Steve Beeler

You have a goal…I have a way to get you there.

Plan-Do-Check-Act

5S

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Lean thinking is not just for factories. 5S is an easy to implement process to organize any workplace. With a little pro bono coaching, my new friends at Holland Physical Therapy have completed their first 5S project. Here’s how we did it:

5S

We started out with this quick one-page lesson in 5S, the seven wastes, and Plan-Do-Check-Act. As you might expect from its name, 5S is a five-step process. It is a pathway to a clean, uncluttered, organized workplace reducing waste and improving productivity:

1) Sort
2) Set In Order
3) Shine
4) Standardize
5) Sustain

Introducing the seven wastes (defects, overproduction, transportation, waiting, inventory, motion, and over processing) sharpened the focus on waste reduction. Introducing the Plan-Do-Check-Act continuous improvement cycle reinforced 5S as on on-going, every day commitment, not a one-time event.

Next, with a shared understanding of terminology and principles, we did a quick walk through of the clinic. Five of the seven wastes seemed to apply the clinic’s lack of organization…we could not find examples of overproduction and over processing. Cleanliness was not an issue. However, the Holland Physical Therapy team was greatly concerned about not finding something when needed and the time wasted looking for it.

The cable column was selected as the initial application area.

We first sorted through the area and set aside what was not needed. Then we organized and labeled everything used at the cable column. Masking tape was used to temporarily identify parking spots for the many accessories. The team agreed to sustain the cable column 5S through an end of day tidy up: anything out of place would be put back to where it belongs. After a day or so, the team assessed the area, made improvements, and moved onto the next 5S application area. P-D-C-A.

5S Group Photo

Through 5S, the team at Holland Physical Therapy is on their way to better utilize their space and easily find what they need when they need it.

Spaghetti Diagram

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spaghetti diagram

A spaghetti diagram provides a visualization of the flow of material and/or people through a manufacturing or business process. A spaghetti diagram is primarily used to visualize transport and motion, two of the seven wastes in lean thinking.

A spaghetti diagram is easy to construct. The most important input is process knowledge. Assembling a small team is a great idea, especially if the process of interest spans multiple departments. Not much else is needed: a plant layout or schematic, colored pencils or felt tip pens, and maybe some sticky notes.

Start at the first step of the process. Where does this material go next? What happens when it gets there? Be sure to capture decision points. For example, defects go to a rework station before moving to the next process step. I like to use red for transport (e.g., forklift moves or conveyors) and blue for motion (e.g., picking, placing, stacking). Use whatever colors work best to visualize your process.

A spaghetti diagram is a qualitative tool. To make the analysis more quantitative, you can measure distances, take process times, count forklift moves, etc. There are no rules, use whatever metrics best quantify your process.

After establishing a baseline of the actual process flow, use a future state spaghetti diagram to visualize the benefits of plant and office rearrangements, capital investments in new or additional equipment, etc.

Here is a case study. A manufacturer was ramping up production of a new product line. Additional capacity was planned but where are the best locations for the new equipment?

Current State Spaghetti Diagram

The team was very surprised by the amount of transport in the current state spaghetti diagram. There were long red lines everywhere…a plate of spaghetti indeed!

Alt #3a Spaghetti Diagram

Future state spaghetti diagrams were developed for layout alternatives with the new mold cells and assembly cell, the two planned capacity investments. It was quickly seen that an additional curing oven (not in the capacity plan) would dramatically reduce transport distances and forklift moves: the red lines are fewer and shorter.

A spaghetti diagram is a simple and effective method to analyze and compare plant layouts. Put one in your Plan-Do-Check-Act tool continuous improvement kit.

 

 

 

Manufacturing Plan Verification

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Our dynamic economy is characterized by constant change. New products are brought to market, achieve commercial success, and then are made obsolete by something better, faster, cheaper. An essential element in successful product creation is manufacturing plan verification.

A manufacturing plan is a comprehensive compilation of all the facts, figures, and assumptions around making something of commercial value. A plant layout is necessary but not sufficient. How fast must each machine operate? What are the quality requirements? What are the optimum inventory levels? What is the annual volume for these things? How much money is available for investment? What is the unit cost target?

manufacturing plan verification

A manufacturing plan describes a long chain of events from order to delivery. It is not just enough to get each link in the chain to work. The entire system of links must work in harmony to deliver business plan operational and financial metrics. This is not easy to accomplish. Manufacturing plan verification manages this risk.

A robust manufacturing plan starts with business plan objectives which are cascaded down into plant department performance targets through a high-level discrete event simulation model. Next, department performance targets are cascaded down into line level performance targets through a more detailed, line-level discrete event simulation model.

 

Manufacturing Plan Verification

With performance targets in hand, manufacturing teams can confidently do the detailed processing of their link in the chain. Systemic risk has been minimized if not eliminated totally: if each link meets its target, then the chain will work as expected. Manufacturing teams may be working remotely but they are not working in isolation…they are connected through the line-level discrete event simulation.

Before program approval, each manufacturing team reports back on their performance predictions for its link in the chain. These values replace the targets in an integrated discrete event simulation model of the entire manufacturing process. The manufacturing plan is verified when this integrated model meets or exceeds business plan objectives.

In addition to verifying production flows through discrete event simulation, material handling simulation models are often utilized prior to program approval to predict and optimize fork lift requirements, traffic flows and congestion, and indirect labor requirements.

Does simulation lead manufacturing planning or vice versa? A little of both, in a collaborative Plan-Do-Check-Act continuous improvement cycle.

Simulation Output Reports

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Discrete Event Simulation is a portal to the future: find constraints, test strategies to break them, improve performance to the goal, maximize investment returns, and reduce risk. Shown below are three powerful simulation output reports.

Simulation Output Reports

Time in State. Time in state simulation output reports are especially useful in finding the constraint in the process. Before the constraint, machine elements are generally blocked (blue) more than they are starved. After the constraint, they are starved (yellow) more than they are blocked. The machine element with the most uptime (green) is likely the constraint.  Knowledge of the constraint’s location is a key factor in improving the process. Focus on the constraint for opportunities to increase throughput. Look to non-constraints for opportunities to reduce operating costs.

Simulation Output Reports

Volume Histogram. The volume histogram is the first to two methods used to validate a simulation model against its real-world process. How production counts vary over time is an important metric…the less uncertainty the better.  The volume histogram provides a qualitative comparison of the hour-to-hour variation in volume throughput.

Simulation Output Reports

X-bar & R Chart. The production count X-bar & R chart provides a more quantitative comparison between the simulation and the real-world process. Statistical process control charts not only quantify the magnitude of the common cause process variation but also identify special cause events. Characterizing the output variation as common cause vs special cause is an important factor in validating the simulation model. It is difficult if not impossible to simulate special cause events. That is because discrete event simulation “engines” utilize constant probability random number streams. If the real-world process variation is being driven by non-random events, then the root causes of the special cause events will have to be removed before the simulation what-if results will predict future performance.

These three simulation output reports are the foundation for a Plan-Do-Check-Act continuous improvement of the simulated manufacturing or business process. Simulate, validate, and experiment. A robust solution will follow.