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Industrial Big Data, How to Calculate ROI for Downtime with TDC
Just as True Downtime Cost ® (TDC) justifies Lean projects, it also works for downtime big data projects.
- Downtime
- Time during which equipment or system is unavailable for use. ⇓ MORE ⇓
- DOWNTIME
LEAN acronym for 8 forms of waste.
Defects, Overproduction, Waiting, Not utilizing talent, Travel, Inventory, Motion, Excess processing- Down Time
- Time off work to relax.
Downtime Definition
This article introduces two elements that must be added to most big data initiatives to ensure end-users can realize their Return On Investment (ROI) goal. Gain insight on how to calculate ROI on industrial big data collection using an accurate downtime costing system, TDC.
We'll get to the two missing big data elements in a bit. But first, it may help to give you a little historical context for better understanding.
In 1995, we founded and introduced the True Downtime Cost ® (TDC) to the industry. We ran into the main barrier, realized ROI, or should I say, perceived ROI. Back then, not many machines and systems were networked together. Implementing it on every machine/system and getting the data to a single point for consolidation and True Downtime Cost analysis was expensive. Nowadays, the cost is much lower. Of course, a compromised solution was arrived at.
Critical machines and systems, like bottlenecks, would have an HMI or other display device to indicate to operators and managers the current OEE and TDC values. This communication was to effect action and change to improve those values. Thus, not only showing an acceptable ROI in real-time but also displaying dollar amounts on the screen, as money has consistently been a great motivator. Management kept the approach simple and relevant to its end users. Later, I share an excerpt from the "action plan" chapter of "True Downtime Cost - 2nd edition" as an example.
About a decade later, the industrial sector started seeing the networking of machine PLCs and systems more commonplace. Of course, Fortune 500 companies and other world-class organizations led the way. TDC focus and application popularity steadily grew in the industrial sector as a cost justification tool to better reflect the true ROI. TDC is especially popular in improving focus, effectiveness, and cost-justifying Lean Six Sigma initiatives.
However, downtime big data initiatives still needed to become mainstream due to a second barrier: data overload. End users and vendors alike believed adding code to consolidate and analyze the TDC data for strategy analytics was too cumbersome. Even those who collected the big data did not perceive the true ROI, failing to analyze and act on the data collected. CMMS was an excellent example of that back then; they mostly used maintenance management systems as just a work order management system.
Fast forward to today …
Today, you see millions in marketing campaigns to promote things like big data, the 'cloud' (store your sensitive data and apps offsite via the internet.), and the "IIoT" (Again, send your sensitive data over the internet.) Ironically, the marketing industry has realized the ROI of big data initiatives and other sectors like IT.
IT departments even took it further to justify their Big Data ROI with a knock-off of TDC cost of downtime, referred to often as 'Real Downtime Cost". But the industrial sectors still lag behind them, as is typical. No offense to the industrial sectors, as the government lags way further behind them. Plus, with potential damage to man or machine, the industrial sector has a lot more risk involved and should advance cautiously.
2 Barriers to Industrial Big Data:
- Realizing the ROI.
- Acting on the big data collected.
The most significant data issue today is not acting on the data collected. The same problem existed at the beginning of this long story. The paradox is that industrial companies and vendors must act on the data to realize the total ROI. Yet they don't act on the data or even start the big data initiatives because they have yet to perceive the total value of doing so. Just like with the Lean initiative, the glue that binds the two is TDC.
Initially, without accounting for True Downtime Cost®, they might perceive a 10-30% ROI. If they applied TDC cost of downtime metrics, they may learn it is 300% or more! As said previously, money is the motivator. When the decision-makers see the actual value, they are more likely to act on the data. That action increases their ROI even more, flipping the paradox into an ever-growing constant improvement driver.
Side Note: There are similar scenarios and barriers on the macro level. Take CMMS software, for example. Today CMMS software has excellent data analysis and reporting capabilities. But those CMMS that do not apply TDC cost of downtime metrics within them or find many of their end users using those features and acting on them. Thus, not realizing the total ROI, end-users settle on using CMMS as record-keeping software only, or worse, never using it. Without TDC, it is missing the motivator ... $$.
Big Data Action Plan:
Below is an excerpt from chapter 10, "Action Plan" of "True Downtime Cost - 2nd edition". It will give you some idea of how to start from the TDC perspective. You can get the book at the link above to learn the True Downtime Costing system TDC, how to define the cost of downtime for better business continuity, and much more.
The True Downtime Cost Action Plan:
How to calculate ROI for employees.
Overview of the action plan:
When introducing TDC into an organization, it is necessary to get senior management and shop floor buy-in. Ensuring support from both requires careful planning to communicate the information each group needs to support the TDC initiative. A well-structured plan and timeline showing the sequential steps involved in the project, the necessary resources, and costs need to be developed and explained to all parties.
Steps along the way:
Top down:
For change to succeed, you must start from the top down. Start by asking your plant manager or corporate manager to review a copy of this book. Let the manager know you would like to assemble a team to create an action plan best suited for your facility's situation. Also, mention you would like the manager's input and welcome their involvement.
Select a team:
If you are an individual company, select a team size that suits your situation. It is well known in world class-establishments you want at least a machine operator, a maintenance person, and someone from management.
If you are a corporation implementing this action plan, you will need "a reasonable sample" of employees from the companies under you. It would help if you started your program and team-building with one of your companies as a test.
Before taking it corporation-wide, have a corporate team analyze the process of implementing TDC. Use the results to refine the standard operating procedure. You would then implement the new action plan on all companies within your corporation.
Set a goal:
The team's first assignment is to set the right goal for your company. Our advice is, "It is better to have a goal that is too high than one that is too low." Remember to include solid financial methods of measuring success as one of your goals.
The fact that you are implementing a methodology that enables you to monitor monetary value closely sets you up for success. For example, if your goal was to review your two most costly lines and automate True Downtime Cost for them. You should see the displayed total cost of downtime drop, and the equipment will be down for less time. You would see a TDC display indicating a real saving to report. With goals in place, you can develop a roadmap to profit from TDC knowledge.
Develop a plan:
Next, you need to create a plan to reach your goal. As with any solid plan, it needs a timeline and allowance for contingencies. Also, remember to 'plan to plan,' set time aside to analyze progress, address issues, and review the plan accordingly. Whatever level you choose, your project will be in two parts. The first is the TDC implementation plan, and the second is the TDC utilization plan. Both are equally important to your overall success.
Below is a recommended plan template for minimum implementation and utilization. It includes a worked example plan. You can always start small and let success drive further performance throughout your facility.
- Goal
- Area of implementation
- Method of implementation
- Time to be completed
- Define technical steps and targets.
- Evaluate the area of implementation defined in the goal.
- Determine steps
- Place steps in the timeline template
- Apply methods of implementation following the timeline.
- Determine roles and resources.
- Request what needs to be done for the different groups, departments, or individuals.
- At the same time, brainstorm on methods for your organization to benefit from the TDC knowledge gained by reaching your implementation plan.
- Once the TDC implementation plan has succeeded, record the benchmark baseline of TDC and move on to the TDC utilization plan below.
- Implement your reaction plan based on the new TDC tools available.
- The plan may be as simple as managers making better decisions.
- Every week or month, survey those involved and report to assess the current monetary value earned/saved by utilizing the new TDC knowledge.
- After 3, 6, or 12 months, interview all of the above to summarize and report on how much money the TDC knowledge has saved your company.
- Make this known to all from the top down.
After the above, in chapter 10 of the book, it is followed by a detailed example plan. Then, the following areas of chapter 10.
Implementing Automated Data Collection
Motivating Management
Motivating Employees
Utilizing TDC in Daily Decisions
Then, it's on to chapter 11. You can read all the chapters and sections by downloading the book at True Downtime Cost - 2nd edition.
Understanding Industrial Big Data: How It Differs from Others
In today's digital age, "big data" is often used to describe vast information. However, big data takes on a unique significance for industrial applications. Below, we will explore how industrial big data differs from other types and why it is crucial in shaping the future.
What is Industrial Big Data?
Industrial big data refers to collecting, analyzing, and utilizing vast amounts of information generated within industrial settings. Unlike other forms of big data, which may focus on consumer behavior or website analytics, industrial data centers around machinery, equipment, and processes. It encompasses real-time monitoring systems, sensor data, machine logs, maintenance records, and production metrics to optimize operational efficiency and drive informed decision-making.
Volume and Velocity:
Industrial data generates a sheer volume and velocity that sets it apart from data sources. Many sensors and devices continuously collect data at high speeds in places like factories or power plants. This enormous influx of information allows companies to monitor performance metrics in real time, enabling quicker detection of issues or bottlenecks. The ability to process and analyze this massive volume of data promptly gives industrial companies an edge in enhancing productivity and ensuring seamless operations.
Variety and Complexity:
Another distinguishing factor of industrial data lies in its variety and complexity. Unlike traditional forms of consumer-oriented big data that predominantly deal with structured information like customer preferences or purchase history, industrial data incorporates a wide range of data types. Industrial data includes unstructured or semi-structured data from various sources, such as machine logs, IIoT, machine sensors, PLC controllers, HMI, SCADA, maintenance reports, and more. The complexity arises from the need to integrate and analyze this diverse array of data to gain valuable insights that drive operational improvements.
Value and Impact:
Industrial big data holds immense value and profoundly impacts business operations. By harnessing this data, companies can identify patterns, trends, and anomalies previously challenging to detect. Predictive analytics are employed to anticipate equipment failures, reduce downtime, and optimize maintenance schedules. Furthermore, industrial data analysis can enable better resource allocation, cost of downtime reduction, and improved quality control. Ultimately, the insights gained from industrial big data have the potential to transform industries by enhancing efficiency, productivity, and overall profitability.
The future of industrial big data:
The future of industrial big data is an exciting realm where the power of data meets the real world. Mining data from the industrial process and harnessing sensor information can unlock many insights and opportunities. Big data links intelligent manufacturing and the Industrial Internet of Things (IIoT).
Big data serves as the crucial link between these two transformative technologies. It enables us to collect, analyze, and interpret large amounts of industrial process-generated data. Using this valuable information to calculate TDC, we can optimize operations, enhance efficiency, and drive innovation in manufacturing.
By leveraging industrial data, businesses can better understand their processes, identify patterns and anomalies, and make informed decisions in real-time. From predictive maintenance to quality control, the applications are endless.
The Bottom Line:
Incorporate True Downtime Cost® metrics into your big data, analyze big data and use strategy analytics, and most important ... ACT on insight gained.
Related Learning Path:
We recommend ...
1st HMI Basics
2nd PLC Troubleshooting & SCADA
3rd OPC SCADA
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