News & Journal Articles

The 2026 edition of MLT-2 is now available for download.

This version includes support for a new custom Navy ERP T-code (ZRMWM0003) which provides all data fields available in the material label.

After a few years of MLT-2 enjoying a well-deserved retirement, I received a query regarding a user’s inability to use the MLT-2 MS Access version in 64-bit systems. My first impression was to be flattered that someone was still using it. Then I started looking into the problem.

I had not looked at the MS Access VBA code in years, so I first responded that everything should work, since I am able to run the program just fine in my 64-bit Windows 10 machine. It wasn’t until later that I realized my error. Despite Microsoft stating the MS Office installs in 64-bit by default, I was running the 32-bit version of Office in my MS Access development machine. So, after correcting that, I was able to replicate the 64-bit error.

After a few hours of tweaking the code to VBA 7 and testing, I had a new version of MLT-2. Along the way, I decided to also ditch the whole subscription model, but keep the expiration date on the releases for ease of support. This way, outdated versions don’t linger in the wild.

There are no new features, but the experience of getting back to doing some development, got me motivated to consider some enhancements. So, let me know what you think. The download links for the refreshed 32-bit and 64-bit versions are below.

I have been asked about statistical inventory sampling so many times that I felt that I should share my thoughts in an article (or many). In simple terms, statistical sampling is the selection of a subset of items to represent a population and it is a very useful tool for establishing inventory accuracy.

In order for the process to be considered representative, there are a few fundamental rules:

• Each item in the population must have an equal chance of being selected (i.e. no targetting or cherry-picking).
• The selection of items to sample must be random and free of bias.

The following are some common terms you would encounter: confidence level, sample size, population size, margin of error. The vast majority of the time, the confidence level and margin of error will be dictated to us, as this determines the precision and robustness of our results. A very common set of parameters typical in DoD supply chain policy is 95% confidence level with a +/- 2.5% margin of error.

If talking about confidence levels and margins of error is still fuzzy, fear not, there are tools that make the whole thing practically plug-and-play. I even developed my own spreadsheet calculator in one of my articles on my website (link here).

Note: if you, like me, are “of a certain age” in Navy Logistics terms, you might remember a program called STATMAN that for many years was the Navy’s standard tool for these calculations when dealing with inventory accuracy.

In order to conduct what is called “Simple Random Sampling” there are only a few computations that we need to make: 1) the sample size, and 2) the accuracy results (usually a percentage or ratio). We were going to compute the accuracy anyway regardless of method, so that leaves the sample size as the only new thing we need to figure out. To get the sample size, simply plug the following parameters into the spreadsheet calculator linked in the previous paragraph (or an online tool like this one): 1) population size, 2) confidence level, and 3) margin of error. This will give you the minimum sample size that needs to be collected in order to satisfy the parameters. Select that number of items at random from the population – you can do this manually or using most spreadsheet programs. Then conduct the inventory and compute the accuracy percentage. Whatever percentage you get (e.g. 93%) would be expressed in terms of a “confidence interval”, which is nothing more than stating the margin of error with our results (e.g. 93% +/- 2.5%). That’s it!

Now, like anything, it can get much more complicated. There are sampling strategies that involve stratification, different allocation methods, etc. that are well beyond the scope of this article. Therefore, we will leave things here for now and I hope that you found this article useful.

If you would like to dive a little deeper into the statistics involved in this type of sampling, there is an excellent video in the zedstatistics channel in YouTube titled “What are confidence intervals? Actually” that I highly recommend.

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References and further reading:

• GAO’s Using Statistical Sampling, publication PEMD-10.1.6
• Guy, D. M., Carmichael, D. R., & Whittington, O. R. (1994). Audit sampling: an introduction to statistical sampling in auditing. Wiley.
• Cochran, W. G. (1977). Sampling techniques. John Wiley & Sons.
• COAN904, Probability Sampling Course, GAO. (Note: sadly, the course no longer appears to be offered externally, but the manual can still be found here)

This is a follow-up to my previous post where I detailed my adventures trying to implement in Visual Basic .NET the Inverse Normal Cumulative Distribution Formula (a.k.a. NORMSINV).

As I mentioned in that post, I had been looking for a way to implement the NORMSINV function into a .NET application. I explained how I adapted Peter Acklam’s algorithm for which I adapted some C++ code that I found on the Internet into my .NET application. Later I found that others had done adaptations for various other languages including C#.

Sadly, though Acklam’s algorithm worked well, I could not get the function to match precision closely enough to my legacy program that I was replacing.

Peter Aklam never published his algorithm in any peer-reviewed journals so, other than some historical web pages, it was not possible to follow up to see if any changes or progress took place. However, those historical pages pointed me to the previous work of others.

Conducting that literature research, I found the seminal paper “Algorithm AS 241: The Percentage Points of the Normal Distribution” by Michael J. Wichura (1988), Applied Statistics, vol. 37, pp. 477-484. This paper included an implementation of a function “to compute the percentage point z_p of the standard normal distribution corresponding to a prescribed value p for the lower tail area” in FORTRAN. Wichura’s algorithm itself extends previous work by Beasley, J. D. and Springer, S. G. (1977), “The percentage points of the normal distribution”, Applied Statistics, vol. 26, pp. 118-121, which introduced the function PPND (Percentage Points of the Normal Distribution) as part of algorithm AS 111, also published in FORTRAN.

After a quick crash-course in FORTRAN syntax, I was able to easily (but tediously) translate Wichura’s algorithm to Visual Basic .NET. Wichura published two versions of the algorithm in his paper. I only adapted the double-precision function called PPND16 that you can freely download from my github repository.

Since Wichura’s code is published work and all I did was essentially translate it, I am not making any claims to this code other than as a humble contributor. Please feel free to copy or modify it and I hope you find it beneficial.

(Note: Support for MLT-2 will end on 30 September 2022)

This past Summer we conducted a series of statistical sampling inventories, which caused me to take a look at the Material Label Tool as a starting point for that project. I added some statistical sampling functionality and actually started calling it STATMAN II because it incorporated many of the features of the original STATMAN.

So I have decided to fully evolve the tool into a complete physical inventory data collection utility with full scanning and parsing capability of Navy ERP material labels.

Now that the application is done, I decided to drop the STATMAN II name and am simply calling it MLT-2, or Material Label Tool 2nd generation since it does much more than labeling now.

Who knows? It might come in handy one day.

Update: I have added a page on the main site with a demo video and more information. See it here.

Readers might remember my previous article on counting methods and RFID where I promised to revisit this topic. Well, here we are.

Interest in using RFID for warehouse physical inventory management continues to be high. The technology is very attractive and it is not difficult to wonder why it has taken so long to adopt RFID at this level. Let’s explore the challenges:

1. Location, location, location. This remains an elusive problem. Using RFID to locate items down to the bin level is difficult, not necessarily because of technology limitations but mostly due to the laws of physics. There are schemes to overcome these issues, such as smart bins, triangulation, etc. but these don’t scale well and are usually cost-prohibitive. See Bouet & Dos Santos (2008) for further reading.
2. Business uses. In a warehouse, we perform just a handful of basic operations: receiving, shipping, putting items away, picking items, and counting items. RFID shines when items are moving, such as cars through a toll plaza, passengers at an airport, etc. Therefore, it is no surprise that RFID does well in shipping and receiving operations at distribution centers, under proper conditions. But this is what we call “pallet level” tracking. In this article we are talking about bin-level tracking of individual product units in the warehouse. Picking and putting material away already require personnel to physically contact the items, so RFID does not solve any business problem there. That leaves physical inventory as the only potential area of opportunity, and making a large investment in RFID to improve one transaction area may not make the best business sense. It is difficult to find a paper in this area that does not sound like a sales pitch, but for further reading give Hackenbroich, G., Bornhövd, C., Haller, S., & Schaper, J. (2006) a try.
3. Technical. I cannot ignore technical challenges in this article, but let me concentrate on what I call business-technical decisions. For example, the data that is associated with each tag, interfacing systems, tag design strategy, integration of technology with business practices and rules. I would argue that this is the most critical part of any RFID implementation project. Those technical decisions can have a drastic impact on business and operations. There are many papers written on this subject, but many are full of bias toward specific vendors or technologies, so a safe source is the RFID Wikipedia page.
4. Workload impact. After the business-technical decisions are made, we need to consider impact on workload and productivity. In order for RFID to work for us, we would need every single unit to be tagged. Tags have to be printed/recorded, the data associated with each individual tag needs to be entered in a system, and the tag has to be physically affixed to each individual item. Let me stress that we do not mean that, if we have 10 units of a product, we would generate 10 duplicate tags – no, it is much worse, we would generate an individual tag for each unit. In other words, each tag needs to be unique if we hope to use RFID for counting. This is where some people bring up the fact that many tags have a “license plate” that already makes them unique – what that argument ignores is that each tag identifier still needs to be associated to each item being tagged. In contrast, this is never the case with barcode labels so, no matter how we slice it, we would be doing more work. Do we want our receiving process to take all that additional time and effort? Good time for a business case analysis. Does the benefit outweigh the impact? I had mentioned Daniel Hellström in one of my earlier articles, and he has a good case study that applies to this topic, see Hellström, D., & Wiberg, M. (2010). Perhaps one of the most prolific authors on the subject is Gary Gaulker; see his paper from 2010 for a good example of his work related to this topic.
5. Regulations. As you might have guessed, my belief is that the principal benefit of RFID in retail warehouse operations is in physical inventory support, albeit with the known challenges about location and up-front workload. However, regulations might not even allow us to do that. Current DoD rules are not specific enough, though that may be about to change, on what constitutes a “physical count” but some auditing standards an accounting policy make it more obvious that it means human observation. So, for physical inventories that support some regulatory compliance, and unless we are talking about very short range RFID, it appears that RFID is not an option for inventory accountability until the rules specifically allow it.

This article is only a superficial discussion of the many issues facing RFID adoption for DoD warehouse physical inventory management. There are likely more areas that can be included. As usual, the issues are not all technological. Much more analysis needs to be conducted on the potential for business re-conceptualization in DoD enabled by RFID technology; until then, RFID will remain a technological solution looking for a business problem.

References

Bouet, M., & Dos Santos, A. L. (2008, November). RFID tags: Positioning principles and localization techniques. In Wireless Days, 2008. WD’08. 1st IFIP (pp. 1-5). IEEE.

Gaukler, G. M. (2011). Item-level RFID in a retail supply chain with stock-out-based substitution. IEEE Transactions on Industrial Informatics, 7(2), 362-370.

Hackenbroich, G., Bornhövd, C., Haller, S., & Schaper, J. (2006). Optimizing business processes by automatic data acquisition: RFID technology and beyond. In Ubiquitous and pervasive commerce (pp. 33-51). Springer, London.

Hellström, D., & Wiberg, M. (2010). Improving inventory accuracy using RFID technology: a case study. Assembly Automation, 30(4), 345-351.

I first considered writing an article to discuss strictly MIL-STD 1916,  Department of Defense Test Method Standard: DOD Preferred Methods for Acceptance of Product (see PDF file attached to this article). That standard replaced MIL-STD 105 and it is often quoted when discussing statistical sampling, but often with unrealistic expectations. I know what you are thinking, another statistical sampling article? I just can’t seem to get away from the subject. Anyway, I started to dive into the standard in order to explain how it is most useful and when it is more appropriate to stick to general, and more basic, statistical methods when I found myself reading about reliability engineering.

Do reliability engineering concepts apply to inventory accuracy? Do they apply to any business process? It turns out that they can, and there are concepts such as Markov Decision Process (MDP) that are often applied to business. 

If we consider physical inventory control as our system, then we can see how that system exists in one state at a time that depends on a previous state (like a Markov chain). The system, most of the time, relies on transactions that themselves have a probabilistic outcome that would have an effect on the state of our inventory.  For instance, if 2% of receipts and issues posted are in error.

This brings back some memories. I once wrote a paper on how a logistics information system can impact operational readiness (Ao) and how optimizing the information system could have a more beneficial impact on operational readiness than increasing the reliability of individual weapons systems, because improving Mean Logistics Delay Time (MLDT) improves Ao for all associated weapons systems, even those that have not yet been invented.

It is the same concept with physical inventory controls. The system for managing the inventory, including transactions, policies, and procedures can have a more significant impact on inventory accuracy than any other physical attribute or strictly inventory-related activity.

What does that mean exactly? That there are other blocks in the chain that we need to consider. For example, procurement transactions, database maintenance actions, etc.

So what does that mean for MIL-STD 1916? Although it would not be wrong to apply MIL-STD 1916 to statistical physical inventory sampling to measure accuracy, one would still have to get their hands dirty (sort to speak) in order to analyze and extrapolate the results in a way that they provide us with a measure of our inventory accuracy for our entire population. In order to measure our results to see if we meet DoD guidance, we would still need to compute sample size, margin of error, and confidence intervals using basic statistical processes, even if relying on tables and methods from MIL-STD 1916. That military standard lends itself more to what engineers and reliability analysts refer to as “zero accept, one reject” methods (see this paper by Al-Refaie and Tsao (2011)). 

So,  to tie back to the MDP concept, MIL-STD 1916 absolutely has a place in physical inventory controls, but most especially in evaluating the reliability and acceptability of the transactional processes that are part of our inventory reliability chain.  In other words, testing each block in the chain of processes that affect inventory, such as receipt, issues, transfers, etc. is an excellent application for this type of statistical analysis.

Book recommendation of the month. Well, not really a recommendation, but a suitable reference to the above article: Modeling for Reliability Analysis: Markov Modeling for Reliability, Maintainability and Supportability by Jan Pukite & Paul Pukite. Buy a used copy for $10, the book is not worth the full sticker price.

Recently, I have noticed an increased interest in questioning the validity of conducting statistical sampling inventories. Many people do not understand the concepts or the value of statistical sampling and that may be driving these perceptions.

This post includes some excerpts from a paper that I wrote some years ago. It establishes the relationship from the CFO Act of 1990 down to DoD guidance on the conduct of statistical sampling inventories.

The Chief Financial Officer’s (CFO) Act of 1990 (Public Law 101-576) Established Statutory Reporting Requirements Regarding Inventory and Assets under the Authority of the Agency’s CFO

The CFO Act of 1990 provides the statutory requirements that:

• Establishes the authority of an agency’s CFO including “directing, managing, and providing policy guidance and oversight of agency financial management personnel, activities, and operations.”
• Requires the implementation of sound financial management practices under the CFO including “the implementation of agency asset management systems, including systems for cash management, credit management, debt collection, and property and inventory management and control.”
• Requires the CFO to submit “an annual report to the agency head and the Director of the Office of Management and Budget.”

The Federal Financial Management Improvement Act (FFMIA) of 1996 (Public Law 104-208) Established Specific Statutory Audit Requirements

• The FFMIA establishes the periodicity of auditing requirements to annual by stating that “no later than October 1, 1997, and October 1, of each year thereafter, the Comptroller General of the United States shall report to the appropriate committees of the Congress.”
• Section 805, subparagraph (b), in essence gives the Office of Federal Financial Management the authority to put any agency on report (i.e. DoD) by requiring “a listing of agencies whose financial management systems do not comply substantially with the requirements of Section 3(a) the Federal Financial Management Improvement Act of 1996, and a summary statement of the efforts underway to remedy the noncompliance.”

DoD Financial Management Regulations (FMR)  implement Public Law 104-208 (FFMIA of 1996) and 101-576 (CFO Act of 1990)

• The DoD FMR, Chapter 4, paragraph 040305, establishes a clear relationship between inventory records and the general ledger by stating “activities must reconcile line item accountability records to balances recorded in the general ledger inventory accounts at least quarterly.”
• Additionally, the DoD FMR, Chapter 4, paragraph 040306, specifies physical count as the process used for reconciling inventories and general ledger: “Activities must take physical counts of inventories in accordance with the procedures prescribed in DoD 4140.1 R, “DoD Materiel Management Regulation.” Activities must adjust the general ledger for differences between the general ledger balances and the physical count.”

The DoD 4140.1-R Establishes Policies for Physical Counting and Gives Priority to Sampling Methodologies

• The DoD 4140.1-R, paragraph C5.7.5.1.4, directs DoD components to “devote resources and select items for physical inventory” as a means to comply with DoD FMR.  Paragraph C5.7.5.1.4.1 of DoD 4140.1-R gives number one priority to “annual random statistical samples that shall support the determination of logistics record accuracy and financial record accuracy.”

The DoD 4000.25-M Vol 2 Establishes Procedures for the Conduct of Statistical Sampling Inventories

• Paragraph C6.2.1.1 of DoD 4000.25-M reiterates that physical reconciliation is the material accountability method: “Ensure accurate property accountability records for the physical inventory are maintained in support of customer requirements and readiness by performing physical inventories and location surveys/reconciliations.”
• Paragraph C6.2.2.1 recognizes the impracticality and inefficiency of conducting complete inventories: “The dynamic nature of the physical inventory control function and the cost of counting and reconciling records require that the approach be more selective than the 100 percent wall-to-wall total item count concept.”
• Paragraph C6.2.10 of this instruction reiterates the policy in DoD 4140.1-R which gives top priority to sampling methodologies. Subparagraph C6.2.10.1 states “a stratified, hierarchical inventory sample shall be accomplished at least once annually for the purpose of validating the accuracy of the accountable record.”

 

References

• Chief Financial Officers (CFO) Act of 1990, (Public Law 101-76)
• Federal Financial Management Improvement Act (FFMIA) of 1996, (Public Law 104-208)
• Federal Managers Financial Integrity Act of 1982 (FMFIA) (Public Law. 97-255)
• Government Management Reform Act of 1994 (GMRA) (Public Law. 103-356)
• DLM 4000.25-2-M, “Military Standard Transaction Reporting and Accountability Procedures (MILSTRAP)”
• DoD 4140.1-R, “Material Management Regulation”
• DoD 7000.14-R, Department of Defense (DoD) Financial Management Regulation (FMR), Volume 4, Chapter 4, “Inventory and Related Property” (May 2009)

I have come to the conclusion that there are simply no good books on statistical sampling for novice practitioners. A lot of the literature begins by covering statistical principles, which is important, but statistics is such a large field that most people get turned off or lost. There is also a lot about the field of statistics that we don’t need to know, for our purposes. Which brings me to my latest book recommendation.

“Audit Sampling: An Introduction”, by Dan Guy, Douglas Carmichael, and Ray Whittington,  is perhaps the best book that I have been able to find. I have the Third Edition of this textbook and it is the most concise textbook that I have found on the subject. It is laid out precisely for auditors, meaning that there are not too many side-bars into statistical or mathematical theory. It is the closest thing that I have found to a step-by-step guide for audit sampling, although that is not what this book is. It is a textbook, in the traditional sense. It also includes some excellent appendices, such as the full text of Statement of Auditing Standards (SAS) No. 39: Audit Unit (AU) 350 – Audit Sampling.

I recently revisited this textbook while preparing for some discussions for an upcoming project. This inspired me to put together a presentation to try to condense the topic of statistical sampling of physical inventory down to its simplest tasks: Planning, Selection, and Evaluation.

In the Planning phase, we are concerned with establishing the statistical parameters, such as the confidence level and margin of error – which, in many cases, are given to us.  We use those parameters during this phase to calculate the sample size (see my previous post).

The Selection phase is concerned with randomly choosing the samples to be tested.

Finally, the Evaluation phase consists of testing the samples, computing the results, and reporting our findings.

I put together a slightly expanded version of the above in my own Sampling Guide, available for download from this link.