Part 5: Analyzing Value Streams by Identifying Waste

An overview of the eight wastes of Lean manufacturing and how to identify them in both physical and digital aspects of the shop floor through VSM analysis.

Sara Hanks

Apr 14, 2024

Photo by ChatGPT 4.0 - Not exactly what I was going for, but close enough, lol.

👋 Hey, it’s Sara. Welcome to my weekly newsletter where I share insights to become more efficient.

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“The most dangerous kind of waste is the waste we do not recognize.” – Shigeo Shingo

Introduction to Waste Identification

As I was creating this newsletter, the power flickered for a 1/2 of a second. Just enough to reset the router & modem, which delayed my newsletter creation by 5 minutes, while I waited for the internet to return. Fortunately being Sunday, I’m snuggled underneath my sloth blanket and have the patience to wait. If today was a workday, I’d have gone on to do something else, delaying the output even more.

Have you ever felt frustrated when you're asked to redo a task you thought was done? Or perhaps you’ve experienced micromanagement when you’re unnecessarily hovered over and second-guessed at every click and keystroke. How about those painstakingly detailed reports, which, after hours of combing through data, seem to vanish, seemingly unread and unappreciated? These instances aren't just annoying—they're costing us time, energy, and morale. But what if you could spot these productivity thieves before they struck? As you delve into this edition of "Efficiency Explorer," you'll learn to identify the wastes lurking in your processes. Soon, you'll develop an eye for waste, much like noticing your model of car suddenly seems everywhere after you've driven it off the lot.

But what do we mean by waste? In lean manufacturing, waste is defined as any activity that consumes resources but creates no value for the customer. This concept of waste applies with equal gravity to processes that comprise our daily work. There are 8 different types of waste.

1. Unused Talent

Unused talent exists when employees' skills and knowledge are not utilized in the organization. For example, an experienced operator's suggestions for process improvements go unheard. Another example is a team member with a knack for data analysis who spends most of their time on administrative tasks instead of finding trends and insights.

2. Extra-Processing

Doing more work or adding more features than necessary does not add value from the customer's perspective is extra-processing. Repeatedly machining to tight tolerances when the product's design doesn't require it, is an example of extra processing. Generating detailed weekly reports that nobody reads when a monthly report would suffice.

3. Waiting

Idle time that occurs when waiting for materials, information, approvals, or equipment is “waiting” waste. Workers waiting for materials to arrive due to improper forecasting, or a project stalling because of pending approvals from several layers of management.

4. Motion

Unnecessary movements by people, do not add value and may contribute to fatigue or injury. Examples include employees who walk extra distances because tools are not stored at the point of use. Additionally, navigating through multiple software systems to collect information needed for a single report.

5. Inventory

Any supplies, materials, or products that exceed what is needed for immediate use. Overstocking materials take up storage space and potentially become obsolete. A backlog of requests or tasks that await processing in the office, such as receiving purchase orders, is considered transactional inventory.

6. Transportation

Unnecessary movement of materials, products, or information that does not add value. Moving products between multiple storage areas before final assembly. Also emailing documents back and forth for minor edits instead of using a collaborative document platform.

7. Over-Production

Producing sooner, faster, or in greater quantities than what is demanded by the customer. Manufacturing products ahead of demand, leading to excess inventory. Creating and distributing extensive documentation for a process that is rarely used or referenced.

8. Defects

Errors or mistakes that require additional resources to correct. Producing items with defects that require rework, consumes additional materials and time. Data entry errors that necessitate additional hours of correction and verification.

Each of these wastes represents an opportunity for improvement. By recognizing these inefficiencies, you transform from a bystander into a proactive waste detective, armed with the acumen to spot and eliminate these process culprits.

Identifying and Analyzing Waste During Value Stream Mapping

In the previous newsletter, we discussed how to create a value stream map. Waste identification is the next part of performing an action workout to ultimately design a future state.

If value stream mapping and waste identification is new to the organization, distributing the examples of the types of waste ahead of the event may be helpful. During the event, evaluate the team’s understanding of the 8 types of waste. Once the team understands what signifies waste, the next phase of value stream mapping is to identify the waste on the value stream map.

It is important that each representative in the room have an equal say at identifying the waste. Therefore, time needs to be allotted to brainstorm silently, with a minimum of 10 minutes for the team to reflect. Post-it notes and markers work wonders to capture the information quickly from the team. When the time is finished, each person should present their waste items to the group. With post-it notes, the speaker can physically place the waste item onto the value stream map to indicate where the waste exists in the process. Sharing continues until each person has shared.

Once the waste is identified, the team will see themes of similar waste. These can be grouped into categories and should be quantified in terms of time or cost. For example, if the average time it takes to collect payment from a customer is 80 days, then the waste is 80 days’ worth of non-value add cycle time. Take pictures of the waste items as they exist in the process map for future reference and to document digitally later.

Before progressing into the proposed solution stage, it may be helpful to consolidate the grouped waste and place them on a blank wall to facilitate brainstorming and creating solutions to the wastes.

In the next two newsletters, we’ll talk about how different Industry 4.0 technologies can be used to reduce or eliminate waste and design the future state.

Tech Spotlight: Process Mining

Process mining is an innovative technology that bridges the gap between traditional process analysis and data science. At its core, process mining involves extracting valuable insights from event logs readily available in the information systems of organizations. By meticulously analyzing this digital footprint, process mining software reconstructs a virtual map of business processes as they are actually performed. This allows organizations to peel back layers of their operations, revealing the reality beneath the surface of how business processes unfold, the variance from intended workflows, and the inefficiencies that may be dragging them down.

Leading the charge in the realm of process mining software is Celonis. As a frontrunner, Celonis has effectively democratized the power of process analysis, offering a platform that not only maps out processes in real time but also prescribes actionable intelligence for process improvement. Celonis’ prowess lies in its ability to pinpoint process deviations, discover the root causes of inefficiencies, and recommend improvements. The platform stands out by providing a clear visualization of process flows and benchmarks against industry standards, thus equipping businesses with the insights to streamline operations and enhance performance.

For the technically inclined, there is also the option to delve into process mining through various Python libraries. Python, with its expansive ecosystem, offers libraries such as PM4Py and Process Mining for Python (pm4py), which furnish users with the tools to perform process mining from the comfort of their coding environment. These libraries allow for a more hands-on approach, giving users the flexibility to tailor process mining to their specific needs and integrate these insights directly into their existing systems and workflows. With these tools, the power to mine processes, understand them, and reform them lies directly in the hands of those who manage them every day, making for a potent combination of expert knowledge and cutting-edge technology.

In the relentless pursuit of operational excellence, process mining stands out as a beacon, guiding organizations to discover the latent potential within their processes. Whether through sophisticated platforms like Celonis or through the adaptability of Python libraries, process mining is a tool of modern alchemy, turning raw data into the gold of improved efficiency and performance.

Community Conversation

Do you have other questions do you have that I can answer here? Answer in the comments or hit me up on LinkedIn.

Thank you for being a part of our journey. If you’ve found value in our conversation, please consider sharing this newsletter with others who might benefit and contribute.

Until next time, thank you for your support and curiosity.

— Sara 🙋‍♀️

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Erie, PA 16415, USA

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