Using the Tools in Your Performance Excellence Toolbox: Part 17 Benchmarking

Using the Tools in Your Performance Excellence Toolbox: Part 17 Benchmarking

This is the seventeenth in a series of posts on using performance excellence tools.  Benchmarking is a tool that can really help you determine the best solution to your problem.  It can point you to best/better practices.

First let’s understand what is meant by benchmarking.  “Benchmarking is the process of comparing one’s business processes and performance metrics to industry bests or best practices from other industries.”, http://en.wikipedia.org/wiki/Benchmarking

The easiest way to do this is to start by saying what it is not.

• It is not a bogey or target that you set to measure internal progress. To illustrate this, I’ll relate a story from my experience as a consultant.  Consulting for a commercial records center, I asked the owner if he had benchmarks for key operations.  He said that he did.  The company had financial benchmarks to gage progress. That is not the type of benchmarking used in process improvement.
• It is not an excuse to make a field trip.  When I was first introduced to benchmarking in the 1980s, the division that I was with had a rough time understanding how to properly use benchmarking.  Immediately people wanted to get in a car or jump on a plane and go talk to the “experts” in the field.  That was a huge waste of resources.
• It is not even identifying the best in your business and talking to them.
• It is not for the faint of heart, untrained or impatient.

Successful benchmarking requires the following steps:

• Set aside a good amount of time to complete the process.  At a minimum you are going to spend 40 hours and for big processes with big problems you can be at it for months.
• Clearly identified and documented gaps or inadequacies in the process you want to benchmark.  To do this you need to:
  • Analyze your process
  • Identify the errors
  • Identify potential solutions (this can be done before or after benchmarking)
  • Identify potential benchmarking partners.  This is the most critical point.  You are looking for the best practice for the process you are working on.  This may come from an unexpected place.  Too often people want to benchmark the best in their business not the best in the process.  Let’s return to the commercial records center for an example.

They were trying to improve their delivery process.  When I explained the need to benchmark, the owner immediately said let’s benchmark the largest in the business.  That was the wrong answer.  They needed to benchmark the best at delivery not the best commercial record center with the best delivery process.  If you are going to improve your delivery process you look to the organizations where that is a core function.  They needed to benchmark companies like FedEx or UPS.

You have identified your potential benchmarking partners.  Now comes the hard part – enlisting them.  Benchmarking partners are not always easy to enlist.  For some the reason they are the best is held as a trade secret or their key competitive advantage.  Put yourself in their position.  How willing would you be to share what has made you the best of breed?

You need to be able to answer their WIIFM (what’s in it for me).  Some potential partners are open to sharing because what you are asking does not pose a threat to them.  You may not be a competitor in their market or business.

Others may be willing if you sign a nondisclosure agreement with them.

One of the best ways to enlist a partner is to share with them your results or cross benchmark with them.

There is much more to benchmarking.  I will cover it in upcoming posts.

 

Using the Tools in Your Performance Excellence Toolbox: Part 16 Developing Your Plans

Using the Tools in Your Performance Excellence Toolbox: Part 16 Developing Your Plans

This is the sixteenth in a series of posts on using performance excellence tools. You have narrowed your potential solution to hopefully one or at most three.  Your next step is to develop the plans required to test your potential solutions.

First did you answer the questions in my post – Using the Tools in Your Performance Excellence Toolbox: Part 15 Selecting Your Potential Solution – satisfactorily for each of the potential solutions?

• Do you have the required resources to implement the solution?
• Does it fit your business culture?
• Do you have the political backing needed to implement

If you read the standard works on Lean and Six Sigma on planning your solutions for testing, you will note that it is extremely heavily tilted to manufacturing particularly statistical analysis of the workflow.

For example control charts are generally set up to measure large amounts of data points, e.g. manufacturing 100 widgets an hour over 10 shifts a week or processing thousands insurance claims.

To use a control chart for low volume transactions requires some tweaking of design to help you track the performance of the process.  You have baseline data from your earlier stages post to start the measurement process.  Here is an example of a simple control chart using a MS Excel template.

Using a control chart

1. Determine what a defect (imperfection) is.  A sample can have several defects.  In this example a defect could be:

• Smudges
• Misaligned page
• Incomplete print/missing letters

2. Determine how big a sample size you are going to use.  Do you want to do a 100% sampling or a representative sampling?  The more you sample, the more accurate your results will be.  But the more it will cost you to do the research.

3. Determine the frequency of sampling.  How often you do you want to sample.

• Example for points 2 and 3.  You want to sample one item in 10 on each line.  Or one item in 10 on every third line.

4. Record the number of defects per sample.

5. The chart automatically calculates the mean (average) for all the samples in the daily sample period.

6. It then calculates the mean for all the daily means.

7. The next four steps calculate the various deviations from the norm.

Many of the tools you can use during the planning step have already been discussed in previous posts.  These include:

• Project Charter
• Gantt Chart
• Pareto Chart
• PDCA Cycle

Review your project charter.

If you did not create a Gantt Chart earlier, you need to do so now.  If you have one, it is time to update it to reflect the improvement plans steps.

Using the Tools in Your Performance Excellence Toolbox: Part 6 Basics of Data Analysis – Continued

Using the Tools in Your Performance Excellence Toolbox: Part 6

Basics of Data Analysis – Continued

This is the sixth in a series of posts on using performance excellence tools.  It continues the discussion on the basics of data analysis.

Figure 1 shows the conversion of the data into a Histogram with a Pareto Chart output.  A short summary of the purpose of a Histogram is the data distribution shown by rectangular columns that represent the frequency of the data points collected into “bins”.  A Histograms displayed as Pareto Charts, provides a distribution in order of frequency from highest to lowest.  It also shows the percentage of each bin.

Figure 1 Histogram as Pareto Chart output

Figure 2 shows a standard Histogram where data is distributed in ascending order of the value of the “bins” vs. the percentage of the number of data points or occurrences.

 

Figure 2 Histogram

This starts to show some of the differences in more detail.  You notice that 60% of the clerks process an average of 16 or less invoices a day.  You need to look deeper into the data.

Try to answer why there is such a delta between the high and the low.  You will have to dig into the raw data more.  Look at each clerks’ data as a self-contained unit.  When you compare Bob to Liz (Figure 3) (Bob processes the most, Liz processes a little below the average) you see that Bob asks Liz for help every time he has an invoice from supplier D.  This interrupts Liz and tacks on additional time to her average processing time.  It does not add to Bob’s time since it is not counted as an interruption.

Figure 3 Comparison of April 5’s data

 But this doesn’t identify the root cause of the problem.  You now need to find out why Bob interrupts Liz when he has invoices from supplier D.  You can take to flights of fancy if you want (but it will be of little value to you in the end), e.g. maybe Bob has a thing for Liz.

You then do the same type of analysis with the other clerks to see if a pattern arises.  The results show that Bob asks for help far more than anyone else and he asks for the most help from the senior clerks who also happen to process the lower numbers of invoices.

To get to the root cause(s) use the 5 Whys.

The 5 Whys is an extremely simple tool to use that is also very effective. You state the problem and then proceed to ask why is this a problem?  Answer that and ask why is this a problem, and so on.   It is like peeling an onion.

Pose problem: Bob requires help with Supplier D invoices thus interrupting other clerks.

1. Why? Bob needs to learn the intricacy of dealing with all the suppliers.
2. Why? Bob has been targeted as a potential supervisor and is receiving OTJ training.
3. Why? Management wants potential supervisors to be taught by the SMEs.
4. Why? They think it saves them training expenses.
5. Why? No direct cash outlay for training.

Another result of your data analysis identified that Supplier D’s invoices required more time to process than the other vendors almost uniformly regardless of which clerk processed them (Figure 4).

Figure 4 Average time to process an invoice by supplier

Pose problem: Supplier D’s invoices require more time to process.

1. Why? Supplier D invoices are lengthy and more complicated than the other suppliers.
2. Why? Supplier D submits multiple separate invoices but requires payment by one check.
3. Why? It is Supplier D’s standard policy and Bob’s company’s procurement doesn’t push back on this.
4. Why? Supplier D is the sole source of this material for the company.
5. Why? Procurement doesn’t want to risk losing their sole supplier.

You have found two potential root causes.

Using the Tools in Your Performance Excellence Toolbox: Part 5 Basics of Data Analysis

This is the fifth in a series of posts on using performance excellence tools.  It covers the basics of data analysis.

You have gathered your data and you are saying, I have all this stuff, now what do I do with it? It doesn’t make any sense to me.  Your goal is to turn the data into information that will lead you to actionable opportunities for improving your process’ performance.  You are looking for the root cause of the problem.

Unlike an attorney who should never ask a question in court without already knowing the answer, if you think you know the answer, you might not be asking the right questions.  But if you want to draw the curve then plot the data to meet it, you are wasting time.  The goal of the analysis process is to start to understand what is happening within your process.

Note: It is perfectly fine to gather data to verify an assumption that you have or to make certain that your process is working within specs.

There are many analytical tools available to you.  The important thing to note is that the tools only get you part way through your analysis.  You need to interact with the data, discuss it with others.  Don’t get upset if you go down a few dead-end streets.

Using the accounts payable invoice model we started with in the earlier post we begin to analyze the results of the data gathering.  There are 10 accounts payable clerks each tracked their time for 5 weeks.  This gives us 50 days of data to work with.

What do we want to learn from the data?  Let’s say that one of your assumptions is that some clerks markedly process more invoices than others.  To confirm this, some basic questions you may want answered are how many invoices does each clerk process in a day?  How long does it take to process an invoice?  How do clerks compare to each other?

The first thing you have to do is make the data manageable.  You have 50 data sheets. Each sheet has roughly between 100 and 300 data points.  That gives you a potential of 5,000 to 15,000 data points.  You need to get them into a usable format.

Figure 1 is an aggregate of the data needed to determine each clerk’s daily average of processed invoices.

             Figure 1 Aggregate Daily Average of Processed Invoices

If you tried to plot this you would wind up with a graph that looks like Figure 2.

Figure 2 A Confusing Way to look at the Data

A better way is to get at the data is to look at each clerk’s daily numbers as an average and compare them to each other.  Figure 3 shows Bob’s average daily output and includes the median and mean times which in this case happen to be the same at 18 per day.

Figure 3 Individual Clerk’s Average Number of Invoices Processed

You calculate the daily average for each clerk.  Divide the lowest number by the highest number and put it in percentages.  This gives you 57%.  Invert that and you find that there is a 43% difference in productivity between the clerk who processes the most and the one who processes the least.   But when you calculate the Mean (16), and the Median (15) you start to see that there is more to this than a simple delta between high and low.

 

Figure 4 shows the data plot on a bar chart.

Figure 4 Bar Chart Comparing Clerks’ Output

If you convert the data into a histogram with a Pareto Chart output (Figure 5), you see there is more to the story.  So you need to dig deeper which we will do in the next post.

Figure 5 Histogram

Using the Tools in Your Performance Excellence Toolbox: Part 4 Data Gathering

Using the Tools in Your Performance Excellence Toolbox: Part 4
Gathering Data

This is the fourth in a series of posts on using performance excellence tools. It covers the reasons for and basic methodologies designed to help you gather baseline data.
Why gather data? There are numerous phrases to explain why:
• Data is king!
• “What gets measured gets done” (attributed to numerous individuals)
• “Without a standard, there is no logical basis for making a decision or taking action” Joseph Juran
• And my favorite – “In God we trust, all others bring data.” W. Edwards Deming
But the basic reason is that without a baseline to work from you do not know if or how much you are improving your process. And without data you can’t determine your baseline.

What is data? “1. factual information (as measurements or statistics) used as a basis for reasoning, discussion, or calculation …” http://www.merriam-webster.com/dictionary/data

Data and information and occasionally even knowledge are used interchangeably, though incorrectly. You are gathering data not information to set the baseline.

So how do they differ? Data is the raw facts about the process (symbols such as numbers). Information is an analysis and configuration of the data (often stated to be achieved by human interaction on data) that makes it useful. Knowledge is the combination and reflection on the information to answer the how of a question.

I have come across some people who use surveys, interviews, focus groups, etc. to “gather data.” To me this is VOC not data gathering since there is very little if any raw facts gathered. Oh well, to each his own.

Now that we have defined data, how do we collect it? First what type of data are you looking for? It must be quantitative in nature. Quantitative data:
 Deals with numbers
 Can be measured
 Examples: Length, height, area, volume, weight, speed, time, temperature, humidity, sound levels, cost, members, ages, etc.

In addition to quantitative data you can use Qualitative (note in addition to not in place of). Qualitative data:
 Deals with descriptions
 Can be observed but not measured
 Examples: colors, textures, smells, tastes, appearance, beauty, etc.

Three things to remember when gathering data:

• KISS it (Keep It Simple Stupid) unless it is absolutely necessary, e.g. high complex manufacturing process), keep the data gathering as simple as possible
• 3 bears rule
1. Not too little
2. Not too much
3. Just enough

• Verify it – is it trustworthy, reliable, repeatable

You need to be aware that you don’t become infected with the paralysis by analysis virus. The first signs appear during data gathering and include the irresistible urge to just get one more piece of data.

The basic way to gather data is by using a data gathering worksheet. These are customized to your specific needs. I generally use an Excel Workbook for by data gathering. This allows a great deal of flexibility and you can add as many worksheets as you need to tailor your responses.

For example suppose you wanted to collect data on the time it takes the Accounts Payable staff of 10 to process invoices.

Determine the sample size and duration (number of cycles).
• Sample size – 10 employees
• Duration – 5 weeks (each day is a cycle)

You set your data point requirements. In our case they are:
• Employee
• Date
• Supplier
• Start time for each invoice
• End time for each invoice
• Start time for each interruption
• End time for each interruption
• Type of interruption

Figure 1 Data Gathering Worksheet

Each employee will complete a worksheet each day during the data gathering cycle.