Improving the Order Entry Process with Lean Six Sigma

This post has already been read 3219 times!
0 Flares Twitter 0 Facebook 0 0 Flares ×

More companies find themselves involved in a form of complex manufacturing. This has always been the case within the printing industry. Almost each new job is a bit unique and requires clear instructions, set up, quality control and customer approval. Indeed, perhaps the most expensive cost of doing business is rework. If there is a mistake in any of those processes, the company usually eats the costs. And those costs have a triple whammy: labor and materials are charged for the first run. If reword is required, there is the labor and material costs of the subsequent run; and there is the lost opportunity cost of now using the rework labor and materials for a new billing opportunity. Fortunately, Lean Six Sigma processes can help reduce and even eliminate complex manufacturing re-work. The following is a case study how a printing company changed its processes to address the order entry process which is usually the cause for much of the cost of re-work.

Needless to say, the mitigation of the order taking problems starts with the recognition that there is a problem. Once that hurdle has been passed, a team is formed to address the challenge of making needed changes. Once formed, the team identifies specific objectives to be accomplished through change.  Typically with the order entry process, there are four objectives attached to this task:

1) Getting it right the first time. It’s a good idea to set realistic goals and ramp them up as improvements become process. For example, reduce first time errors by 33% and improve this goal over time.

2) Reduce delays. Track the process with time sheets. Set realistic goals for progressive reduction.

3) Gather root cause data for causes of delays.

4) Reduce total Rework costs over specific periods.

Needless to say, rework labor and materials are usually not tracked and this is the main strategy to reduce this insidious and profit killing malady common to all one-off “job shops.”

Yet another round of data collection. In their initial value stream mapping effort the team should pay special attention to where they think the errors were occurring. Indeed, most people will have an anecdotal idea of what is causing the problems but the root causes my not be the same as the symptoms.  Much more quantifiable data is normally needed so that the true root causes could be isolated for corrective actions with the right process metrics. After some brainstorming, the team will decide to set up a data collection method to capture root cause information for each order that was returned from any downstream step for corrections.

A spreadsheet should be created to tabulate the following information for each return for corrections:

  1. Date of the discovery of the problem.
  2. The identity of the sales representative who processed the original order.
  3. Which of their defined markets this was for (to identify any correlations)
  4. A description of the error – this is a short-hand code, such as “Numbering” or “Layout”, or “Finished Size”.
  5. Who made the error(s) – as there are hand-offs for some process steps that the sales representative does not do.
  6. Problem description detail – specifically what was wrong.
  7. A notation – about whether or not the information was provided correctly on the order form in the first place.  It turns out that there are normally four possible answers which were important to learn – including; No, Yes, Not Applicable, and “Not Clear”.

In the case study taken over a two-month period, there were 137 observations that were added to the worksheet from which the team was able to perform some initial Pareto analysis (this is also known as the 80-20 rule, where 20% of causes account for 80% of the effects).

Uncovering true root causes is the critical mission in this improvement project.  The team took great pains to make sure that everyone involved in the data collection understood this was not a witch hunt.  To avoid bias in collecting the information, the team members took the time to examine each reported incident carefully and perform a root cause investigation within 24 to 48 hours before the annotations were finalized in the spreadsheet.  People who participated were thanked and those who assisted were singled-out for a special “thanks” from management.

They tracked the error rate on new orders entered over the two-month period and determined their “first time right” rate as 71%.  Their short term objective was to improve this by one-third – or move it to at least 80% right in the first 60 days after implementing countermeasures.

They then did a Pareto evaluation of the data for the various categories – which uncovered some interesting facts.  First, 5 of the 16 sales representatives correlated to 64% of the problems reported.  This was not necessarily an indication these 5 people are under-performing, as one might conclude without more information.  Remember Pareto’s law correlates to sales output performance – 20% of your sales force usually accounts for 80% of your top line results.  This information was not that helpful.

The next cut at the data was to examine who it was who “made the error”.  Of the 12 individuals identified, 2 of the 12 names that popped-up accounted for 85% of the data.  Yes, you read that right – just 2 of 12.  Now, before you begin pounding your fist on your desk with a big “Eureka!” let’s not get too hasty.  I cannot count how many times I have seen management hear about this kind of data only to begin jumping up and down screaming about “firing the guilty” – and replacing them with someone new that gets better results.  Fortunately, cooler heads usually prevail.  You see, the two individuals who were apparently “making the mistake” really were largely dependent on the information presented to them to do their work.  They were the poor souls who had the dubious distinction of completing the order in the order entry system working from the various written inputs they got from the sales staff and others.  Therein lies the heart of the problem.

We continued the study of the data – examining the detailed reason for the errors.  Things got a lot messier right away.  There were 16 major classifications of the nature of the errors in detail.  Of these there was not a nice clean 80-20 breakdown.  Five of the sixteen major causes that were 5% or greater on summed-up to 36% of the data – a clear indication that there was not going to be any simple “silver bullet” cures for this team to pursue.  This was not particularly good news for the team!  We pressed on to another layer of analysis hoping to find more helpful data.

Finally – the golden nuggets were discovered – a weak procedure.  More often than not, when doing a detailed analysis of root cause in office or service processes we find that a weak procedure or policies is the ultimate root cause. This was again the case in this example.  The team examined the final category of data – the “Correct on Order Form” field.  This is where things got interesting.

First, 20% of errors had “Not Applicable” in this field.  This was very useful information to the team because it was uncovered that the all-important order entry form did not include enough information in the first place!  Basically what was realized is that important information was missing critical. Indeed, there were no “check points” in the work procedure that allowed for verification or in-process checks to validate orders.  Indeed, they were launching the orders out into the system hoping (by default) that someone would intuitively see the problem and correct it before any damage was done.  Last time I checked, letting 20% defects slip to the next step in a process with no formal detection or prevention method pretty much assures very poor results.

Taking a closer look at this 20% of the issues we spotted a relative few common issues recurring including missing proofs and artwork, missing signed letter of intent, wrong production method specified, sample not marked or attached, and no strip-up pulled.  The team discovered that relatively simple modifications to the order entry form and some additional training for the sales reps and order entry clerks to be able to identify these problems on the first pass allowed them to eliminate 75% of the errors due to these issues.

Next, the team looked at the records that had “no” correct information in the form which made up 26% of the total data.  What this means is that the order entry clerks had no chance up-front to enter the order correctly – as the input was wrong to start with.  In this case, we looked at the biggest issues which were: color issues (the biggest single issue) including description, size, separations, specifications, and art notes.  After that the big issues were layout, modified incorrectly, numbering issues, shipping, and size specifications.

This realization was a very important one that the sales reps did not understand what was required and were outright making the wrong decisions or forced to rely on guesswork.  Now that the team had isolated the specific recurring issues, the solution was to charter a sub-team to come up with a methodology to solve the problems.  This team included production personnel, the art department, the order entry clerks and the sales team.  They focused on the specific products that had errors and learned that by creating a visual guide book with examples and a simple-to-follow guideline for allowable color, traps, and die cut combinations on the various product lines.  By clearly delineating the possible conditions for 80% of the orders (and having special orders handled in a separate work flow), the team discovered several benefits.  First, the number of errors due to sales department mistakes plummeted.  Secondly, they were now much faster at processing the orders – which contributed to a shorter lead time (due to less time head-scratching or waiting for someone to answer questions).   Stress levels dropped dramatically – as the sales department was getting a lot of heat for mistakes.

Finally, the team looked at the remaining errors – the 54% where the information was “correct” in the form – but somehow things got messed up from thereon.  Examining these issues as a sub-Pareto uncovered a relative few recurring issues including: mistakes on artwork approval, mistakes on color descriptions & separations, layouts, and numbering, and quantities.  These 5 issues easily made up 80% of the mistakes made during order entry and later in the process.

The same team that worked on the “guide book” for the sales department also worked to improve the order entry form to make each of these types of information more specific and easier to understand in the forms.  They then created simple, easy-to-use visual work guidelines for the order entry folks so that it would be much easier to get it right every time versus having to work form memory.

The rest of the story.  If you are wondering how all this turned-out, well – it was a great success and served to encourage continued efforts.  Reprinted orders – their #1 profit loss issue – collapsed by more than 50% within weeks of the completion of the above work efforts.  The team continues collecting data on their orders that are returned and track their accuracy rate percentage (which last I checked was in the high nineties) routinely.

Based on what the team learned they later charted a project to go after the total lead time to get things through their art department after order acceptance.  They had great success – reducing the lead time from two to three weeks on average to less than 3 days on average. . .  but that’s another story.


Additional Reading

Operational Excellence for the Future of Manufacturing

Operationalize Your Strategic Plan Using Lean Six Sigma


If you liked this article, we'll be happy to send you one email a month to let you know the newest edition of the MetaOps/MetaExperts MegEzine has been published. Just fill the form below.