics, KPIs, and OEE values are calculated.

Finally, results are presented in real time on TileBoards (visual display dashboards), which are available to all employees and located throughout the factory floor. The dashboard is configurable, and the system is designed to allow presentation on tablets, phones, or other devices based on customer preferences.

TileConnect works with either new or existing equipment and can be set up in as little as a day and at minimum cost. Monitoring system output, including OEE, helps identify problems such as excessive downtime or outof-spec production. Using Worximity’s downtime tracking tool or giveaway monitoring tool, specific areas that need attention can be identified and performance improvements developed.

TileConnect sensors are fastened to equipment, where they gather performance data and send it to the cloud in real time. Then, Worximity’s Smart Factory analytics software calculates OEE and other important KPIs, which are displayed on TileBoards located throughout the factory. Employees can track OEE values, identify areas needing improvement, and develop needed changes. Tracking progress with OEE and its component scores is an excellent way to gauge the impact of implemented changes and react accordingly.

TileBoard

 

Chapter 5

Data Acquisition Methods Then and Now

Managers today need to access critical data at the right time. They also recognize that most data acquisition methods are expensive and complex and do not present critical information quickly.

Often, cost considerations are a compromise over process critical, real-time data, resulting in managers using post-process reporting tools. This gives managers information on postmortems only, but it does not allow teams to act cohesively or proactively.

For many manufacturing operations, the lack of simple and readily available technology for calculating OEE means that data is collected using decentralized, archaic methods, often involving people. Information must be collected and centralized, then passed to analysts who calculate various metrics (including OEE) to present to management. Although such a data monitoring process encourages a KPI-driven culture within a company, it does not allow managers to make timely and effective decisions to reduce waste and immediately react to problems.

IIoT tools were recently developed to address industry needs. These technologies are designed to automate data collection, improve accuracy, and be available at several levels of the business.

Using an automated data collection system to track process performance data and calculate productivity eliminates two essential problems:

  1. Errors in the data due to human mistakes
  2. Delays caused by data processing and KPI calculation

Chapter 6

Competitive Pressures That Motivate Manufacturers to Adopt New Data Acquisition Technologies

Current trends in the manufacturing sector indicate a relatively fast technology adoption rate, with major players having already invested 12- 18 months of IIoT involvement.

These competitors recognize that these technologies are both easy to install and cost-effective and are consistently deriving benefits from IIoT and Industry 4.0 productivity methods. Manufacturers reluctant to adopt newer data monitoring technologies and retain legacy systems, which are costly to maintain, risk two significant threats:

  • First, slow and archaic data processing inherent in older systems will continue to present supervisors with late and potentially erroneous data.
  • Second, non-scalable data monitoring technologies do not allow any flexibility for process or equipment changes required by the marketplace.

All the while, competitors have invested in automatic data collection systems, quickly improving their operations and positioning themselves to take advantage of reduced costs, increased capacity, and reduced waste.

"Current trends indicate a relatively fasttechnology adoption rate, with majorplayers having already invested 12-18months of IIoT involvement."

Chapter 7

Using Technology to Track OEE and Increase Productivity

Operations management requires key people to make quick decisions in order to adjust or maintain optimal process control. This is a challenging task for managers at several levels of the business and requires constant exchange of process data.

Once a data system is deployed and adopted, whether it is archaic, outdated, or simply not accurate, it is not easily discarded. It is common to see managers postpone taking action, whether to implement new systems or to change existing ones.

Nevertheless, technology developments in IIoT, machine learning, and lean manufacturing methodology have created opportunities for manufacturers to enhance process performance by adopting real-time data collection and sophisticated analytics. Using advanced machine monitoring devices and Smart Factory analytics, organizations today can increase profitability while counting on rapid deployment and cost-effective technology solutions.

Worximity’s best-in-class suite of Smart Factory analytics is a perfect approach for companies eager to increase their production line performance and improve their bottom line. Worximity’s productivity solutions represent a significant step forward for any company wanting to monitor their OEE and seeking to reap better results from process assets.

Using TileConnect, Worximity’s advanced, proprietary smart sensors, machine monitoring can be installed quickly—sometimes in less than a day. These sophisticated sensors are attached to equipment and continuously transmit data wirelessly to the cloud. Worximity’s Smart Factory analytics software then calculates OEE values and other performance metrics, which are displayed on TileBoards located throughout the factory.

Operating data is gathered in real-time. Data monitoring is continuous and exact, representing an accurate picture of current production status. The Smart Factory analytics system is both configurable and flexible. Results can be displayed on smartphones, tablets, or laptops, so every employee is aware of production status at every moment. Alerts can be set to notify key employees of critical changes to KPIs that need prompt attention. Employees can watch real-time changes in OEE and other metrics to immediately identify developing problems. Action can then be taken to correct any problem before defects or other issues accumulate.

Worximity’s system is both easy to onboard and scalable. For example, causes for downtime, an essential factor in improving productivity, can be gathered, along with the amount of downtime and its impact on production. Historical data is logged and can be compared to current operations. An example screenshot of a TileBoard display is shown in Figure 1. Quick response to problems is key to keeping production on track and improving throughput and margins.

"Operations management requires key people to make quick decisions in order to adjust or maintain optimal process control."

Downtime

 

Chapter 8

How to Implement an Automated Data Collection System

Calculating OEE and other useful KPIs should initially focus on developing measures across the factory’s primary process “constraint” or “bottleneck.” Initial analysis should identify critical elements on the processing line that are limiting performance. 

Implementing changes based on accurate and real-time OEE data in the early stages of implementation can have a substantial positive impact on performance.

If you are interested in seeing how Worximity’s OEE KPI solution can help your team improve operations, a quick demonstration can be arranged at a minimal cost. We also offer a no-contract, limited-time trial of our Smart Factory analytics systems, which can be quickly deployed at a minimal cost so you can test your team’s technology adoption capacity, among other things. Case studies of successful applications of Smart Factory analytics in a variety of food processing situations can be found here.

Worximity’s specialists can help answer your questions, analyze your operations, and provide advice and direction for system implementation. Our consultants are available to assist with planning, installation, and training and are always available for ongoing consultations.

Following experiences from hundreds of deployments, we have identified opportunities for improvements that can be seized on almost immediately following system deployment. Worximity’s Smart Factory adoption rate is high because of its simplicity and flexibility and because it can demonstrate a return on investment in the first 3-6 weeks. Talk with a Worximity technical specialist to see how Worximity’s Smart Factory system can be used to help improve your operations.

Worximity’s Smart Factory analytics is designed to have an immediate impact on operational performance and aims to be the foundational tool for a continuous improvement strategy. The typical deployment steps are:

  1. An analysis of your manufacturing processes before the implementation can sometimes uncover easily captured or “quick-win” opportunities for improvement.
  2. Patented IIoT smart sensor devices automate data collection accurately at any process step, allowing a complete process monitoring deployment solution or a laser-focused monitoring strategy at a specific process step (e.g., a bottleneck), depending on priorities identified in Step 1.
  3. Depending on Step 2 deployment options, cloud-based data is displayed in real time, providing OEE KPIs, production rates, and downtimes. This allows teams at all levels of the business to react quickly using accurate, real-time production data.

Chapter 9

Challenges to Implementation

When planning the deployment of an Industry 4.0 production monitoring solution, one of the main challenges is to create continuous improvement awareness across the entire organization. 

When employees understand the approach and benefits of performance improvement initiatives, they will more readily accept change. An internal culture transformation is frequently required. This level of organizational change, however, is often a slow process.

To quickly achieve results and capture benefits from investments in technology, employees should be presented with the short- and long-term value these investments represent. Also, it is important to demonstrate that the onboarding process is easy and frictionless. A whole-company commitment to target performance levels should be demonstrated and fully communicated to staff and operators.

The first half of 2020 saw unprecedented shifts in consumer demands caused by the unexpected emergence of a worldwide pandemic. Whether these changes are long- or short-term remains to be seen.

For manufacturers producing products with commodity-like characteristics, such as food and beverage processors, survival in this environment requires on-the-line improvements in manufacturing efficiencies. Paper-thin margins mean there is little room for price adjustments. As a result, profitability depends on gains in production efficiencies. This means operating strategies must focus on reducing costs to produce and deliver products.

Worximity’s Smart Factory analytics software is designed to provide the right kind of data to support a strategy of lowering manufacturing costs, improving product quality, and reducing time to market. First, we benchmark your operations to assess how you stack up against others in your industry and to gauge improvement potential. Then, beginning the implementation at a factory bottleneck, our team installs Worximity’s real-time data collection and cloud-based analytics. The results are the metrics and data your employees need to drive performance improvement.

Worximity’s Industry 4.0 solution is easy to deploy. Implementations often take less than a day, and positive results are almost immediate. The purchase, activation, and setup processes are frictionless and 100 percent remote. Our consultants possess deep implementation understanding and have accumulated experience from hundreds of successful deployments. They can actively lead training and informational meetings, demonstrate the benefits of the Smart Factory approach, and gradually involve critical employees and team members. Our team will then work with your company to set objectives and plan the improvements, and we will follow up continuously to ensure results are achieved.

An initial production line bottleneck is a good starting point, but total system implementation doesn’t end there. Building a comprehensive data analysis structure involves incrementally adding additional data points along your processing lines. This incremental approach has proven to be an effective and successful method for attaining long-term benefits. Working hand in hand, we aim to put your company on a path to industry “best-in-class” status. Still, some companies think that the process of implementing Industry 4.0 technology will be complicated and costly. Worximity’s simple and flexible TileBoard solution can be deployed gradually and only scaled up as results are proven. The typical return on investment is 6:1 or higher, and our team can help you achieve such an ROI in 3-6 weeks.

"To quickly achieve results and capture benefits from investments in technology, employees should be presented with the short- and long-term value these investments represent."

In today’s uncertain environment, food and beverage manufacturers are looking to Worximity’s Smart Factory analytics for fast results and excellent ROI. You can count on Worximity to provide:

  • Easy deployment and life cycle
  • Rapid implementation that is 100 percent remote and frictionless including purchase, activation (one day), installation, and setup
  • Factory benchmarking against competitors
  • “Best-in-class” goal-setting within industry groups
  • Typical returns on investment of 6:1 within 3-6 weeks

Chapter 10

Benefits and Next Steps

At Worximity, building win-win projects with our clients is a critical element of our success.

The team at Worximity is dedicated to seeing that your implementation is planned, executed, and completed successfully and that your staff is trained and ready to make the necessary changes. We are dedicated to helping your company achieve maximum performance, increase profitability, and create a culture of awareness and continuous improvement. We believe we can help you achieve ever-greater success, as we have with many other companies.

Managers still using archaic data collection and analysis tools should contact a Worximity representative today or schedule a demo of Worximity’s OEE toolkit. What’s the next step in your performance improvement strategy? Let’s get on it already!

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