Insiders View on Reducing CNC Machine Downtime

Exec Summary 

A leading manufacturer of specialized metal products for the aerospace industry had significant challenges with visibility into their finishing shop. The issues were primarily around downtime and bottlenecks. They were having a negative impact on the plant’s production throughput, revenue, and on-time delivery. 

After a few conversations and a Digital 360 review, it was clear an IIoT (Industrial Internet of Things) / Manufacturing Analytics solution was required to monitor the machines to get accurate, real-time data from the plant. 

A solution was put in place to monitor 23 presses and CNC machines. The results of the solution were very valuable. They were able to get accurate, real-time data which enabled them to find the causes of downtime, do some root cause analysis, apply corrective actions, get a nearly 40% reduction in downtime, and set themselves up for addressing more valuable challenges. 

Lessons Learned


We’re doing something different here. We are going to talk transparently about a customer and how they were able to solve some challenges in their plant. In this situation, we will talk a little about the solution in place. However, most of this will focus on details typically not discussed regarding IIoT / Data-Driven Manufacturing solutions.  

Specifically, we’ll discuss topics including specific steps the manufacturer took to solve their challenges and their learnings, how we and the manufacturer worked together on certain aspects of the pilot project, the value the company got from the project, and even some hiccups along the way on both sides. Our hope is that you’ll find this valuable and can learn a thing or two from the discussion. We will highlight the lessons learned for the manufacturer which you might find valuable. (We have removed the names for sake of confidentiality.) 


The company is a specialized foundry/manufacturer that produces specialized ferrous and non-ferrous products from foundry mold to the final finished product. They have a finishing area comprised mostly of CNC machines. One of the final stages of processing the metal is to finish the products in 3- and 5-axis CNC machines.  

CNC Machine



They knew they were having issues with production. They determined this by looking at the number of products going out the door, the number of operators and machines, and the estimated cycle times. The team felt they should be getting more production than they were, but didn’t have any strong data to back up that argument.  

The operators were reporting that they were working full tilt. So, some team members were recommending to the president that they buy another couple of machines and hire some new operators. However, there were a couple of instances where the president walked around the plant with one of the manufacturing engineers and asked a critical question…why were so many of the machines idle when he walked by. It was then that the manufacturing engineer realized they needed better data. 

Data could be obtained manually by putting together a spreadsheet with dropdowns for downtime reasons, and record data manually. But who will record the data reliably? As a manufacturing or process engineer, you can’t be there yourself. Our COO here at Ectobox, Dave Grafton, in a prior position, worked at a plant that didn’t have any manufacturing analytics solution at the time. To solve a certain challenge in the plant he had to spend a full 3 days with the operators on the plant floor to understand what’s going on. Obviously, a manufacturing or process engineer can’t spend 3 days to gather data for each of the problems in the plant. There needs to be an automated way to get the data. 

If the manufacturer is truly working to become a data-driven company, they should be able to provide the data to the smart people in the plant who are solving the problems…the manufacturing engineers, process engineers, Lean and continuous improvement teams, and also the operators. 

With an IIoT solution providing up to the minute data directly from the plant floor and operators, the plant engineers can quickly iterate through issues, solving problems, and moving to the next problem. 

The manufacturing engineer at this plant realized getting operators to record more data manually wouldn’t work. She also realized the idea of spending anywhere between 1 and 3 days full-time on the plant floor also wouldn’t work. 

Her situation was also compounded by multiple other issues in the plant (no plant has only one issue, as we all know). The other issues included scheduling and on-time delivery challenges, bottlenecks, certain machines experiencing some excessive downtime for unknown reasons, a need to rearrange the flow in the foundry, and there were several Lean Kaizen events to finish out. So what does one do? What do you do in this kind of situation? 

Where to Start 

In this kind of situation, the first thing to do is step back and clarify your priorities. To do that one should use a process that answers these questions: 

  • Where should time be spent to get the biggest value to the company?  
  • What challenges in the company have the lowest complexity and highest value?  

These questions can be answered by spending a bit of time (not a lot) with a small team going through a straightforward process that we call a Digital 360 (aka Digital Operations 360 Review). The end of the process produces a valuable, validated roadmap of tasks and projects to tackle. This process gets all information about the challenges on the table and produces a validated and valuable roadmap of what challenges to tackle next and how.   

This clarity on the challenges for this manufacturing company and the resulting roadmap was important. There were a lot of issues, a lot of competing interests for the manufacturing engineer’s time and for the company’s other resources. Going through this process was our first recommendation to this company when we were introduced to them. 

We’ll admit, it can be a challenge to pull a small team of leadership and some team members together, and focus on this process. However, once it’s done the value of the process and the roadmap are huge. 

Here is a quick summary infographic on how it works. Digital 360 infographic

The result of the Digital 360 was a roadmap with the priority being a pilot project to monitor machine utilization with downtime reason codes. Gathering production data turned out to be a nice-to-have solution in the short term and, therefore, wasn’t required. The downtime data with reason codes was selected because it was a lower-cost option for monitoring the machines and provided the highest value and return, relative to other projects on the roadmap. The team also decided to run the project as a pilot project on 2 machines. This turned out to be a nice way to introduce the new technology and the new data to operators, manufacturing engineers, and leadership. 


Lesson Learned 

Use the Digital 360 process to identify low complexity, high value challenges to solve and create a roadmap. That helped this company clarify that a pilot project to address machine downtime was the most valuable and simplest project to execute. 


Project Planning 

Once the decision on the pilot project for monitoring 2 machines for downtime with reason codes, we recommended the project be treated like a “real” project. Some companies will immediately dive into setting up the technology. Then when things go wrong in the project and scope creeps there is no limit defined, no line drawn in the sand to know when to adjust, approve changes, or pull the plug. 

For this, we always use a Project Charter document. Here is a sample we use for some larger projects. We are big believers in keeping things simple. Having said that there is also a Goldilocks zone for planning projects, even the small ones…not too much, not too little, spend just the right amount of time planning. And that time planning is proportional to the size, complexity, and value of the project. 

Project Charter

If nothing else, define the following basic criteria and check-in with the people in the project around these parameters on a weekly basis: 

  • Goal/Objective of the project: make the goal a clear, SMART goal, maybe include ROI (Return on Investment) 
  • Due Date: when will it be done 
  • Budget: money and time/resources to spend on the project 
  • First several tasks, to get the project started 
  • Primary Stakeholders: Who is the PM on each side of a project (vendor and client), and who are making the decisions on the project 


Lesson Learned 

Run every project like a project, with a goal, budget, due date, several next tasks defined, primary stakeholders defined, and check status weekly. For more formal projects use a Project Charter document. 


This concept helped the manufacturer gain clarity on the project, success criteria, and the potential ROI. It seemed this wasn’t practiced often at the company. The manufacturing engineer liked the process and has since used it on other projects with success. 

Business Case and ROI 

Part of defining the project plan is defining the goal. That goal should include the project achieving some value for the company. This can also be the ROI (Return on Investment). The people planning the project should define a metric for determining whether the project and solution were a success. Simply saying the project is done, the machines are connected, and pulling data isn’t sufficient. You need to dig another level deeper. 

What is a hypothesis you can create about the project? Here’s an example: “We believe we can reduce downtime on a single machine by 40%, which produces an additional $30K in product each month.” Creating a hypothesis provides extreme clarity for measuring success. The IIoT system implemented, if that’s the solution being put in place, should also be able to provide those actual numbers…you should be able to see 40% downtime improvement on a chart, and see the $30K additional manufacturing value on a screen. 

Project Launch 

For this project, as we do for all projects, we went through a short Project Launch call/meeting with the manufacturer. On the customer’s side, the call included the CEO as the Sponsor in the Project Charter, the Manufacturing Engineer as the Champion, an IT person, and the plant manager. In the meeting, we reviewed the outlines of the project plan, confirmed some details, scheduled the first couple of activities including the On-site Visit and when to connect the machines, and ended the meeting. 

Site Survey 

We conducted a Site Survey as the next step in the project. The idea of a Site Survey is to review the machines in question to confirm how we will connect to and get data from them, review the layout of the machines and network availability, etc. For getting data from the machines often it’s a question of whether they have the capability to provide data via a common data communications protocol like MTConnect, FOCAS/FOCAS2, or similar. If that’s not possible our alternative is to use external/noninvasive sensors with small, reliable PLCs to get data from the machine (e.g., Banner Engineering sensors with a DXM gateway or similar products). We then create our BOMs (Bills of Materials), pull or order the products needed, and schedule the installation on-site. 


The Site Survey was a valuable activity. We’re often told by the company what machines we’ll be connecting to and sometimes provided a lot more detail than needed. However, we always find some surprises which, if not uncovered, would’ve been very costly during the project. This alone makes the On-Site Visits worth it. 


For this project, we were given some information on the machines regarding the manufacturers of the CNC machines and the controllers on the CNC machines. This list included the make and model numbers of the controllers and other data. It was a good thing we required the Site Survey, as we always do. It turned out some of the information on the machines was incorrect. Had we showed up with equipment based on the list of machines without seeing them we would have had to make some extra trips. The ethernet ports on one of the machines were buried inside the machine after some customization by the vendor. The other machine required an external sensor and IoT gateway. 


Lesson Learned 

Always do a Site Survey and review the machines in person to confirm how to connect and get data out, understand the network availability, etc. 



We then installed a single IoT gateway device (i.e., an industrial PC with machine connectivity software) and connected it to their Haas machines. We worked with the client to make small adjustments to the configuration of some machines (e.g., modify some settings in the Haas machines to make MTConnect data available). Then the machine connectivity software was configured, the machine connected, and tests performed for pulling data from the machine.  

While the machines were being connected our team worked with the manufacturer’s team including the manufacturing engineer and some leadership to setup SensrTrx. One of the best solutions we can put in place is where the customer knows how to set up and use the product so they don’t need to rely on our team for every little change in the future. 

Once the IoT gateways were connected to the SensrTrx platform in the cloud over a secure firewall the client was able to immediately see data for their machines. 

 Simplified Solution Design


Training of a few team members on the SensrTrx platform was brief, about 1.5 hours in total. Once the manufacturing engineer was training on the system she then trained the operators on the 2 machines for how to use the Operator Screens. That training also took only 1-1.5 hours. She emphasized to the operators, as everyone should including the company’s leadership, that the system wasn’t being used to allow big brother to watch them. It was about giving them and her data they all needed to do their job better, which is to do a better job for themselves, for their teammates on the plant floor, to operate more efficiently, and to grow the company because it’ll all benefit the staff even more. 

It turned out that some operators that were initially resistant to the project ended up being champions of it, especially with the profit share program already in place for the staff. They realized the level at which they were producing (i.e., not up to snuff), and they were accountable to each other and themselves once they saw actual downtime and production numbers.  

We have many times over that the numbers always go up after an IIoT solution is put in place. Often companies measure at an OEE of around 30% and in a matter of 6-9 months are able to get OEE up to 60% to 90% merely by implementing IIoT systems and sharing the data. 

Downtime Pareto

Lesson Learned 

Emphasize to the operations staff that IIoT solutions are there to help them. The numbers always go up after an IIoT solution is put in place and the data is shared with everyone. The people on the shop floor do care and do want to do better for themselves and for the company.  


How to Clearly Identify Issue 

The manufacturing engineer was ecstatic to finally see accurate, real-time from the machines that made sense. She was immediately able to see some of the downtime issues with reason codes. She let the system collect data for about a week to ensure she had good data for a normal operating week. This is when the manufacturing engineer’s real work started…finding out where the issues are and solving them. 

She needed to figure out where the biggest bucket of wasted time was, nonvalue add time. This analysis of finding nonvalue add time can be done by creating an accurate Value Stream map during the initial analysis process (Digital 360), which we often like to do. The issue with the Value Stream map is that is a snapshot of a point in time and can quickly become out of date. That data is also available on an ongoing basis from an IIoT solution to get accurate, real-time data. 

In this situation, the Pareto charts on downtime by occurrence and downtime by time from the SensrTrx IIoT platform were the best places to get the data. 

 The idea for addressing the downtime issue is to clearly identify the biggest bucket, and then to get as specific as possible with that biggest bucket to enable valuable analysis. In other words, for downtime, get reason codes that are actionable. 

For this client, the biggest bucket or issue was a lot of calls to maintenance. It was clear “Call Maintenance” as a downtime reason code wasn’t actionable, it wasn’t the information that helped determine the core cause. Therefore, the manufacturing engineer quickly realized she needed to adjust the setup of SensrTrx to ask the operator for the 2nd level of detail if they selected Call Maintenance. She was able to do that within minutes and without having to take any new training classes or write any new code. 

Once the data is specific and actionable, then the next step is to determine the cause using Root Cause Analysis.  One should ask themselves if the problem is uptime, utilization, runtime, or cycle time. Is the machine running in the most efficient way?  

Typically, most issues don’t occur during the production of the part on the machine, i.e., the value adds time on the machine. The cycle times or value-added time on the machine are often the smallest part of the time spent creating the project. The issues are often in the nonvalue add time, which is before and after the cycle time (i.e., changeovers, setups, programming, etc.). A typical number for the nonvalue-add time we see in discrete manufacturing time is 80-95%. It’s a startling number, 80%-95% of the time spent by a company to produce a product is not considered value-add time, time that is directly valuable to creating the project. 

Maybe the issue is the changeovers are too long. Digging deeper in a Root Cause Analysis could determine that the person doing the changeovers isn’t very good at them and therefore takes too long. Or maybe the run-time could be the issueit’s too long. Why would that be? Maybe the tools are dulling and need to be changed out. Getting accurate, real-time data on these issues into an IIoT platform for problem-solving can help identify these issues in a Root Cause Analysis process. 

Once the cause is determined, then a corrective action needed to be created. 

After the corrective action is in place the IIoT solution should be used to validate the corrective action was appropriate and the core issue is being solved. 

Lesson Learned 

To solve a challenge around machines, find the biggest buckets of time or resources spent. Then get clear, actionable reasons for the cause. Then do a Root Cause Analysis to drill into the true cause. Finally, establish a Corrective Action to put a solution in place.

Often the true causes of challenges will be in that 80%-95% nonvalue-add time spent before and after the part is produced.

For this manufacturer they found the cause of the issues were twofold:

  1. Misclassification of some downtime as maintenance where it should’ve been classified as changeovers; and
  1. Changeovers being done incorrectly then caused problems with the machine running efficiently, which then resulted in calls to maintenance.

The corrective actions were:

  1. better training on changeovers; and
  1. identifying a couple of operators as the most efficient and accurate change-over people, and setting them up for success as the go-to people for doing certain complex changeovers.

The manufacturing engineer monitored the downtime data for the two pilot project machines for an additional 1 ½ months and confirmed the corrective actions helped reduce the downtime by an initial 25%. She has since worked to reduce downtime by an additional 20% and beat the original metrics that defined success for the project.

Hiccups in Solution 

There were some hiccups in the project, there always are. Any company is lying if they said a project went perfectly. In this story, we’ll open up the kimono to show that no project is ever perfect. The challenge is then to still reach successful conclusions on valuable projects in spite of the challenges that inevitably come up. We’ll briefly review some of those challenges and how they were handled by everyone. 

In this project the issues included: 

  • IT/OT connectivity issues – Communication over the firewall between the IT network and the plant network). 
  • Addressing Wi-Fi coverage – The pilot machine was located around the corner and behind a metal latticework from the Wi-Fi router. This prevented good connectivity and required the use of a repeater. 
  • Supplies of products – The pilot project was implemented during the COVID pandemic which interrupted the supply chain and slowed delivery of a couple of devices). It has a small effect on the project schedule. 
  • Extra trip – Ectobox missed a couple of details on the connectivity of the machines and had to make an extra trip to fix the issue. It had a small effect on the project schedule. 
  • Leadership – The project was slow to start due to a lack of support by some leadership at the manufacturer, but then the ship was righted and quickly got on track. 

All these issues ended up being small and were readily handled. The star of the project was the manufacturing engineer. She worked tirelessly to ensure everyone was on the same page, machines and operators were prepped, she learned the tool in 1 ½ hoursgot a lot of value out of the completed solution, was a champion of implementing the IIoT solution on the remaining machines, and was wonderful to work with. 


Lesson Learned 

Project planning, the Project Charter, the Project Launch meeting, and continuous communication throughout the project between the manufacturer and Ectobox enabled the project to go smoothly and handle the small hiccups that are bound to happen in any project. Without that work ahead of time, the project could have gone much differently. 


The manufacturer in this situation had issues with excessive downtime on some CNC machines. The recommended approach in this project was to use a logical process to identify the priorities in the plant and a roadmap for tackling the most valuable and least complex challenges. This lead to implementing a pilot project on two machines to test the SensrTrx system, drive adoption in the plant incrementally, and solve some real, live issues with machine downtime.   

The project was successful, reducing downtime on the pilot project machines by about 45% to start. The manufacturing engineer is working on the process and using the SensrTrx IIoT platform to further the downtime. The next challenge to tackle on the roadmap defined from the Digital 360 is production scheduling and bottlenecks. SensrTrx and the real-time, accurate data from the machines will be critical to identifying the bottlenecks, why they exist, and how to eliminate them. 

The IIoT solution is now being scale up to all the other machines in the plant. In the end, the solution will likely prevent the company from spending about $500K in capital costs to buy a new machine which is no longer necessary at current order levels (do more with what you have). Additionally, the company is increasing production and revenue by about $340K/machine/yr (2 shifts/day, 7 hrs/shift, 20 days/mo, 6 parts/hr, $110/part = $185K/mo production revenue for 1 machine). 


Impact of Accurate Data for Lean Projects

Exec Summary


Many small manufacturers struggle with successful Lean efforts. Often the cause is bad data. Industrial IoT solutions can drive improvements in Lean efforts by providing accurate, real-time data. The data comes from remotely monitoring production, scrap, equipment downtime, processes, and waste in the production system with Industrial IoT solutions. Manufacturing staff can then closely monitor the systems over the short term to determine if the Lean efforts were effective and the long term to ensure the issues don’t return.

Value Stream Maps and Kata’s are specific tools often used in Lean efforts to identify and eliminate waste. These Lean tools use metrics to measure and reduce waste. Data from the Industrial IoT solutions can feed the data for these metrics. Using Industrial IoT as the source of the data provides accurate, real-time data which enables successful Lean efforts and compounds the positive results. Additionally, Industrial IoT saves significant time for recording and analyzing data compared to gathering data manually.



Are your Lean efforts as effective as they could be? Where do you get data to identify and monitor the impact of your Lean efforts in your factory? The answers to these questions can have a significant effect on the ROI of your Lean efforts. The best results of Lean work come from having accurate, real-time data. Without it, your results will suffer.

Accurate Data is Important


I’m sure you remember the essence of Lean is to reduce waste in your plant. People are often surprised to hear the statistic that nearly 80% – 95% of all activities in producing your products for your customers are non-value-add, especially in high-mix/low-volume plants. That means there is a significant amount of wasted motion, transportation, waiting, and more going on in your plant.

This waste can come in multiple forms: waiting and non-utilized talent of the operators during unplanned machine downtime due to machine breakdowns, overproduction of parts as a machine processes the part more than is needed, transportation as people and tools are moved around more than required, machine downtime due to longer change-overs which could be reduced, excess inventory piling up in front of a workstation, or excess inventory of spare parts for maintaining machines due to reactive maintenance practices, and more.

What is your process to identify that waste? One way to identify waste in terms of Lean is to create a Value Stream Map of your plant’s current operations and a Value Stream Map of your plant’s preferred future state. Creating the Value Stream map includes gathering data about the manufacturing processes, like cycle time, takt time, uptime, planned downtime, unplanned downtime, production lead time, processing time, transportation time, production counts, and other values. Users of the Value Stream Map then review the details to identify where waste is and how it can be reduced or eliminated.

The key to finding non-value-add activities in your plant is the data. Those values are captured on the Value Stream Map like cycle time, wait time, transportation time, and so forth. Everyone knows the expression “garbage in, garbage out.” It applies well in this situation. Suppose the data gathered and placed into the Value Stream Map isn’t accurate. In that case, the Value Stream Map user may incorrectly identify a source of waste or misapply resources to reduce a given non-value-add activity.

Case Study


To truly understand the impact of no data or bad data when reducing waste with Lean efforts, think about a situation in your plant. Let’s say we have a foundry manufacturer with a finishing area. They are a high-mix, low-volume manufacturer. The finishing area has presses and CNC machines. There are parts in a job that pass through several machines in sequence in the finishing area. Each machine performs a different task and has different cycle times. Currently, operators are recording production for each job and overall machine downtime on paper at the end of a shift. The company is so busy handling issues that it’s rare the shift supervisory or production manager has time to hand-enter data into an Excel file for analysis. It is also likely that maybe a couple of operators might be writing down data that may not be entirely accurate. The only reliable data that exists is the number of products packed at the end of the line.

In this situation, we don’t have much data to work with, only some basic spreadsheets with incomplete and possibly inaccurate data. Therefore, it’s difficult to identify what operators are working efficiently, what kind of wait times exist, is the machine downtime due to change-overs optimal, and whether there any issues with machines causing problems and resulting in unplanned downtime, etc.

What data should we get to measure? It’s hard to say until we use a Value Stream map to tell us, which we’ll do below. When creating the Value Stream Map, we’ll find there are two stages where we need to gather data: 1) at the point when we’re creating the Value Stream Map, and 2) on an on-going basis to confirm the Lean work has produced good results and those results continue.

Industrial IoT Turbocharge


For the example above, how will we gather and compile that data? You’ve already seen the challenge: operators have a job to produce parts, not be data entry clerks. So, entering data is not a priority for them. Additionally, entering the data by operators and compiling the data by others is a form of waste. It could be considered excess processing and non-utilized talent, if not more. It also creates an issue because the data is often incomplete and inaccurate. This means garbage in, garbage out in terms of results and value from the data.

If instead, we were able to automatically gather data from machines, operators, and other systems without manual intervention by anyone, we would get data directly from the machine and the data would be available immediately; the data would be accurate and real-time. This data will then provide clear visibility into what is going on the factory floor. This process of gathering data from the plant floor is where Industrial IoT comes into play.

What is Industrial IoT


What is the Industrial IoT (Internet of Things)? IoT is the process of pulling data from sensors on devices like machines in a manufacturing plant, pulling that data over a network, converting that data to valuable information, and then presenting that data to people to make decisions and, most importantly, take valuable action based on that information.

Industrial IoT solutions are typically made of a set of sensors, a gateway or edge device, a network, a software platform, and a set of security protocols and tools to ensure all data at rest and motion is secure. The sensors will communicate with the gateway/edge device, and the data is then sent to the software platform (aka, Industrial IoT platform). The data is often processed, filtered, and analyzed in the edge device and more commonly in the Industrial IoT platform. That platform is also where manufacturing staff view and analyze the data on a variety of devices. For legacy equipment, the sensors are added to the existing machine. For newer equipment, there may be sensors and a software system inside the machine (e.g., a controller in a CNC), or a PLC with sensors. In these situations, the gateway/edge device is connected to the newer piece of equipment or PLC, and the data is pulled, translated from the existing data protocol, and then sent on to the Industrial IoT platform.

The structure of an Industrial IoT solution above sounds complex. It turns out it does take some experience setting up these solutions, but it is by no means rocket science; it is also a straightforward solution. The types of tools, sensors, IoT gateways, and other devices that exist today make each project a repeatable solution, not a new science project for every situation. Additionally, highly experienced Industrial IoT engineers and project managers, along with an approach to solve business challenges first rather than technical challenges, are key to successful implementations.

One of the issues we have seen with small and mid-sized manufacturers is a fear of projects going out of control and costing hundreds of thousands of dollars to get any kind of reasonable data and a return on investment. This is undoubtedly not the case. The approach of working on a problem incrementally and starting with a pilot project for a Lean effort can limit risk and increase the ROI of the Lean effort. Pilot projects for Lean efforts can typically be put in place within a couple of days or a couple of weeks and cost a lot less than you might think.

3 Examples of Lean with IIoT


Let’s get back to our case study above, the foundry manufacturer with a finishing area. We’ll review how accurate data can significantly impact Value Stream mapping for identifying and monitoring wastes in a Lean project.

Visual Stream Maps


“Value stream mapping is a lean manufacturing or lean enterprise technique used to document, analyze, and improve the flow of information or materials required to produce a product or service for a customer.” ( Value Stream maps are a stepping stone to lead into various Lean techniques that fall under the Continuous Improvement umbrella. It enables the transition into the various Lean techniques because the resulting map clearly defines how the shop floor currently operates, no matter how well or how poorly. Value stream maps can also be used to define a preferred, future state shop floor operation. It can establish a clear goal of how the shop should be running after the issues identified in the current state map are addressed.

The definition of a Value Stream map includes references to the flow of information. This reference is a direct indication that data is a vital part of Value Stream Maps. Data in the Value Stream map provides context on the manufacturing process; where there are delays, where data is gathered manually, how many times a part is processed, where the parts are moved, etc. In other words, it is an important tool for identifying wastes in the manufacturing process.

When reviewing a high-level Value Stream map like the map above, you can see the data points to gather cycle times, change over times when there is planned downtime, wait times, production, number of people per shift, etc. These values are used in the example in this post. You can also display first pass yields, quality, scrap, reliability, expediting costs, and more. This data is collected using Industrial IoT solutions. Once collected, the Lean team can monitor the data after changes are made to the processes. This way the team can ensure the changes were valuable and then continue to monitor and improve the systems over time.

Let’s return to the foundry with the finishing area example above. Documenting the manufacturing process in a Value Stream map with current data would be a valuable exercise. This will provide valuable insights such as finding that parts typically pile up in front of a certain machine which happens to have a faster cycle time. Before creating the Value Stream map the inventory piling up may not have been a concern, possibly because they figured the piles didn’t site long due to the faster cycle times. However, additional data was added to the map which indicated the machine has long changeover times creating a lot of machine downtime and lost opportunities for production. The Lean team might then realize that if they worked to dig deeper into the changeovers they may discover some waste they could eliminate and vastly decrease the changeover times and related machine downtime. If successful, there would be a cascading effect which reduces the inventory and thereby increases asset utilization, production throughput, and revenue for that machine.

Other Examples


Here are a couple of real-world examples of manufacturers using Lean to identify and reduce waste with Value Stream Maps. These case studies are good examples of how accurate and real-time data provide big impacts when combined with Lean efforts.

Case Study 1

Here’s a situation we heard about recently which underscores the impact of using Industrial IoT with Lean. Additionally, the manufacturer identified the issue from a Value Stream mapping effort. This large manufacturer produces huge machines. Those machines were, in fact, train engines (i.e.,locomotives). The challenge was forms of waste resulting from the distance between the shop producing engines and the shop of repair technicians. The repair technicians had to walk a long distance to get to new locomotives about to come off the line that required servicing. When the technician arrived at the engine, there’d be a list of work to be done. If the technician didn’t have the tools and parts required, they’d have to go back to their area, get the right tools and parts, and come back. This caused issues with downtime of the crew assembling the engines and last-minute scrambling to reassign them during that production downtime. What wastes can be found in this situation? The three easiest to identify are transportation, waiting, and non-utilized talent. This particular issue was one of several around the plant.

Early on, the Lean/continuous improvement team couldn’t clearly identify this as an issue until they had some data from a Value Stream mapping effort. Once the data about transportation and waiting were captured “on paper,” it became clear this was a big issue. It quickly became clear that the best solution was to pull production and quality data for the engine from an Industrial IoT solution and provide each engine’s data to the technicians. They can then review the quality issues that exist from their shop, determine the parts and tools required, and go the long distance to the engine in question prepared for the work at hand. This access to accurate, real-time data resulted in eliminating nearly all the wastes identified, improving the production throughput, and reducing production costs.

Case Study 2

A manufacturer experienced issues with on-time delivery and was a mid-sized manufacturer with high-mix, relatively low-volume production (i.e., they produced highly customized parts in medium to low volumes). The engineering team’s customizations included work to modify drawings, confirm they are correct, and send them through a set of approvals. Part of the leadership team in engineering took on the challenge to solve the issue.

One of the first efforts was to create a high-level Value Stream map to document product and data flow. It started at the beginning, which included the sales team. Documenting the process was straight forward. Once the first version (without data) was completed, it was nearly impossible to see where the waste issues existed. Some effort was put into identifying part counts, cycle times, wait times, etc. As soon as the numbers were added to the Value Stream map one of the major issues quickly became self-evident. There was a ten-day wait time for orders from sales to get into the engineering department for processing. It turns out that sales wouldn’t request the right drawings and other detailed information from the customers. When engineering would get an incomplete order, they would push it back to sales or sit on it without notifying sales that the order was incomplete. Once accurate data was available in the Value Stream map, the issue was quickly identified.

The company then created a custom ECN (Engineering Change Notification) software product created by Ectobox (LINK TO CASE STUDY). Once implemented, the engineering leader monitored the improvements in wait times and throughput for his engineering department. The data also helped identify where they could make many additional improvements in processes to reduce the wait times, further increase throughput, and ultimately significantly impact on-time delivery to the customer.



Lean manufacturing relies heavily on data. In manufacturing, as with any business, the data in the business processes is gold. Having access to that data to understand, analyze, and improve the processes can make a company operate much more efficiently. The positive results from Lean efforts are compounded when accurate, real-time data is used.

How to Reduce Machine Downtime in 2021

Manufacturers all across the world are focused on trying to reduce machine downtime. Why? Because it is one of the biggest obstacles to overcome in the manufacturing industry, and expensive to deal with. From the machine itself to the product that is not being produced, this valuable time can get expensive very fast. This makes it clear that you need to do anything you can, and have a strong emphasis on your efforts to reduce machine downtime.

Gathering data to help reduce machine downtime.

What is the best solution?


As we have addressed, this is a huge problem. There are many ways to approach the issue, with one being the best, most efficient option that just makes sense. IIoT systems are the answer. Technology is advancing, automation is the future. Not just within the manufacturing industry, but in the entire world for every single thing we do in our daily lives. There is no way around it, that is the direction the world is going. If you want to stay in the game, you have to adapt. 

“You can either ride the wave of change, or you will find yourself beneath it.” This is a quote I like to keep in mind. Picture yourself on a surfboard in the ocean, you are just sitting there relaxing, enjoying the view. A huge wave comes into view and is approaching. You can either keep sitting there and be consumed by the wave, completely submerged. Or, you can start paddling and put in the effort to catch the wave, ride it, and stay on top. The same principle applies to nearly every situation one might encounter today. 

Companies that are thriving today are taking advantage of the technology that is available. What worked 20 years ago, is vastly different from what can lead to a successful business today. The “I’ve been doing this for 35 years and I’m still here today” mentality will not work anymore. 

This is not just a new piece of equipment, people are calling this the fourth industrial revolution (Industry 4.0). IIoT systems are not just for huge companies like General Electric. Remember when a 50” TV would cost you $3k 15 years ago? And now you can get yourself a brand new one for $200. Technology in general has become substantially cheaper, and more available. 

How does IIoT Reduce machine downtime?


I told you all about why you need an IIoT system to reduce machine downtime. Now I will tell you how it works. 

IIoT systems allow you to see everything that is going on inside of your machines. A set of sensors get placed onto the machines or we can connect directly into the machine and pull data out. We then connect the sensors or machine to a gateway edge device to filter and transform the data and then send it on to an IIoT platform. Then, tablets or TVs (which are cheap now as I mentioned in an illustration earlier, smooth right?) will display numerous dashboards showing an abundance of information from the IIoT platform. You can track the availability of products, increase productivity and performance, determine the real cause of problems, and of course, reduce machine downtime. The best part? You get all of this information in real-time!

No more spending time with machines off, inspecting them, doing routine, preventative maintenance to ensure nothing is wrong. This is all wasted time, very expensive wasted time! To do this manually, you have to stop production (wasted time), pay for someone to inspect the equipment (wasted time), and that is if there is absolutely nothing wrong with the equipment. If there does happen to be something that requires attention, that adds another level of problems that need to be diagnosed and fixed (wasted time). Our common goal here is to reduce machine downtime, not unnecessarily add to it. 

It is expensive, and it is a headache to fix all of these problems. Even just diagnosing the issue, finding out why a machine stopped working is a long process that can be avoided. With all of the data gathered and alerts from an IIoT system, you will know exactly what is going on and have a chance to avoid the problem completely before it even happens. Now you’re moving into doing Condition-Based Monitoring and also Predictive Maintenance. You will also gather insights from the data, and know exactly why this issue occurred. That way you can know your machines better, make better-educated decisions, and have the time to shift your focus onto other aspects of your company. Using these tools correctly will reduce machine downtime substantially, and result in more time, and money in other areas, without the headache.



IIoT systems are the key to a lasting business in today’s manufacturing world. Automation is changing the way the world does business across the globe. Again, this is not just a new idea or piece of equipment, this is the fourth industrial revolution. Changes are taking place across the board. Learn more about your machines, gather data, insights, reduce downtime. Use this information to make easier, more educated, clear, and confident decisions. Ride the wave, don’t get caught beneath. 

Feel free to contact us at Ectobox anytime. We are more than happy to answer your questions and help you along the way to reduce machine downtime and take your business to the next level.


Breaking it Down: Building Blocks of Smart Manufacturing

A Smart Manufacturing company is a manufacturer with fully integrated solutions that empower it to be highly flexible and responsive
to changing conditions, with self-healing systems. Those capabilities enable a company to reduce costs, generate more revenue, and

increase their competitiveness in their market.

So how does it all work, and where do these capabilities come from?

The Nine Blocks that Stack Up to Smart

There are 9 technologies or building blocks that stack up to create Smart Manufacturing.

  1. IoT (Internet of Things): Taking data from sensors on machines, moving it over the internet, and converting it to valuable information to help people make decisions and drive action.
  2. Automation and robots: Using robots and software to enable machines and people to work together more efficiently.
  3. Big data: Storing and using massive amounts of machine data for data analysis to gain insights about operations and machines.
  4. AI (Artificial Intelligence): Using software tools to build models and algorithms which can learn about niche areas of your facility to distinguish between normal and critical conditions.
  5. AR (Augmented Reality): Viewing models or images overlaid onto the real world on a mobile device.
  6. Additive Manufacturing: Creating objects from 3D models by joining materials layer by layer.
  7. Modeling: Portraying or defining aspects of the physical world in digital form for gauging equipment status, simulating operations, or controlling live systems.
  8. Cyber Security: Applying old and new ideas of encryption, protecting attack vectors, and combining digital with physical security tactics to protect data in motion and data at rest.
  9. Cloud: Moving away from local, on-site servers towards 3rd parties like Amazon, Google to store, service, and process data.

Stacking the Blocks

Each of these technologies can deliver value on its own. However, companies can realize exponential value by combining technologies.

Here are a few examples:

     Predictive Maintenance

  • Technologies: IoT + AI
  • Method: AI experts create a machine learning model. IoT pulls data from machines and channels it through the model to assess the probability that a part or whole machine may fail within a certain period of time.
  • Benefits: Clear identification of critical maintenance windows, resulting in up to 50% cost reduction by addressing issues only when needed but before failure.

     Robot-Assisted production

  • Technologies: Robots + Automation + IoT
  • Method: A “human assistant” robot is programmed to work alongside people to handle repetitive and mundane tasks or sets of tasks. The robot is pre-programmed to handle multiple task sets. Input from IoT data (such as production data further up or down the line) directs the robot to change its tasking based on real-time production needs.
  • Benefits: Increased production with existing staff and reduced pressure to hire/replace workers.

     Data-driven quality control

  • Technologies: IoT + Automation + Big Data
  • Method: Set up machines to test products immediately after a certain stage of production. Automatically analyze that data relative to product quality standards. Based on results, either trigger alarms for humans to intervene or have the equipment modify its own behavior appropriately to meet the required standards.
  • Benefits: Earlier notification of equipment problems, earlier identification of product quality issues, reduced waste, and rework. Increased production of a product to standards.

Choosing the Right Blocks

Some of the most exciting benefits of Smart Manufacturing come from the myriad and creative ways that new technologies can be combined and implemented. To find the right combination requires effort: thoughtful planning, employee input, and focus. But even small and mid-size companies can increase their competitive position, with access to the same building blocks as larger manufacturers.

It’s True: Custom Software Can be More Affordable than Off-The-Shelf

We talked recently with a manufacturing company that has a custom ERP system they built themselves. It performs the functions they need and it is customized to exactly the way the business runs. It’s a good fit.

Not long ago the company was purchased by a much larger conglomerate of manufacturing companies. The parent company is using SAP. They suggested getting the smaller, new subsidiary to use SAP but upon analysis, we quickly realized that the existing, custom ERP system was better for that situation, because it would have required millions of dollars to get SAP to fit the smaller company’s needs.

These are the situations we deal with every day, identifying when custom software solutions for manufacturing companies are valuable and appropriate, and then creating or customizing and implementing those solutions to fit the required business outcomes and specific, required features.

Do the Basics First in Reports and Business Intelligence

It’s like baseball. You want to focus on the fundamentals before you try the expert moves.

Let’s say you’re watching your local team slide in the standings, as we have unfortunately seen with the Pittsburgh Pirates. You’ll hear the sportscasters and managers and coaches talk about getting back to the basics before they get fancy.

It’s a solid piece of advice. If you have complex systems, and not everything is working, it’s best to keep it simple, and focus on these basics first:

1) Know your desired outcomes. What do you want to know from your software and reporting systems? How will the business benefit from tracking this data? What insights do the higher-ups need to make important business decisions?

2) Identify only the data you need to gather to get those reports. Don’t add an extra field or two because you think you might use it in 10 years. Focus on what you need now and only in the near term. There is always room for improvement LATER.

3) Create a list of issues that need to be fixed. Add details to each item such as specifically how to reproduce the issue or where it is, what is the result you’re seeing now, and what result should you see.

4) Tackle each item one by one. Make a concerted effort to not jump ahead, not to tackle only the easy ones, and not to allow yourself (or whomever is implementing) to get distracted and go off and do other things.

If you’re able to execute these basics, then you will surely climb up in the rankings and have great success.

Case in point: We are working with large manufacturing company that was acquired and is experiencing similar issues. There are some legacy software, database, and reporting systems left behind, not all of which work. We are starting our work by helping them get back to what they’re calling “ground zero”. We are identifying what business outcomes they want from the system, defining the details of what needs to be fixed, and cranking through the issues one by one until they are all fixed. Once done they’ll be really happy and will be able to have the data needed to make important business decisions. They will also be in a much better position to define and pull out new sets of data for data analysis and reporting to take the business to the next level.

How to Build a Winning IS Strategy

In the last post, The Biggest Software Mistake Your Company is Probably Making, we covered the basics of what an information systems (IS) strategy is and the importance of having one in place. This week, we’ll dive deeper into what makes a good IS strategy and how to build one for your company.

What makes an IS Strategy a “good” one?

First and foremost, it needs to be created by someone with personal understanding and experience with software and data systems. It’s not enough to have an IT leader with an understanding of hardware, networking, storage, and security to set and implement your IS strategy. Hint/workaround: If you don’t have a CIO on staff with deep software knowledge, your next best bet is to have a high-ranking staff member with required knowledge and experience take the lead.

Next, a winning strategy provides a clear definition of what the company needs to run the organization, and what software is and is not appropriate to acquire and implement. This improves the decisions that are made, preventing potentially poor IS decisions that end the end work against the company’s direction and growth, such as buying software that is too expensive or that doesn’t align with new products and services the company is providing, not staffing appropriately to support the in-house technologies.

Finally, a good IS strategy makes decisions easier for a company leader when deciding what IS actions to take to help the business grow. And it prevents the company from wasting large amounts of money and time with the wrong software or data initiatives.

How to Devise Your IS Strategy

The first question your company should ask itself is how intentional you will be about the growth of their company. Intentional growth implies creating a vision and plan for the next 5-, 10-, or 20 years. If IS decisions are made without consideration for the business plans, the results from the decisions can hold back the business, cause major disruption, and cause major losses in the business.

Once you have that vision, look for ways that IS tools and concepts can enable the success of the overall corporate strategy. You don’t need to make specific decisions on technology choices right away. Just record any ideas and organize them into an IS strategy document.

Questions to ask before you begin (query both executives and mid-level managers)

  • Does an overall corporate strategy exist? (If not, this should be created first.)
  • Does the company have a CIO (or person that is wearing that hat)?
  • Does the CIO have understanding and experience of software and data systems?
  • Do other management and leadership team members involved in making IS-related decisions understand the need for an IS strategy and how it will impact them?
  • Have IS-related decisions been made recently which have turned out to be poor decisions? (i.e., much more expensive than planned, projects failed miserably, projects made the company worse-off rather than better-off)

Quick Reference: Creating an IS Systems Strategy

  1. Establish your business goals and objectives in the form of a business plan
  2. Review current technologies in place and current IT/IS strategies
  3. Define how the business goals can better be facilitated with certain software
  4. Establish high level schedule for implementing new systems, aligning with business plan
  5. Organize the IT/IS strategy in a document
  6. Assign a senior leader to be responsible for executing the plan