Quality in a Warehouse – Reimagining the Process

Working DollarOver the last 20+ years in and out of warehouses, it never ceases to amaze and horrify the quality department operations and the waste of resources spent in either making up for failed quality or haphazardly running quality programs, thinking they are a waste of time.  Even those companies who focus resources on quality never seem to understand the trifecta of operational integrity (Product in, Quality, Product Ship) that quality could bring to an organization.  The reality is simple, quality is either your focus, your company is growing, or quality is a nothing burger, and your company will eventually be purchased or bankrupted.  There is no third option; we need to be clear on this point; quality is this important!

In quality and part of many compliance requirements are counting to ensure inventory is correct.  The counts break down into three types:

  • The Hunt – goes by many names but generally requires a person to count everything in a specific inventory location, e.g., shelf, rack, drawer, bin, etc.
  • Cleaning Inventory – is almost always called a cycle count, and its main job is to take the errors found in the hunt and clean them up, resolving specific issues.
  • Correcting Inventory – goes by many names but is generally used as a specific action where research is required, combining the hunt and the cleaning to find particular errors, lost product, and specific SKU issues.

If the counts were ordered by a financial institution instead of the quality department, the processes for clearing the errors might differ; the names often vary.  Yet the categories are pretty generalized to cross industries and remain applicable to a general discussion on operational improvement and excellence.  Specific companies will change names and processes, but I affirm the categories are sufficiently described to be practical and applicable.

Money

A fact of life, inventory is expensive, counting that inventory burns blue money, and those funds are generally not recoupable!  When speaking about money, colors of money are critical to a discussion.  Unfortunately, too many business leaders are either too concerned with green money or not cognizant sufficiently of the other colors to see how they all play roles on the bottom line’s performance.

  • Blue Money: Potential Money.  For example, buy a hammer with green money and invest $20.00.  But, in the hands of an experienced tradesperson, that hammer is worth thousands of times this amount of money over the life of the hammer.  The inverse is also true; in the hands of an inexperienced tradesperson, the potential for loss of money is incalculable!
  • Red Money:   Specifically, debt where interest is owed, on top of the principal, and other cash outlays.
  • Black Money: Dead Money!  Dead money cannot earn interest, cannot be spent due to fear, and cannot be used anywhere.  For example, drop a $10.00 bill into the sofa, and that money is as dead as yesterday’s fish!  Worse, once found that capital is usually in someone else’s hands.
  • Green Money: Cash!  Plain ol’ greenbacks.  Be those digital dollars, actual paper money, change, etc.; this is money that can still be invested, spent, and transferred for products.  Generally representing the bottom line.

While other colors exist, the focus is on these four types specifically.  Let’s use an analogy here for a moment.  A warehouse company hires a person to count inventory (green money outlay).  That inventory person invests time to count inventory (blue money) errors in stock could be red, black, green money errors, based upon how the inventory problems are resolved.  If no inventory problems exist, only the blue money was spent potentially finding the mistakes.  However, if errors were made and inventory errors exist but were not found by the inventory counter, more potential money has been lost than green money.  There is a blue, green, red, or black money loss on top of the original investment to have the inventory counted.

There is an axiom pertinent to quality in every industry, “Burn enough blue money, and green money evaporates with no trace.”  Hence, if the quality people are burning too much potential money to find defects in inventory, green money (cash) will disappear off the bottom line without anyone ever knowing or tracking the loss.  This brings the conversation back to types of counts and the problems in quality operations.

Fundamentals of Reconnaissance

Anyone who has ever conducted reconnaissance will know and understand the connection between quality and inventory in a warehouse and reconnaissance.  For those not familiar, here are the fundamentals of reconnaissance.  Reconnaissance is all about observations and reporting, communicating and making decisions about intentions, forecasting, and deciphering to make the best decisions while passing relevant information to leaders.  Guess what; The same is true of quality departments, especially in warehouse inventory.  The seven fundamentals of reconnaissance are:

  1. Ensure continuous reconnaissance occurs
  2. Do not keep reconnaissance assets in reserve
  3. Orient on the reconnaissance objective
  4. Report information rapidly and accurately
  5. Retain freedom of maneuver
  6. Gain and maintain enemy contact
  7. Develop the situation rapidly

Essentially, in civilian speak, the fundamentals of reconnaissance boil down to initial observation, data collection, data analysis, response to data, and response assessment (evaluate actions with an eye to the improvement of response).  Repeating only for emphasis, every employee in a manufacturing or warehouse environment is part of the quality chain of events.  They need to know how their actions individually lead to group (business) success.  Case in point, a stock person stocks a bin with a product; if that bin is crammed full, the product is going to fall out, become damaged, and create problems for the next person to look at that inventory location.  If in a manufacturing environment, if stock feeding machines are not uniformly loaded into the machines, damage, injury, and death potential are maximized.Inventory Quotes Humor. QuotesGram

It is important to remember that this is part of the first step in reconnaissance, observing what is currently happening.  Observation is also part of the most basic type of count, the hunt.  Knowing what the inventory looks like, how to access that inventory, maneuvering on a production floor, personal safety, and equipment knowledge and safety are all part of properly observing, collecting, and reporting data.  A person I know once told me, “Keep throwing spaghetti at the wall until something sticks.”  What is not mentioned is the need to prepare the spaghetti so it will stick when thrown.  Observation is where preparation occurs, and the business skipping preparation will always fail to capture the data for analysis accurately.

Counting Inventory

The hunt represents the counts with the least return on investment and a need.  Hunting inventory errors is akin to hunting game only with a camera.  You might get good pictures, but hunting with a camera will not fill your belly if you are hungry.  Personally, I despise the hunt and have long advocated for these counts to be removed from the quality department’s count types or be redesigned to become more valuable.  Simply counting inventory for the sake of hoping to find an error is anathema to good business sense and propriety.Inventory Quotes Humor. QuotesGram

Remember, a paradox occurs when two items are compared, and at first glance, they are opposites, when in reality, and with consideration, the truth is revealed they are more closely related than they are opposing.  The same is true to counts that hunt for inventory defects and proper observation, providing why I despise the hunt counting.  Preparation is a prerequisite to revelation, knowing where the inventory is, how to maneuver in a warehouse, and reach the stock; all this and more are essential.  Yet, when counting inventory, I affirm there must be a better way than endlessly sending people out to count, hoping to find defects.

Some companies have mixed inventory hunting counts with shelf maintenance and bin cleaning defects.  Warehouse rash, trash, litter, dirt, and debris in a warehouse remain a significant safety issue and should be cleaned regularly.  However, if the stock person is not already cleaning and stocking bins and shelves properly, the quality assurance person sent out to hunt for defects will become demoralized and stop cleaning up after the stock handlers.  Whether those stock handlers are pickers, packers, pullers, stockers, etc., the title is less important than the role they play in quality for handling the inventory, keeping a steady strain on the cleanliness of the shelves, bins, and storage locations, and correctly placing the stock into the inventory locations.

Several colleagues who are part of the quality control group in warehouses express similar sentiments to the following: “My job in quality would be a lot easier if those stocking shelves and those pulling stock to ship would pay more attention to how they handle the stock and the inventory locations.”  To which my answer is always the same, “Are all your people aware of the role they play in quality?”  By the comments answering my question, it is fundamentally clear that there is a Grand Canyon-like chasm between those not officially in quality and those in other roles, and fixing the problem, and eliminating the useless hunt counts, is all part of bridging this chasm!The Crazy Work Related Moments (51 pics) - Izismile.com

Hunt counts do one thing valuably, they provide an innocuous way for quality people to learn the inventory and observe conditions generally, which sounds like two separate actions, but in reality, they are the same action.  That’s the entire value in hunt counts; these counts cannot clean inventory defects; they can only take a picture and report that picture to begin another warehouse process.  The frequency of errors in the inventory hunt process forms a view that reports how clean or dirty a warehouse’s inventory process is; but, this report can be related with greater accuracy without the hunt counts.  Unfortunately, because the data reported is shared in numerical values, individual bias in the statistical reporting tools can be manipulated and often is misrepresented by conscious or unconscious bias.

Hence, we can conclude that the hunt count by itself has little to no value (green money), is expensive (blue money), and will heavily influence the acquisition and maintenance of red, green, and black money.  What is a person to do?[2020's] Top 11 FAR CPA Exam Study Tips - Pass on Your 1st ...

Possible Solutions

Possible solutions are aptly named because no warehouse is exactly the same, no company is exactly the same, and the quality department mission will always differ from one business unit to another and between businesses that compete.  I admit I am heavily biased against hunt counts in the warehouse and manufacturing industries.  However, I am also heavily biased about removing something that works for something untried in the hopes it will replace a flawed system.  Thus, the solutions proposed remain possible solutions to initiate the spark to a future conversation and obtain input from smarter minds.

  1. Since the hunt counts are basic, and the roles of stockers and pullers are very similar to an initial role in quality where learning and observing inventory is a prerequisite, make the entry-level job for stockers/pullers/quality all the same position—cementing the need for everyone to play an active role in quality while also removing lines that separate.
  2. Fundamentally change the hunt count to focus not on inventory locations that appear clean but those in chaos. Chaos in an inventory location should be the primary focus for correction, not simply a mindset that everything will eventually be counted, so invest in useless counts to make work.  Hence a stocker or puller would approach an inventory location with problems and count that full location while cleaning and straightening that location and reporting that location as problematic for corrective remediation with the last person who visited that inventory location.
  3. Stocker/pullers will not be able to correct defective inventory; this is a Sarbanes-Oxley headache for compliance, but this is a good thing. A level two quality associate could then be dispatched to that newly cleaned, organized inventory location to perform inventory correcting actions, thus speeding the corrective inventory action and providing better data on associate activities.
  4. Part of reconnaissance is using data more wisely; this includes capturing data details, improving training, promoting quality as a mindset for every employee, and analyzing the data for specific corrective actions the business can initiate in inventory locations, shapes of packaging, and handling stock more efficiently to prevent damage. Follow the data path to root causes and act on correcting root causes.

Final Thoughts

Knowledge Check!Qualitative data is almost useless by itself.  Quantitative data is practically meaningless by itself.  Thus, operational reports must contain both types of data to provide a clear picture of events and be the most useful in improving decision-making.  More to the point, mixing both types of data individual bias and subconscious manipulation of the data is more difficult, thus mitigated.  Reconnaissance is all about communicating and capturing data for analysis.  Why should a business leader only have quantitative data to base decisions upon; hence the need to understand data and use data more wisely.  Never settle for only one type of data in a report, never settle for what has always worked in the past, and never allow business processes and procedures to live longer than 18 consecutive months without a full review and torture testing to check for better ways and means.  It cannot be emphasized enough, “If you do not try the impossible, you will never achieve the possible.”

© Copyright 2021 – M. Dave Salisbury
The author holds no claims for the art used herein, the pictures were obtained in the public domain, and the intellectual property belongs to those who created the images.  Quoted materials remain the property of the original author.

Using Data to Tell a Story

Bobblehead DollIn a conversation, it was discovered that a vital aspect of communication was missing. Data has a story to tell, but many never know the story or have never figured out how to tell the story properly.  Either way, the reports containing data fail to capture the audience’s attention, lose the audience, or fail, thus making the process of collecting, analyzing, and using the data useless!  Hence my aim here is to aid you in improving your storytelling power and, by doing so, increase the credibility and usefulness of your data to convince your audience.

We must bow to a truth; data does nothing, proves nothing, and cannot convince anyone.  All data can do is support a proposed course of action.  Hence, the data is reliant upon the story, and the story is reliant upon the data.  This principle cannot be stressed enough or more emphatically.  Let me illustrate.

Here is some data:

5184 0.83 4 5123 5 0 3 95 80 242 0
183 4 483 2.9 1 3666 1 17 1 2 0
2 0 705 15.32 2 11458 0 0 0 5054 760
35 1 493 28.8 4 10258 1 0 33 970 390
17 1 33 15.15 2 18811 3 0 40 32 5
2741 8 838 11.81 1 35843 15 298 64 239 104
0 0 1438 4.59 0 1582 0 0 0 22 1
1827 1 1011 18.5 0 40208 0 645 800 418 308
0 0 28 3.57 0 732 0 0 18 69 7

Use storytelling to present with power | Presentation GuruEven with column and row headers, this data is useless.  It is trying to tell a story, it is reporting symbols correctly, and the data is an accurate indication of work occurring; but, where is the value of the data?  What can you surmise from this information?  What trends are happening?  Are the trends good or ill?  What good will having this data do for you without an explanation about each row, each column, and a basic understanding of how each data point was collated?  How is the data read, right to left, top to bottom, left to right, bottom to top, or some other way?  You have all the data you need in this table to make several decisions, plotting multiple courses of action, but how do you know what to do?

Hence the truth; data does nothing, proves nothing, and cannot convince anyone.  All data can do is support a proposed course of action. Therefore, the data is reliant upon the story, and the story is reliant upon the data.  This principle cannot be stressed enough or more emphatically.  When in doubt, always return to this truth about data.  If the data is incoherent, sometimes the story can make up the slack and convince.  If the story is confusing, the data cannot take up the slack.  Worse, if the data and the story are incoherent, time and resources were wasted.

Storytelling Stock Photography Clip Art - Children Telling Stories , Free Transparent Clipart ...I mentioned storytelling with data, and the person I was talking to seemed perplexed.  No, the story does not begin, “Once upon a time, in a far off land, there lived….”  Nor should the story begin, “Column A is aggregate data pulled from …”  What we are discussing is finding a medium between these two extremes where the data’s story can begin, without compromising the veracity, but still keeping the audience’s attention.  Believe it or not, storytelling begins with planning, and planning starts with selecting the audience.

Planning: Selecting the Audience

The first question every writer must address is, “Who is your primary audience?”  For children’s books, the answer is simple, choose an age group of children, and voila, you have a primary audience.  For adults, the decision tree is a little more complicated.  For example, is your primary audience expert-level professionals, graduate students, baccalaureate students, high school graduates, or seasoned professionals without a degree?  Is the primary audience vendors, stakeholders, employees, engineers, a mix of all, none of the above, politicians, lawyers, judges, doctors, etc.?  Until you know the audience, you will not know how to address the audience, what language to use, the grammar, the syntax, and the vocabulary.  All of which require planning to capture the audience’s attention and tell the story successfully.Non Sequitur - Plasticity of Language

I choose to write for general audiences, using plain language principles, for my intended audience are experienced professionals, adults, and general people.  I make this choice not for ease of writing but for the enjoyment of communicating.  In this blog writing forum, it is better to be plain and speak simply than trying and peg a specific audience.  I presume you already know many principles, and my job is to flush those principles into current memory and help build on what you know.  I also suspect you know how to use a search engine to gain additional information if you so desire.Five tips for effective data storytelling

Hence, making choices about your primary audience is critical to how you address your audience.  When using data to convince the audience to take action, you will also need to plan how and when to use the data.  But, before you use the data, you must tell the audience how the data was gathered, how it was analyzed, and why these are important to know.

Planning: Data – Gathering, Analyzing, Presenting

Consider the table above; without dollar signs, how can you tell which fields represent dollars?  Without percentages, how many fields represent a percentage?  The presentation might not be everything in data storytelling, but it is definitely a significant part of the task.  Hence, there should be a plan to present the data long before the first pieces of data are gathered or analyzed. Primarily if the data consists mainly of numbers, for numbers are merely symbols and can mean anything!

WHY STORYTELLING IS THE NEW HOT BUZZWORD! - Advanced Marketing StrategiesGathering data is all about relating how the numbers originated.  Not created, originated; creation implies the numbers were made up; origin implies the numbers existed and were discovered.  Choose your descriptive words carefully in storytelling, especially where numbers originate.  Each number is a symbol representing something; hence, each number requires an explanation of its origin story and value to the results.  Never forget, if you have 100 number columns and 200 rows, you will need at a minimum 100 explanations and origin stories.

Analyzation, what tools helped you crunch the numbers?  Any regression, statistical analysis, any bell curves, remember, each instrument used will need an origin story and purpose for use.  Each tool will have a preferred method for presentation, does the presentation clash with other numbers being represented?  Are tables too complex due to the number symbols and presentation demands?  When writing the origin story, the clarity in telling the story is discovered in the origin stories, so make sure you understand how the data is to be presented.

Planning: Presentation

Business storytellingHow will the final report be given, PowerPoint, Prezi, Adobe PDF, MS Word, Email, Conference Room deliverable, Web Chat, etc.?  The presentation of the final product dictates what goes into the story, how graphs and charts are used, the language employed, body language in presenting the data, and a host of other decisions.  Yet, too often, this planning step is neglected to the end of the process.  Then massive changes have to be made to the product on the fly, creating confusion, wasting resources, and creating stress and problems unnecessarily.  Before the story’s first words are told, decide how the final product will be presented, right alongside the primary audience, and how the product will be delivered, it will save time and resources!

Convince – Don’t ProveMediocrity Joke

You intend to convince an audience that the proposed course of action is the best course of action.  Data can never prove anything!  The story must convince a course of action, and a decision tree is appropriate; thus, the storyteller must tell the audience why!  Why is this course of action the best?  Why is another method of effort not to be considered?  Why should other courses of action not be undertaken or explored?  Why?  Why?  Why?  When in doubt, circle back to the unalterable truth discussed in the beginning, data does nothing, proves nothing, and by itself cannot convince anyone of anything.

Convincing the audience is the storyteller’s job.  The data is there to assist, provided it has been properly introduced, correctly presented, and described with aplomb.  Then, the data can be an effective tool to carry the day and aid in convincing decision-makers (people) that a course of action is correct.  Failure of the story, failure to explain the data, and your efforts are wasted and the proposed course of action not taken seriously.

Knowledge Check!Please note, these are the basics of using data to tell a story.  Experience and time are harsh taskmasters, and they will teach advanced courses in using data to tell a story.  However, understanding these basics will keep you from learning through failure while being taught the advanced skills in storytelling using data.  One final aspect of planning involves language, especially grammar and the technical aspects of writing.  If you do not know the language, find a native speaker to review the presentation before publication and delivery.  Get to know tools available to aid in proper spelling, grammar, and punctuation.  Your diligence in learning technical skills in communicating will pay dividends for good or ill.  Invest wisely!

© 2021 M. Dave Salisbury
All Rights Reserved
The images used herein were obtained in the public domain; this author holds no copyright to the images displayed.