Your address will show here +12 34 56 78
Dashboards & Displays, Data Visualization, Process Manufacturing, Troubleshooting & Analysis

In this article we will explore common types of process engineering software and highlight some powerful software applications that every process engineer should know.

Process Engineers! Solve challenging process & product quality issues with these process data visualization tools.

Learn more

Types of Process Engineering Software

Process Engineers use software every day for a range of reasons.

There is software that allows users to visualize data, perform calculations, and conduct data analysis to help with process optimization and root case analysis.

Software can help mitigate risk with simulations and modeling. For instance, engineers can test how a process may react to changes without introducing hazards or wasting material.

Today and for the foreseeable future, process engineering software allows us to communicate problems, ideas, and solutions.

Graphical Dashboards

Engineers often create graphics dashboards to help display real-time data in a more consumable way.

Graphical dashboard tools are often combined with trend visualization software such as PARCView.

When creating a dashboard, it is important to know the audience and information to be conveyed. Will the dashboard be a process diagram, displaying a single view of how the process is running? Or a quality page, showing recent lab data and specifications.

Flashing values or pop ups can be used in displays to alert operations when a parameter goes out of specification. When adding alerts or colors, be aware of colorblindness and how that can modify interpretation of the display.

Reporting Software

Reports can range from detailed documents to concise information with embedded charts and images. The type of report will dictate which software to use.

There are multiple reporting tools available. Some, like dataPARC’s production monitoring software use simple coding to pull in data and automate the report while others may require more manual effort.

Microsoft’s Excel is a foundation in data reporting. It is a powerful tool and there are many internet resources that can help users at any level build reports in Excel with VBA.

SSRS (SQL Server Reporting Services) is another standard reporting tool for those who have data in SQL. SSRS can be used to create reports and send them on a schedule.

Microsoft Word is often used to create reports. It is best used for reports where automation and accessibility to data is not needed.

Centerlining Software

Operational envelopes, or Centerlining, is used to make sure process parameters are set up consistently from run to run. This helps to produce good quality product more quickly.

Engineers will build and review operational envelopes as guides. These ranges need to be set up for every product produced, which can be time consuming and inaccurate.

With PARCview’s unique Centerline tool, an engineer does not have to analyze and create the operational ranges. By adding variables to the centerline tool, it can identify if any variables are running outside of normal operational ranges.

dataPARC’s Centerline

LIMS Software

Process engineers can manage and use LIMS (Laboratory Information Management Systems). LIMS provide a way to record, apply limits, and organize lab data and processes.

LIMS can be independent systems such as LabWare or SAP . Others can be integrated with data visualization and process software, allowing users to view lab and operations data and lab data in the same program.

LIMS are important to process engineers because they track testing and quality information which can be key when working with customers or providing a look into plant operations.

ERP Software

ERP software is used for product tracking and scheduling. It can be used to allocate items to projects for billing purposes or for tracking project hours.

ERP software helps keep the process organized and allow engineers and operations to know what is coming up on the production schedule.

SAP and Oracle are two common ERP applications. They help keep everyone on the same page with a single source of truth.

Looking for new process engineering software? check out dataPARC’s process engineering toolkit. Tackle root cause analysis, process monitoring, predictive modeling, & more.

Root Cause Analysis Software

Software to assist in root cause analysis can help reduce downtime. When a process goes down the cause may not be known right away.

SOLOGIC is a program dedicated to root case analysis, it has tools included cause and effect diagrams, fishbone, and incident timelines.

By looking at process trends and centerlines from data visualization software, engineers can identify if any variables were in upset conditions prior to the down.

By recording downtime root causes an engineer could look at that information in a pareto chart. This will identify the most common or most time-consuming reasons for lost time. With this information the engineer can go after minimizing occurrence or duration of a specific cause.

Pareto charts can help identify common lost-time reasons

Data visualization software with built in downtime tracking capabilities can help expedite root cause analysis.

Check out our real-time process analytics tools & see how you can reduce downtime & product loss.

Check out PARCview

Process Optimization Software

A key role for many process engineers is process optimization. This can include in depth Six Sigma projects requiring large amounts of data, testing and implementation of solutions.

Throughout such projects statistical software, such as Minitab or JMP, is used.

minitab

Process optimization software can help identify variables that are statistically significant to the process, pointing to the parameters that will have the greatest impact.

Software is utilized to help come to conclusions, however subject matter experts can identify which statistically significant data is also practically significant and worth pursuing.

Quality Management Software

Process engineers work with quality to ensure products are made to the production specification and that customers are receiving a consistent product, order after order.

Histograms of variables with the 3-Sigma value and Cpk can be used to help create or review specification and control limits.

Many data visualization applications can produce histograms and simple charts to help with quality data analysis. Excel can also be used to create such charts.

Process Calculation Software

Process engineers are often responsible for calculating Overall Equipment Effectiveness (OEE), producing process metrics and or other process calculations.

Software will reduce errors in these calculations and produce results quickly.

In the case of process calculations, there are times when making a change to process requires calculating different volumes and ratios of material to prevent a reaction. Utilizing a software over manual calculations can prevent errors that may result in downtime or safety incidents.

Depending on if the value is used for a one-time purpose or needs to be regularly calculated can narrow down which software should be used to create the calculation.

Excel can be used; however, data would need to be pulled into the program. Integrated data visualization software can perform calculations and historize those values, so they are always ready.

Simulation & Modeling Software

Simulation, modeling, and sizing software is used when designing a new process or making changes to and existing process.

AutoPIPE is used for pipe stress analysis, Pipe-Flo can perform fluid flow calculations and AFT Arrow is a gas flow simulation that can be used for insulation sizing. AutoCAD is used for a range of 3-D modeling including drafting and design.

Such model and simulation programs can allow engineers to test and see how the process may perform before it is built – mitigating risk and saving material.

Other Useful Software

When asked what process engineering software they use daily, each engineer we spoke to had had different programs for the areas of software outlined in the previous section. There were some that came up repeatedly.

Whether you are starting your career as a Process Engineer or have many years under your belt, utilizing some of this software can promote career development and help streamline daily tasks.

Microsoft Office

Microsoft has several programs that are used on a regular basis, even if your company doesn’t have these specific ones, there is something like it.

https://www.microsoft.com/en-us/

Looking for new process engineering software? check out dataPARC’s process engineering toolkit. Tackle root cause analysis, process monitoring, predictive modeling, & more.

Excel

As mentioned in many of the sections above, Excel can be used for a variety of reasons and is a pivotal tool to have in the toolbox. Excel can be used to generate reports, create charts and graphs, complete calculations and analyze data.

Word

Word is a simple tool for creating reports, Standard Operating Procedures (SOPs) and other types of documentation.

OneNote

OneNote is a newer product that can be used as a digital notebook. It helps organize notes, and one could even build their own quick reference guide. Similarly, to other Microsoft products, OneNote notebooks or sheets can be shared with multiple people through OneDrive.

PowerPoint

There are many options for presentation generators and PowerPoint is still used to lead presentations and meetings. PowerPoints are a simple way to present information.

Teams

Teams is not the only online meeting and communication tool, but it is a very common one. Teams is used for inner company chat, virtual meetings, and document sharing. Within teams you can create groups (teams) that is connected to SharePoint to share documents, chat, post. Emails can even be sent directly to a group so other people can comment on it as a thread.

Outlook

Outlook is the primary hub for emails and meeting scheduler. Internally, you can view others calendars to find when free time to schedule a meeting without emailing the back and forth.

Project

Project is used to build Gantt Charts for project management. Often used for scheduled downtime and other large projects. These can be very detailed and helpful to stay organized. There is a bit of a learning curve.

Snagit / Snip It

Screenshots and images are helpful to include in emails, reports, SOPs to enhance communication.

The original screenshot was using the print screen button on the keyboard. Now, with multiple monitors this is unrealistic because the desired image needs to be cropped to a certain size.

Snip it is standard on Windows computer, but it has limited editing options, and does not auto save the images. It does allow to take a screenshot of a certain area, rather than the entire screen.

A step up from Snip it is Snagit, it is a paid software, but it allows multiple screenshots, saving them to a library to come back to later. The editing capabilities are far superior to Snip it, allowing annotation, arrows, blurring, etc.

https://www.techsmith.com/screen-capture.html

These are just a few examples of the countless screen capture software out there. In short, it is an essential tool for all business types.

Notepad++

Viewing and editing code without having to run it in production is valuable. Notepad++ is a versatile program allows users to see code in its programing language.

It has add-ins and one can compare code sets against one another, it will highlight what the difference which can help find bugs, typos, etc.

Notepad++ is free. It keeps tabs saved so you can close and re-open without having to save the files to a specific location.

https://notepad-plus-plus.org/

digital transformation guide

Want to Learn More?

Download our Digital Transformation Roadmap and learn what steps you can take to achieve data-driven success in manufacturing.

Download PDF
0

Dashboards & Displays, Data Visualization, Process Manufacturing, Troubleshooting & Analysis

Reducing downtime increases productivity, lowers costs, and decreases accidents. Downtime tracking software can be utilized to help reduce downtime. Knowing why the process is going down is key to reducing it.

Monitor, report, & analyze production loss from unplanned downtime, poor quality, and performance issues.

Learn more

What is Downtime?

Downtime is any duration in which a process is not running. However, not all downtime is created equally. There are two types of downtime, planned and unplanned.

Downtime events represented visually in dataPARC’s process trending software.

What is Planned Downtime?

Planned downtime is when production schedules a time to take the process down. Planned downtime is a necessity to maintain machinery by conducting inspections, cleaning, and replacing parts.

Planned downtime allows operations to organize, schedule and prepare for the downtime. They can coordinate with contractors, order parts and plan tasks to complete while the process is down. Planned downs can be organized so personnel have tasks to accomplish and the necessary tools on hand.

What is Unplanned Downtime?

Unplanned downtime is when the machine or process is down for any unscheduled event. This can be due to a part break, lack of material, power outage, etc. Unplanned downtime is unpredictable and should be targeted when aiming to reduce overall downtime.

Importance of Reducing Unplanned Downtime

Unplanned downtime is significantly more costly and dangerous than planned downtime. Since unplanned downtime is unpredictable and the process could go down for numerous reasons it is impossible to be prepared for every situation.

Waiting on parts or the necessary personnel to fix an issue takes time and could mean the machine is going to stay down for longer. Longer downtime is less time making product, directly effecting the bottom line.

Another cost attributed to unplanned downs is the unsellable product made and wasted material. The time right before, at the time of the down and start up of the process tends to lead to off quality product.

Unplanned downtime can attribute to near-misses or accidents. During unplanned downtime the goal is to get the machine/process up and running again as soon as possible. This pressure can create a stressful, chaotic environment resulting in people reacting rather than stopping to think about the best plan forward.

Reducing unplanned downtime can help lower overall operating costs. It also reduces the times when employees are put in unpredictable situations, decreasing the likelihood of an accident occurring.

How to Reduce Downtime

There are numerous reasons for process downtime and multiple approaches may need to be implemented in the effort to reduce it.

1. Track Downtime

Before jumping into the steps of reducing downtime, it is critical to track it. Tracking downtime lets you see why the process is going down and provides a metric on if it is being improved.

The data collected in tracking downtime will be used to help reduce it. Consider collecting the following data for each down occurrence:

  • Duration
  • Reason/Cause
  • Product at time of down
  • Process Area
  • Shift or Crew
  • Operator Comments
  • Other attributes such as environmental occurrences due to downtime, waste collected over the duration, safety concerns, etc.

This data can be collected manually, however having an automated system will ensure the data is collected for each event. More consistent data will help reduce the downtime.

Downtime tracking software can automate and help organize the downtime. Some considerations when researching downtime tracking software:

  • Ease of use
  • Automatically captures downtime events
  • Records downtime cause and other data
  • Analyze data and events
  • Integrate with process data

There are many options for downtime tracking software on the market. Some are dedicated downtime tracking applications, while others, like dataPARC’s PARCview may offer a suite of manufacturing analytics tools that include a downtime tracking module. The right choice is the one that will be used consistently.

Looking to reduce downtime? dataPARC’s real-time production monitoring software uses smart alarms to automatically alert operators & maintenance crews to u.

2. Monitor Production

Having a system to monitor production can also help reduce downtime.

Visible process trends at operator stations give a visual of how the process is running over time and if variables are migrating or staying consistent.

Real-time production dashboards can be used to display quality data, relaying information directly from the lab to operations. This ensures product is continuously on quality.

Alarms can be used independently or in conjunction with trends and dashboards to warn operators when upset conditions are occurring. This can allow them to react more quickly, potentially preventing a down from happening.

3. Create a Preventative Maintenance Schedule

Preventative maintenance happens during planned downtime or while the process is running. Part replacement during planned downtime allows the site to order the necessary parts and make sure the proper personnel are on site to perform the tasks, saving time and money.

Regular maintenance when the process is running, such as adding or changing lubricating oils, and cleaning can help increase the lifetime of the parts.

Once a scheduled is created it can be tracked to ensure tasks are being accomplished. MDE (PARCview’s Manual Data Entry) can be configured on a time schedule and integrated with alerts. If a task is skipped, a reminder message can be sent to the operator or escalated to a supervisor.

Maintenance data can be captured and digitized to help predict downtime events for the development of preventative maintenance schedules.

Recording preventative maintenance data allows sites to analyze it alongside downtime and process data. Correlations can appear and help drive necessary maintenance and reduce downtime.

4. Provide Operator Decision Support

Unplanned down events are inevitable and cannot be eliminated completely, so a priority of reducing downtime should also be reducing the duration when a downtime event occurs.

Creating tools and troubleshooting guides for operators to use in the event of a down will help get the process back up more quickly.

To get the process running, operators need to know why it went down in the first place. Providing operators with the necessary resources to find the root cause is key to resolving the issue quickly.

Process dashboards, trends 5-Why analysis, and workflows can help determine the root cause.

Trends, dashboards, and centerlines can draw attention to significate changes in the process. dataPARC’s Centerline display is a tabular report with run-based statistics. This format helps ensure the process is consistent and can point to variables running outside of past operating conditions or limits.

Centerlines proved early fault detection and process deviation warnings, so operators can respond quickly to reduce unplanned downtime events.

A workflow or preconfigured 5-Why analysis can also help point the root cause and suggested solution.

Check out our real-time process analytics tools & see how you can reduce downtime & product loss.

Check out PARCview

5. Perform DMAIC Analysis

The above suggestions are starting points to reduce downtime. If those are in place, the DMAIC process (Define, Measure, Analyze, Improve, Control) can be used. It is a fundamental LEAN manufacturing tool and can be used to help reduce downtime.

Define

First, the process, when the process is considered down, and a list of potential reasons need to be defined.

For each process, determine how it is identified as running or not running.

For many downtime tracking software’s a tag/variable is needed to indicate when the process is considered down. If a specific tag does not exist consider a utility feeding the process such as steam, water, or pressure. As long as there is a clear value that would indicate the process is running or not that variable can be used.

Brainstorming a list of potential downtime reasons is also needed prior to tracking the events. This reason list/tree can be shared or unique for each process area.

Assigning reasons to downtime events provides data that can be used to reduce downtime in the future.

These reasons need to include both planned and unplanned causes. During the analyze phase the planned reasons can be filtered out to focus on the unplanned downtime. For more information on creating a reason tree see 5 steps to harness your data’s potential.

Measure

Measuring and assigning a reason to the downtime is a critical step in being able to reduce it. Having a robust downtime tracking software will help make measuring the downtime easier. Make sure to capture the who, what, when, where, and why of the downtime event.

Once the downtime tracking software records the downtime event, a reason can be assigned.

Some systems can automatically assign reasons based on an error code from the machine. Users can verify the reason or select it from the predefined reason tree.

Additional information can be helpful to capture for the analyze phase. You may consider allowing users to type in free form comments in addition to the predefined reason to further explain why a downtime event occurred. If using PARCview, the evidence field can be configured to capture other important process data over the duration of the event.

Analyze

Now that the downtime is recorded and categorized it can be analyzed. Pareto charts are useful when analyzing downtime events. Data can be charted on a pareto by duration or count of events.

Pareto charts can help you analyze downtime events and learn the most significant causes of downtime.

Take into consideration other key process data such as safety concerns, environmental risks or material wasted, in addition to duration of events to help determine which downtime cause will be most beneficial to target and reduce.

It is not always the reason with the most total minutes down that should be the target.

Take for instance, an event that caused 15 hours of downtime but was due to a weak part and is unlikely going to happen again verses a cause that happens monthly but has only results in about 75 minutes of downtime each event. The reoccurring event is going to be more beneficial to improve.

Improve

When looking for ways to reduce a downtime cause look both on how to prevent the event from occurring in the first place, and how to get the process back up when the event does occur. Both approaches are needed to reduce downtime.

Think about the frequency of inspections, cleanings, how long parts last and if they can be put on a schedule to be replaced rather than waiting for them to fail while the process is running. Refer to the preventative maintenance schedule and update as needed.

Determine the best way to reduce the most impactful downtime causes or reduce the effect. A payoff matrix can help point to the most impactful, least costly solutions.

Control

Continue to measure and analyze the downtime to ensure items that have been reduced do not start popping back up. Repeat the cycle and target another reason. Workflows and SOPs (Standard Operating Procedures) can be created to help stay in control.

Conclusion

Reducing unplanned downtime requires multiple approaches, finding the right tools and software for tracking and monitoring is key to reducing downtime. Data is needed to drive improvement both preventing future events from occurring and reducing the duration when the process does go down.

A downtime tracking software can help save, organize, and review downtime events, allowing you to more effectively reduce downtime in your manufacturing process. dataPARC’s PARCview integrates downtime tracking, and process monitoring in one user friendly program.

digital transformation guide

Want to Learn More?

Download our Digital Transformation Roadmap and learn what steps you can take to achieve data-driven success in manufacturing.

Download PDF
0

Dashboards & Displays, Data Visualization, Process Manufacturing, Troubleshooting & Analysis

Go beyond a typical gap analysis with a real-time gap tracking dashboard. Minimize manufacturing gaps such as operational cost or waste by creating gap tacking systems such as dashboards that calculate the gap in real-time rather than at the end of the month. In this article we outline why in line gap tracking is beneficial and walk through the steps to create real-time calculations and dashboards to track manufacturing gaps as they happen, allowing operations to make data-driven decisions.

Implement real-time gap tracking with dataPARC to help you optimize & control your processes

View Process Optimization Solutions

Why Gap Tracking?

Gap Tracking is going a step beyond gap analysis. Gap analysis is the comparison of actual operating conditions against targets, this is typically done monthly and is an important tool in the continuous improvement process. However, gap analysis has some shortcomings. The feedback loop is drawn out — by the time you can collect the data and compare values it can be days or months after the fact. This can prevent actionable solutions and cause lost opportunity.

To resolve these shortcomings operations needs information in real-time correlated to the levers they can pull on the machine. They need the power to make decisions and adjustments based on this information. The real-time information needs to be quick and easy to view and understand. Progress made needs to be measured and visible in real-time. A dashboard can help provide this information.

Dashboards help visualize the live data and updates or changes to a graphic can happen relatively quickly. Operations can utilize a real-time gap tracking dashboard to alert them of what is causing the gap and get the process back on track in the moment, rather than realize the problem days, weeks, or months later.

How to Create a Gap Tracking Dashboard

Follow along as we demonstrate how we built this gap tracking dashboard.

Conduct a Gap Analysis

The first step in creating a Real-Time Gap Tracking dashboard is to complete a Gap Analysis:

  • Define the area of focus and targets
  • Measure the variables
  • Analyze to targets against the current values
  • Improve the process to with Quick Wins to minimize large gaps
  • Control with routine Gap Analysis, this can include creating a Gap Tracking dashboard.

Gap Tracking Requirements

Regardless of the process and gap being tracked, the same general information is needed to build a gap tracking dashboard. Many of the following requirements will be pulled from the Gap Analysis.

Adequate measurements

Similarly to Gap Analysis, adequate measurements are required for gap tracking, however measurements may need to be taken more frequently for a reactive gap tracking calculation to be performed. This can be a challenging step, but the more variables that are able to be measured closer to real-time will provide more accurate gap tracking calculations.

If variables only have 3-4 data points per day, it can be difficult to see how changes affect the gap in real time. It is possible that variables without adequate measurement are removed from the dashboard and more emphasis is taken on those with more datapoints.

Process baselines

Process baselines are a great way to determine targets if they are not already outlined. Overall process targets typically come from upper management or operational plans. It is necessary to break these overall targets into their individual inputs. Those inputs could be broken down even further. Depending on the process, there could be targets for different products.

One way to determine baseline is finding times of good quality and production, what were the operating conditions and how can they be replicated.

Once a list of individual variables is created a target should be assigned. By meeting each target, the overall target should be met. If the individual variables do not have targets, the process baselines can be used instead.

dataPARC’s Centerline display is a smart aggregation tool which can be used to help establish operation baselines

Custom calculations

Another key step in building a gap tracking system is to standardize measurements and units. All variables should be converted to a per unit basis, some common options include dollar per ton, dollar per hour, off quality ton per ton or waste ton per ton.

Once all the input variables are converted to the same unit, they can be combined to create the overall process gap.

Value opportunities

Involving those with a high degree of process knowledge is critical. They will be able to help identify all the process inputs, then narrow the list to variables that can provide the most value opportunities.

These value opportunities are then tied into the gap tracking dashboard as an operator workflow. The workflow will focus on the variables that operators are able to control and has the greatest effect on the gap. This is where the calculated gap gets connect to process levers that the operators can manipulate to get things back on track.

With insight from a process expert one or two variables may stand out as most room for value added opportunities.

These variables should be the focus when it comes to the layout of the graphic. As most read from left to right, top to bottom, the most important information should appear in the upper left of the dashboard (or oriented closest to the operator if the monitor is going to be off to the side or high up). This will help ensure that those variables with the most opportunity to close the gap are being looked at first.

Operator buy-in

Ultimately, operators are the ones who will be using the dashboard to make data driven decisions in real time. Involve those who will end up using the dashboard as part of the design and implementation to help build ownership and operator buy in. Without operator buy in, the dashboard is a waste.

Software to visualize the dashboard and perform the calculations

Find the right visualization tool for your site. A process data visualization tool should be able to view trends, a grid with the ability to change colors or provide alerts and link to other displays or trends for quick data interpolation.

In this example dataPARC’s Graphic Designer was used to build the dashboard.

Depending on the process and number of inputs these calculations can get rather large and take a while to process on the fly, and even longer if looking at data in the past.

dataPARC’s Calc Server allows for calculations to be historized making this a great tool to use for fast calculations and viewing history.

Considerations When Building & Using a Gap Tracking Dashboard

Gap tracking dashboards are going to look different from machine to machine and site to site. Here is a list of suggestions to keep in mind while building your own gap tracking dashboard.

  • Use grids and rolled up data to convey the current gap. Colored or pattered backgrounds can help draw attention.
  • When a gap occurs, what levers can operations pull to make a change? These variables should be a focus on the dashboard. This can be done by adding trends, focused metrics, or a link to another display to “zoom into” that lever and see if a change can be made and how if effects the process.
  • Work with operators to determine those levers and key pieces of information, ask what would be helpful for them to see on this dashboard. The goal is to get all the important information in one place.
  • Monitor the progress, determine if the overall gap is decreasing, if not determine why. Make changes to the dashboard as needed.
  • The dashboard should provide information without settings requirements on how to run as not all the variables are always within the operators control.

Manufacturing Gap Tracking Example

Let’s look at a Gap Tracking dashboard for a paper machine.

1. Summary Trend

The first thing is a large trend showing the overall paper machine gap in dollars per day. The blue line represents the real-time gap and the yellow the target.

2. Category Trend

The second trend in the bottom left shows the individual calculated gaps by category. These are also on a dollars per day basis. This view allows the user to quickly identify it a category is trending in the wrong direction.

3. Gap Tracking Table

The table in the bottom right of the screen shows the current category gaps on a dollar per ton and dollar per hour basis. The values are highlighted red for over expected cost and green for under. At a glance, users can see what the current status of each category is.

4. Chemical Usage

Off to the right is a chemical usage button that will pull up another display. This button was added because chemical usage was found to have multiple inputs and levers for the operator to pull to close the gap.

Watch the video below to see this gap tracking dashboard in action.

Manufacturing analytics software like dataPARC’s PARCview offer tools to help manufacturing companies perform real-time gap tracking post-gap analysis.

Conclusion

A gap tracking dashboard can provide operators with a clearer picture of the gap in real-time, allowing them to make data-driven decisions. The alerts and real-time calculations bring awareness, letting operators know when something isn’t running optimally.

It is a way to drive process savings by tying analytics to actionable changes.

Instead of waiting for the end of the month to find there was a gap, it is found in real time. Bring troubleshooting into the present, changes can be made in real time to reduce or prevent larger process losses.

Although gap tracking dashboards are powerful tools, they do not replace regular Gap Analysis. To drive continuous improvement, Gap Analysis should be done regularly, targets on the gap tracking dashboard should be adjusted to reflect any process changes.

digital transformation guide

Want to Learn More?

Download our Digital Transformation Roadmap and learn what steps you can take to achieve data-driven success in manufacturing.

Download PDF
0

Dashboards & Displays, Data Visualization, Process Manufacturing, Troubleshooting & Analysis

Minimize manufacturing gaps such as operational cost or waste by performing a gap analysis with process data. In this article we will walk through the steps of a basic manufacturing gap analysis and provide an example.

Implement real-time gap tracking with dataPARC to help you optimize & control your processes

View Process Optimization Solutions

What is a Gap Analysis for Manufacturing?

A gap analysis is the process of comparing current operating conditions against a target and determining how to bridge the difference. This is an essential part of continuous improvement and LEAN Manufacturing.

A manufacturing gap analysis can be performed on a variety of metrics, such as:

  • operational costs
  • quality
  • productivity
  • waste
  • etc.

When it comes to bridging the gap, the ideal case is to reach the target with a single and permanent change. This is not always the case, there are times where the gap is going to be fluid and would benefit from constant monitoring and small adjustments. In these cases, operations can utilize a real-time gap tracking dashboard to alert them of what is causing the gap and get the process back on track in the moment, rather than realize the problem days, weeks or months later.

Manufacturing analytics software like dataPARC’s PARCview offer tools to help manufacturing companies perform real-time gap tracking post-gap analysis.

Who Conducts a Manufacturing Gap Analysis?

Gap analysis can be performed by anyone trying to optimize a process. As mentioned above there are a multitude of metrics that can be measured.

A process engineer might want to reduce operational cost by focusing on energy consumption, someone in the finance department may notice an increase in chemical cost every month, and a supervisor may want to reduce the time it takes to complete a task to focus on other items.

Almost every department can leverage gap tracking in one way or another.

How to Perform a Gap Analysis in Manufacturing

Like many other improvement strategies, we can use the DMAIC method (Define, Measure, Analyze, Improve, Control) to perform a gap analysis and implement a live gap tracking dashboard.

To create a gap tracking dashboard, a gap analysis needs to be completed first. In the last stage, Control, the dashboard is created, and operations can perform steps Analyze-Improve-Control in real-time.

1. Define

The first step is to define the area of focus and identify the target. A great place to start when looking for an area of focus is the company’s strategic business plan, operational plan, or yearly operational goals. Many times, these goals will already have targets in place.

2. Measure

Next, the process must be measured. Take a close look at the measurement system. Is the data reliable? Does the measurement system provide the necessary information? If so, measure the current state of the process.

If there is no current measurement system, one will need to be created. Although in-process measurements or calculations are best, manual input can also be used.

Some manufacturing analytics providers, like dataPARC, offer manual data entry tools which allow users to create custom tags for manual input. These tags can be trended and used the process tags in dashboards and displays.

3. Analyze

Take the data and compare it to the goal. How far from the target is the process? This is the gap. It may help to visualize the process gap in multiple ways such as with a histogram or trend display.

This histogram provides the overall distribution of the data which can help narrow the focus. What does the peak look like, is it a normal distribution, skewed to one side, is there a double-peak, or edge peak?

A trend shows how the process is shifting overtime, are there times of zero gap vs large gaps such as shift or season?

With the measurement system in place, the gap realized, and some graphical representations of the data, it is time to brainstorm potential causes of the gap. Brainstorming is not a time to eliminate ideas, get everything written down first. There are a variety of tools that can be used to help in this process:

Fishbone Diagram

This classic tool helps determine root causes by separating the process into categories. The most common categories are People, Process/Procedure, Supplies, Equipment, Measurement and Environment, other categories or any combination can be used to fit the situation.

The fishbone diagram is a classic tool for performing root cause analysis.

The team can brainstorm each category and identify any causes that could play a role in the problem. Dive one step further with a 5-why analysis, a method that simply asks “why” until it cannot be answered any more to ensure the true root cause is uncovered.

Is gap analysis one of your digital transformation goals? Let our Digital Transformation Roadmap guide your way.

get the guide

SWOT Chart

This chart is made of four squares, with labeled sections: Strengths, Weaknesses, Opportunities, and Threats. This strategy is used to determine the internal and external factors that drive the effectiveness of the process. For potential root cases, focus on what appears in weaknesses and see if potential solutions find their way to opportunities.

SWOT charts are another fundamental root cause analysis tool.

McKinsey 7s web.

The McKinsey framework is made up of 7 elements, categorized as 3 “Hard” or controllable elements and 4 “Soft”, non-controllable elements. In each element, write the current and desired state. It is important elements are in alignment with one another, any misalignment could point to a root cause.

The McKinsey Framework.

4. Improve

Determine the best way to bridge the gap and implement the changes. A payoff matrix or efficiency impact trend can help pick the most effective, least costly options. Focus on quick wins. Items in busy work can be completed but are not a priority. Those in Major Projects, you must ask, is the price work the impact? Anything that is low impact and high cost can be dropped.

A payoff matrix or efficiency impact trend can help you determine the best way to bridge gaps.

After the solutions are implemented, check the results by analyzing the data again and see if there was an improvement.

5. Control

Once the target is met it is important to keep it that way. Monthly reports can be used to keep track of the process gap and make sure it stays in the desired range.

Set up a dashboard, or other visual to monitor the process in real time. By tracking the gap of the process in real time, operations can see how changes to the process effect the bottom line in real time, rather than at the end of the month.

On the road to digital transformation? Get our Free Digital Transformation Roadmap, a step-by-step guide to achieving data-driven excellence in manufacturing.

An Example of Gap Analysis for Manufacturing

In this manufacturing example we are going to walk through a gap analysis to improve operational costs on a single paper machine.

Define

The company’s operational plan has a goal for monthly operational cost. To break this down into a manageable gap analysis, the focus will be looking at a single machine. This machine is not currently meeting the monthly operational cost goal on a regular basis.

Measure

Since this is an initiative from an operational plan, there is already a measurement system in place. The machine operational costs are broken down into 5 variables. Speed, Steam, Chemical, Furnish and Basis Weight.

These variables are measured continuously, so data can be pulled in hourly, daily, and/or monthly averages. The variety of data views will help in the next stage. Each of these variables have a target, but some are missing upper and lower control limits.

Analyze

First, the combined daily operational cost was compared against the target. There were days where the target is met, but it is not consistent.

Next, each of the five variables were compared with their targets separately over the past several months. From this view, Chemical and Steam stood out as the two main factors that were driving up the operational cost. With that in mind, we moved onto the Fishbone diagram and 5-why analysis.

Using the fishbone diagram we were able to determine that chemical and steam were the two main factor driving up our costs over the past several months.

Improve

From the fishbone and 5 why, we found that there were targets but no control limits set on all the chemicals. Operators were adding the amount of chemical they felt would accomplish the quality tests without trying to only apply to necessary amount.

Thinking about the cost/effectiveness diagram, it is cost free to add control limits to each chemical additive. Engineers pulled chemical and quality data from multiple months, created a histogram to find the distribution and set up control limits to help the operators have a better gauge of how much chemical to apply and the typical range that is needed to satisfy any quality tests.

For steam, there were a lot of potential root causes around the fiber mix and cook. The mill already has SOPs to deal with situations such as bad cooks. Another root cause that came up during the fishbone was steam leaks. Most leaks can be fixed while the machine is running, so over the next several weeks there was a push to find and close major leaks.

Control

In this case, since limits were created for chemical usage, alarms were also created to alert operations if they exceeded the control limit. Alerts are a great way to notify operations when processes are drifting out of control so quick corrects can be made.

After a few weeks of these changes, another analysis was be completed. The operational costs are meeting target, and it was time to move to the next process. It is important not to forget about the operational cost, this was monitored monthly to ensure it does not exceed the target.

Conclusion

Performing routine gap analysis is an important step in LEAN Manufacturing and continuous improvement. By following the above steps manufacturers can optimize their process by reducing waste, operational costs, improving quality or going after other key metrics.

digital transformation guide

Want to Learn More?

Download our Digital Transformation Roadmap and learn what steps you can take to achieve data-driven success in manufacturing.

Download PDF
0