Automatic Value Stream Mapping

Introduction

The goal for today is this:?How can we make Value Stream Mapping:

• Easier to perform?
• More accurate?
• More comprehensive?
• With more context?
• An ever-green, on-going part of the daily management process?

To get started, we will take a quick look at the traditional mapping process. We’ll also quickly review the typical mapping process output.

Traditional Purpose of Mapping

Let’s start with a basic question:?Why do Value Stream Mapping?

The purpose of value-stream mapping is to identify and remove or reduce "waste" in value streams, thereby increasing the efficiency of a given value stream. Waste removal is intended to increase productivity by creating leaner operations which in turn make waste and quality problems easier to identify.

Value stream mapping accomplishes these goals by allowing the team to see the big picture of how materials and information flow through the manufacturing process. This visualization allows them to identify areas of waste, select priorities and allocate resources to optimize the flow.

In addition, the mapping process helps the team generate ideas on what the next “future” state of the process looks like, as well as the “ideal” future state.

Mapping Process Overview

Next, let’s take a high-level look at the overall mapping process.

The process usually begins with a kickoff workshop.?In this workshop, we define the scope – what value stream will be the focus of the exercise? Is it for a single product, a product family, or something else? Do we need to do a Product Family Analysis initially to figure out which products we can group together for analysis? The other typical steps in the kickoff workshop involve determining who needs to be involved and creating the plan for how the exercise will be completed. Other steps at this point may be some initial charting such as a “Brown Paper Chart” that gives a very rough, high level flow of materials through the manufacturing process.

The next step in our typical mapping process is Basic Mapping. In this step, we create the basics of some of the fundamental mapping outputs such as the actual Value Stream Map, the Quality Filter Map, Spaghetti Diagrams, etc. At this point, we may just outline our initial ideas of the flow, where quality issues are happening, etc.

Once we have the mapping basics completed, then we need to collect data about the current state to add to the maps. While this can be accomplished in several different ways, the traditional path is to get out to the floor and view the process. This could mean talking to the operators and supervisors to get their input on the takt time and performance information, or it could mean monitoring the process and collecting actuals for a while to get the data. It is also fairly common to perform a Manufacturing Audit at this point. Some examples of criteria for the audit are Schonberger’s 16 Principles of World Class Manufacturing, and Kobayashi’s 20 Keys.

After the data has been collected, it is time for the current state workshop. This is where the team reassembles to add the data to the charts created back in the Basic Mapping step. At this point, the team will look at the completed visualizations to identify where there is waste in the process.

The next step is to perform the detailed mapping. This could either be done during the current state workshop with the entire team, or the team may break into subgroups to create the necessary analyses. We’ll have more details on this step on the next slide.

Once the overall analysis of the current state is completed, it is time for the Future State Workshop. During this workshop, the team will gather again to go through the analysis of the current state, brainstorm ideas, document the planned improvements and create the action plan for improvement. There are two possible future states that the team may produce. There should always be a “Next Future State” map where the immediate goals are documented. Optionally, the team may also produce an “Ideal Future State” map where all possible waste is removed. Either way, the team should create a detailed project plan for executing the improvements to get to the “next” future state.

The final step is to “make it so” and implement the proposed changes.

Typical Mapping Outputs

While the Value Stream Map itself is a key part of the process, there are many outputs from a complete mapping project. The list on this page is by no means meant to be comprehensive! It is simply a view of some of the most common outputs of the process.

Here is a quick summary of each of these:

• Product Family Analysis – this analysis looks to group individual products into families based on identifying commonalities in their manufacturing processes.
• Overall Lead Time Mapping – this can be viewed as a gantt chart or as a pareto and looks to quantify the total lead time. This can cover information flows, material flows, manufacturing, service, etc. Anything that is considered part of the lead time for that product.
• Brown Paper Overview – this is a very high level flowchart of the overall flow within the factory
• Supply Chain Structure – is a high level analysis of the various suppliers in this process.
• Seeing the Whole – this is very similar to the value stream map within a plant, but views the overall, intercompany supply chain map instead. Entire plants will be represented as a single box in the flow.
• Demand Amplification Map – Amplification occurs from batching and inventory control policies in the plant and overall supply chain and leads to “lumpy” demand profiles that resist level-loading in the plant. The purpose of this map is to identify those amplifiers to see if anything can be done to mitigate those effects.
• Cost Time Profile – This is simply a chart that shows accumulated cost against accumulated time. This costing information can provide important context when identifying waste and mapping the ideal state!

• Value Stream Map – This is the traditional VSM and is pictured in the diagram. It is meant to document how information and materials flow through the process to identify areas of non-value add activities and other wastes.
• Demand Amplification Map – This is the same as the map in the previous section, but isolated to amplifiers within the plant
• Spaghetti diagram – a map of movement within the facility. It is used to identify wastes of transport and motion. This can be applied to the overall material movements, but also to movements of people when performing tasks such as setup.
• Quality Filter Map – this tracks the rates and sources of defects during the process. This is typically a high level map, but as we will see later, it can be used to create a much more detailed view of quality when additional detail is available for drilling down.
• Activity Sampling – in this activity an observer will randomly observe the process periodically throughout a day or week and observe how often the process is in a value add state versus a waste state (and which waste state it is in).
• Cost Time Profile – This is the same as the map in the previous section, but isolated to costs and time within the plant
• Order Tracking – this tracks the information flow for an order across:?Planning, generation, cost estimation & pricing, order receipt/entry, order prioritization, scheduling, procurement, manufacturing, assembly, test, shipping, installation, billing, returns, and post-sales service
• Information Value Stream Map – This is the “Information Flows” portion of a typical value stream map. Everything from the supplier in the upper left to the customer in the upper right should be mapped.
• Process Activity Maps – these are very detailed maps for areas of particular concern. The information is similar to a value stream map, but is captured in more detail

Traditional or Digital?

That was a quick overview of the typical, traditional mapping process and its outputs. As can be seen, it is a highly involved process that incorporates efforts from many people. While it has tremendous value to the organization and is a critical part of any lean implementation, the traditional approach is also very wasteful. Most of this data already exists and is available for use. So why gather the data manually? We will take a look at the pros and cons of each approach in this section.

Why Continue the Traditional Approach?

Greater Involvement

These are things that I have heard from countless lean professionals. One of the primary advantages is a greater involvement from the people. As people are collecting information, they are going out to the floor and interacting with the process. They are having conversations with the operators and the supervisors across different shifts. The feeling is that there is much greater involvement of more people than if all of this is just automatically generated on a computer screen.

Lasting Ownership

This goes along with involvement – the feeling from traditional continuous improvement experts, is that there is more lasting ownership from the manual effort. They feel this is because when someone participates to that extent and they go through all the work and effort, it will drive a more lasting ownership of the results. Not to mention an improved understanding of the process itself.

Improved Understanding

On the other hand, if the whole value stream map is generated automatically the people won’t have the same level of effort or understanding. If everyone goes into a conference room and they look at it projected on screen, they will not have the same depth of knowledge as they would if they were out on the floor, collecting the data by hand, walking the process, talking to the people that are involved in it on a day-to-day basis, and really experiencing the process itself.

Visualize Possibilities

To visualize the potential future states, people have to have that deep understanding. When they have spent time walking the process to map everything out, they should be able to better “see” what the future could look like if the process were changed. The waste in the process is there physically when you are on the floor instead of when the person looks at a computer screen. The more involved the person is on the floor, the more they should be able to see. For example, as they are doing activity sampling and marking when a process is in value-add versus non-value-add states, they should be able to better envision a without that waste.

Investment in Change

This is driven by the involvement and ownership of the process. The thought is that someone that has put in all of this effort, done all of this analysis and collected all this data by hand will be much more invested in that next step, which is taking actions.

The example I use all the time for being invested in change is a Fitbit. I have been wearing a Fitbit for over seven years now. I will admit that I have not lost any weight during that time. It is not that the Fitbit doesn't work. It works great – it tracks all my steps and shows me all the pretty charts and graphs. But I have not lost weight because I have not changed my behaviors.

If people are not invested in making changes and committed to changing their behaviors, then they will not remove the waste. This is quite literally the case with my Fitbit (well, waist, in that case).

Summary

Those are the most typical reasons I hear for continuing the traditional, manual approach. They are pretty strong arguments! In our next section, we will take a look at the digital approach and its advantages.

Benefits of Going to Digital

Overall, it is a strong argument to remain manual. What then, are the benefits of going digital and why look at this in the first place? Primarily it revolves around accuracy, breadth, speed and ongoing benefits. The focus shifts from collecting data to making improvements. Instead of creating something that hangs on a wall, it becomes a tool people use on a daily basis.

So let’s go through the benefits of digital in more detail.

All Products at Once

First off, when you're doing the value stream data collection manually, the reason that you focus on a single product family is that you have to narrow the scope. It is not possible to gather data on all products at once, so the focus gets generalized to the product family level and narrowed to a single family.

When mapping is done digitally, the system is connected to the data sources that contain the raw data. And those systems have the data for all products. Instead of being forced to select a family for the analysis and then going out to collect the data, you will have the data for all the products at the beginning. If you want to run the analysis for a single product line, that choice is still available. But now it is a choice instead of the only option.

More Precise and More Accurate

The information coming from systems that gather data 24/7 is far more precise and accurate than information gathered by hand. I cannot emphasize this one enough. To begin with, the data that is typically gathered by hand is almost always biased. If someone is standing next to the line and watching the process collecting data, people simply behave differently.

It is like when you're driving down the highway and see policemen on the side of the road. Most people see that officer watching and slow down and drive by the rules. People behave differently. It is called the Hawthone effect and has been studied extensively in psychology.

Because of this, when you are standing by the line collecting data, the operators will behave differently than normal – it is human nature. So you are collecting bias data when you stand there watching the process. When the data is collected 24 hours a day, 7 days a week, it is unbiased because that's the way the process always works, by definition.

The data is also far more accurate because you capture everything, in detail. You capture the common events. You capture the rare events. You capture the things that happen quickly that often go unrecorded in manual data collection.

Furthermore, you capture much more context with the systems than with a tick sheet. You will know what products were running, who was the operator at that time, what were the failure codes from the PLC, and much more.

Capture Process Deviations

In the manual process, the process is captured by seeing how it works in a snapshot and in interviews with people involved in the process. When those people are asked how the process works, they will generally give an idealized version. What gets captured is the “smooth” version of the process flow. What does not get captured in the manual process are the deviations from the standard flow.

When the data is being collected 24/7, you see all of the waste; you see all of the things that go through rework cycles; you see all the things that take a non-standard path through manufacturing because the machine is down or because Joe isn't here today. With the digital data collection, you see the actual paths materials take through production. You see all the process deviations that take place. You get the full, comprehensive picture of the value stream.

Averages & Distributions

Another weakness of traditional value stream maps is the use of a single number to represent process times. In some ways, the amount of variability in a process is even more important than the mean. For example, the average time can be considered during the planning process to get materials to the right place at the right time. However, when there is a large amount of variability in the process, it is likely that some activities will be idle because they are starved or blocked.

Therefore, it is very important to show measures of variation and distributions in addition to the mean value. Statistical measures such as the range and standard deviation can be used on the traditional map itself. There can also be drill downs or popups to show a histogram of the distribution, as well.

Automatically Updates with the Process

Now we come to perhaps the single biggest differentiator between the traditional method and the digital.

The traditional mapping process is done as a project. The kickoff meeting takes place, information is gathered, the mapping is completed, and actions are taken to achieve the future state process. Once those next steps are completed, the mapping outputs will typically be put into a drawer or hung on a wall. As the process continues to evolve, the maps no longer accurately represent the current state process. Things are done this way in large part because of the effort required to gather the data about the manufacturing process.

This one-time, large-scale effort is very contrary to the continuous improvement process and philosophy of incremental change.

With the digital approach, the map is continually updated to match the manufacturing process as it currently operates. The value stream map will automatically update because it is connected directly to the machines and is collecting information from the shop floor 24/7. When the cycle time is improved, the data reflects that change. When WIP is removed from the system, the map will automatically update to show the new results. If a SMED project is done, the setup times on the value stream map will show the impact.

Instead of something that goes into a drawer or becomes a decoration on the wall, the digital VSM is a literal digital twin of the manufacturing process. Not only will it continue to update with the latest data, but it can also be used as an ongoing management tool.

It Can Be a Part of the Daily Process

Because the value stream map contains information about how long processes are supposed to take (and how long in between processes), it can be used as a tool to identify when things go off track.

As manufacturing is being monitored, the results can be compared to the value stream map and alerts can go out to those responsible when the process is not performing as expected. When deviations from the standard process (for example, with rework) take place, those deviations can be noted and highlighted.

The maps can also be used as part of the daily communication process. As an example, the live map can be used during shift handoff meetings to highlight recent deviations. The maps can be used as part of Kaizen projects to measure the impact of those improvements. Using the maps in this manner create the kind of deep understanding of the process in a much broader audience than is possible during the traditional mapping project itself.

Focus Shifts to Improvements

This understanding and sense of ownership is one of the key objections I hear from people to going to a digital approach. As mentioned earlier, the belief is that when people do not get out to walk the floor and collect the detailed data that the mapping process will not create the same level of understanding or depth of ownership.

In my experience, this fear is only valid if the mapping process is still run in the traditional manner with the data collection process being automated.

As described in the previous section, the ongoing use of the maps to monitor and improve the process can greatly expand both the understanding and ownership of the process. Even in the initial mapping project, the digital approach can further those goals. Collecting data manually is a time-consuming task. If the data collection is automated and immediate, how can that time be better allocated? The team should still be encouraged to spend time on the floor understanding the data. But the updated focus drives a much deeper understanding.

Instead of simply making tick marks on paper or entries into Excel, the team should be analyzing the data about the process and investigating the results on the shop floor. What are the primary sources of observed variation? Where does the data highlight waste that can be removed from the process – and do observations on the shop floor show that those changes are feasible?

In short, by automating the grunt work and shifting the time invested from collecting data to improving the process, we will increase the understanding in our people and the results on the shop floor.

How to Go Digital

We have established there are many advantages to a digital mapping process. Now we will cover the question, how do we actually go digital?

Data Originates at Every Step of the Value Chain

Vast quantities of data originate at every step of the value chain. This image is an example that we looked at with a bakery company. We looked at everything within the four walls of the plant – starting at receiving and all the way out to shipping.

At every step along the process they were already collecting information. There were PLC’s that collected detailed information about each step in the manufacturing process. Higher level work order information was tracked in their MES system. Quality information was available in SPC systems and more. Without doing a complete inventory…there was a lot of data to tap into when we began the mapping process!

Where to Get the Data?

Unfortunately for this conversation – this varies with every customer. Different companies have different systems implemented in different ways and also have different goals for their mapping process.

But there are a number of standard systems where we look for the data. The list below is not meant to be comprehensive, but these are the common places where we start to look for the data we can use in the process.

• ERP
• MES
• Planning Systems
• Supply Chain Systems
• Industrial Internet of Things
• Employee Time Tracking Systems (e.g. – Kronos)
• RFID Tracking

High level data such as routings, inventory status changes and so forth can be found in ERP systems or MES systems. Depending on how these systems are configured, they may also contain information about how long orders are spending at particular tasks or between them.

Planning systems can also be used to find routings and how long orders are supposed to spend at a given process and when they are expected to reach the next operation. The planning systems and ERP systems can also be used to track materials back to the suppliers, if necessary.

The planning process can also be very illuminating when looking at information flows.

However, Industry 4.0 systems can take this information to another level of detail. ERP systems are built around credit points and MES systems are not always implemented to capture detailed information about every operation. Industry 4.0 systems such as I-IoT can provide information at any level of detail useful to the analysis.

This is also where an RFID system can be utilized to generate process information. As one example, you can track material or human movements using RFID to generate a spaghetti map. This enables you to see all of the movements taking place over time. Once again, this can be done on a continual basis instead of a one time project to track those movements.

There are many additional potential sources of data, as we will see in the next section highlighting quality data.

Where to Get the Data: Quality

When creating mapping outputs such as a Quality Filter Diagram (QFD), this can be done manually by collecting data for a short period of time. But this diagram significantly benefits from an automated approach.

A traditional QFD contains just a few categories of issues at each step in the process to identify where defects are being caused. This level of information is rarely actionable. As we will see in the next section, this information is performance data as opposed to diagnostic data.

When looking at using systems to provide this data, there are typically a large number of systems at each company that contain some form of quality data.

For example, we worked with a firearms manufacturer that asked us to create a quality dashboard across their facilities. Within their plants they had 57 different systems that contained quality data. It was nearly impossible for any single person at the company to know about all the systems, let alone use them for analysis.

As part of our project there, we looked at the information flow for quality and created a single dashboard that could be used on an ongoing basis for driving improvements.

Providing Actionable Metrics

The next step is to focus on what type of information we capture from the process. One key is to collect information beyond simple performance data. Instead of looking at a metric such as first pass yield (FPY), it is important to know what is driving the rejects. Without that information, you can tell if things are getting better or worse, but you will not know why!

While this is not part of the traditional mapping process, it is important to making the mapping process more valuable as a management tool.

Enhanced Data Capture

There are many ways to organize the diagnostic data. One way to do it is on a fishbone (or Ishikawa) diagram. It can also be done through pareto diagrams with drilldowns, treemap diagrams, and many more.

The key is that you want to have that structure defined as you are capturing information from the process. Then it will be available for the analysis that you want to do later.

There are several approaches to this categorization. One is to have the operators enter information when events occur on the shop floor. Another is to capture fault codes and other data from the machines and defining a map from those codes to the diagnostic categories. Another way is to use machine learning capabilities to automate the categorization efforts.

How to Show the Maps

The final topic to discuss in going digital is how to display the mapping outputs. Given the number of possible outputs, data sources and goals for improvement, there is no single recommendation on which approach to use. In this section, we’ll cover a few that we have seen successful with our customers.

Purpose-Built Lean Solution

One approach is to use a purpose-built lean system. These types of systems are specifically designed for lean manufacturing analysis and have many, if not most, of the mapping outputs built into the software. There are several such packages in the market, but it is outside the scope of this document to get into the details of one system versus another.

One of the key factors to consider when looking at those systems is how easy it is to feed data into the solution. If it is a manual process to load the data, that should eliminate that system from consideration. Then consider which built-in reports it contains, how flexible it will be going forward and how well it will evolve along with the process.

Business Intelligence Solutions

The next option in our list is utilize business intelligence (BI) systems such as PowerBI, Tableau or Qlik. These systems are very good at taking fragmented information across disparate sources, consolidating that information together and then providing cohesive views of that data.

These systems will also provide for ongoing data collection from those source systems. The downside with these tools is that none of them have the mapping outputs pre-configured. It must be done as part of an initial implementation. This includes the creation of the display widgets such as appear in the value stream map itself. However, once that initial process has been completed, the system should be reasonably easy to modify going forward.

Industrial Internet of Things Platforms

Our next solution in the list is an Industrial Internet of Things (I-IoT) solution. These solutions have similar advantages/disadvantages to the business intelligence options with a couple of key exceptions.

The first is that when a data source does not exist in the BI approach, another new system must be implemented to perform that data collection. When using an I-IoT approach, that system itself can plug practically any data collection gap that exists. In addition, the I-IoT system can act as a hub for data on the shop floor to persist data and also to integrate systems where necessary.

Where I-IoT systems typically fall a bit short is the analysis of historical information. While it is possible to do so in those systems, that function is much easier in a BI system. Some of our customers have utilized a blended approach between these two solutions in a very successful hybrid approach.

Custom Software Development

The final approach we have seen with our customers is the custom software approach where everything is built from scratch. Some of the customers we have worked with have tremendous capabilities within their IT departments. A few of those customers had already put custom solutions in place for data collection. In those cases, it made sense for them to continue that development to add these visualizations and workflows in place on top of their existing code base.

Closing Thoughts

Now for some closing thoughts on this overall topic.

It has been discussed, but how do we combine the benefits of the manual approach with the digital. There is a phrase from David Mann’s book Creating a Lean Culture that is relevant here. He talks about maintaining people’s “fingerprints” on the data as being critical to the success of visual controls within the factory that I believe apply here, as well. I’ve touched on many of these topics, but I believe this is worth addressing again as it is the biggest question I often get from people looking at manual vs digital.

These are the five advantages of the manual approach discussed earlier. Let’s take a look at how to address each one of those in a digital context.

Greater Involvement

People do not become less involved because of digital solutions if the process is run slightly differently. The involvement simply shifts the focus. Instead of doing the grunt work of collecting the data, people can focus on process improvement.

The same amount of time can be allocated to performing the mapping process in the future – you simply have the people spend that time in different ways. Instead of having people stare at a single operation for days on end collecting tick sheets or other raw data, have them take the data that already exists and deepen their understanding of why things are happening that way. What are the drivers behind the waste? What improvements are truly feasible on the shop floor?

It is entirely possible to have much greater involvement with the digital approach when it is done correctly.

Lasting Ownership

In this particular case, I don’t believe there is even a close comparison between the approaches. Digital wins in a landslide.

Instead of hanging on a wall or being stuff into a drawer and forgotten, a digital value stream map becomes a part of the daily process. It can be embedded into the leader standard work by setting up alerts around process deviations and failures. Because it can be turned into a live system that evolves with the shop floor, it never gets out of date and drives lasting ownership from the people using it on an ongoing basis.

Improved Understanding

People should still go to the floor even when the data is automatically collected. They are not barred from the shop floor by going digital! Given the greater depth and breadth and accuracy of the data, it gives a much deeper understanding than the grunt work of data collection.

With digital, it is possible to do both. You can go out and watch the process and see how it's working and follow each step. But you can also have all that information available to give you that deeper understanding of the process.

Visualize Possibilities

Everything that is done in the traditional future state workshop can still be done. If you want to, you can still use sticky notes and put them on the wall to represent the process and then rearrange them as the analysis is performed. If you want to do that, you still can! There is nothing that stops you from that.

But with the digital approach, when you put those sticky notes up to represent the current state of the process, you will have the data behind it so that it actually represents the current state of the process. The more comprehensive data can highlight many more opportunities than the limited data of the manual approach.

Investment in Change

Finally, the traditional idea is that people will be more invested in change if they are on the floor doing the additional work of manual data collection. As stated above, this is simply not the case if the digital approach is performed correctly.

In closing, the digital approach does not eliminate people’s “fingerprints” from the process. It simply shifts the focus from the grunt work to the outcome. Once the primary focus is on the outcome, it helps drive lasting ownership and investment in the process. Which all leads to improved results.

Feedback or Questions?

If you have any feedback or questions, please leave a comment or reach out to me at [email protected].

Thanks!