Author Archives: thedoctor

It’s Not a Functional ExtAInction Battle in Procurement — But It is a Battle in the C-Suite (Part 2)

Namely, it’s a battle of propaganda versus reality, lies versus truth, against not only the other departments but the vendors selling solutions built on the lies and the consultancies coming in and spreading new lies on a daily basis!

The reality is that even though AI is NOT advancing at lightning speed, the claims around it are and that’s more dangerous than the tech. People are getting lured in to tech that’s not market ready, and that’s why so many projects are failing.

Even worse is the 5% that aren’t failing. Even though most of these are far from a resounding success, when the AI works good enough on the mostly tactical tasks it is installed for, the organizations start to trust it (even when they shouldn’t) and get overconfident on the ability of AI. These companies then approve a slew of AI projects and prematurely get rid of people they shouldn’t, hindering everyone’s ability to do a proper job. This can lead to Procurement extinction when it leads to organizational extinction with not enough people left to deal with the first crisis that materializes that the AI can’t handle.

Procurement has to find a way to win the battle of propaganda and stave off “AI” that is unproven or that is in select use cases the organization is not yet ready for due to a lack of data, systems integration, or knowledge to properly use the real AI that works. Otherwise, it won’t survive, and there’s a chance the business won’t either with one economic crisis after another; supply chains constantly breaking as a result of trade wars, sanctions, and border closings as a result of wars and geopolitical uprisings; demand constantly shifting as unemployment and costs rise; etc.

In other words, as the authors wrote in the Functional ExtAInction Battle white paper we started discussing yesterday, we are in an Age of Disruption, but it’s not the tech (which rarely works), but the marketing and lies around the tech (that all of the psychopathic CEOs want to believe so they can fire all their human workers and replace them with 24/7/365 robots that don’t have any rights and don’t need to eat or sleep). The sad thing is that we’d have a better chance of surviving an age of real AI than this, especially when lying is now sanctioned in the USA and instead of being investigated by the FTC you are given a free pass, and should a state court convict you, you can just buy a pardon! Given that most people didn’t understand technology before the Age of (Fake) AI, how can you expect to understand AI and what is real and what is not?

Moreover, an Age of Evolution has to follow because the C-Suite believes,
right or wrong, that every aspect of their organization has to digitally evolve or they will die. This means if that Procurement doesn’t evolve digitally, it will be replaced by a function, or a team, that does. Fortunately for Procurement, every vendor, and their office dog, now claims to be AI-backed, -driven, -enhanced, -first, -powered, etc. even if they don’t have any AI at all! This means that Procurement can select a solution that works for them, which uses configurable, adaptable, RPA; embeds best practice; encodes decision optimization and predictable, dependable trend analysis in its analytics; etc. and automates 80% of their work error free. They can evolve, look like they are meeting the impossible AI mandate, and get better results than the rest of the business.

Finally, a new world is coming because, once we have the AI crash, vendors with real solutions built on real, traditional, AI models that can now be effective with the data available and processing power at our disposal will emerge. Instead of searching for the magic model that will supernaturally become emergent and achieve intelligence and work on every problem, the next generation of vendors will take the time and make the effort to integrate dozens (if not hundreds) of traditional, appropriately trained, models that reliably solve point-based problems with high, and often near-perfect, accuracy; encode guardrails for the rare situations in which the models might fail; and build workflows that are easy to follow, execute, and even manipulate and that solve the tactical data collection, manipulation, analysis, and export problems that take up the majority of a Procurement professional’s time and deliver no value in return for their completion. Procurement teams that wait to identify and adopt this technology will be the ones that rule the new world, while the other teams (departments, and maybe even businesses) won’t exist any more.

Moreover, the Procurement professionals of tomorrow will be almost entirely focussed on strategic capabilities, as the need for tactical efforts will be rare and limited to not yet seen exceptions (as each resolution will train the platform which will then be able to handle similar exceptions reliably from that point on). This means that today’s Professionals need to start preparing for that eventually. They don’t need to rush, but they do need to start and make steady progress.

When we say the urgency is not as great as the authors make it out to be, we mean it. the doctor has been covering this space for 20 years. For 20 years he has been reading “future of” white papers that proclaimed the space was going to be totally different in 10 years. This means that the space should have been totally transformed by modern tech 10 years ago. Guess what? It wasn’t! Nor was it the next year. or the year after that. Or the year after that. For 10 years the predictions of radical transformation have failed to come true. While the pace of digitization will increase, the trend will continue to hold. After all, the point of Procurement has not changed since the first manual was published 138 years ago. The world may have changed, but the world’s second or fourth oldest profession has not! (Now, if the “sales” profession really was the oldest, that makes the “buying” profession the second oldest. However, before that we had [religious] leaders and stargazers, which are still professions today. So that would make “sales” the third oldest and “buying” the fourth oldest, despite the claims.)

We agree you have to start today, but you will only win the race if, like the tortoise, you go slow and steady and master each step while the others try to take shortcuts with tech they don’t understand, become overconfident in the great sounding (but incorrect) outputs that are returned, get lazy and lethargic as a result, and nap on the job — allowing you to pass them by.

Like objects in the rear view mirror, AGI appears closer than it is. (And that’s a good thing, because if AI actually emerges, we are not likely to continue on this planet.)

Like we said before, the paper is worth reading, it gets the stages right, it gets the mild urgency right, it just gets AI wrong (at least where it is today and will be tomorrow, and the day after that, and the day after that for quite a while … at least until entirely new models and breakthroughs are made that may actually model intelligence and not just random computation).

Plus, the history lessons (including those which really don’t have anything to do with Procurement, but it’s a nice lesson anyway) are a good read for those that didn’t study history in school (or even remember what happened last decade).

Is It a Functional ExtAInction Battle in Procurement? (Part 1)

About a month ago, Jonathan O’Brien of Positive Purchasing and Guy Strafford of OneSupplyPlanet released a white paper on this very topic where they claimed:

AI means Procurement is going to be left jostling with the rest of the business to control the commercial space. It is a race. Start now or lose.

I have to say I don’t agree. In fact, we’re still in the start now AND lose timeframe, as evidenced by the recent MIT study demonstrating that 95% of AI projects have failed and the plain and simple fact that the majority of consultancies, providers, and new-age services-as-software providers claiming to provide AI Employees (which is all bullcr@p by the way, see our previous posts as AI Employees Aren’t Real) are applying the wrong AI to the problems they believe AI can solve. (And that’s another issue, real AI can do quite a bit, but not what the greatest con-man since PT Barnum not in politics keeps telling us it can do.)

However, they’re close. You need to start learning what AI is and isn’t now, looking out for the past and next (NOT current) generation of providers who were applying real AI properly and who will emerge to apply next-gen implementations of real (non-LLM) AI properly to Procurement problems (with real education and real procurement experience to back it up, and that’s not a Youtube crash course in how to engineer a prompt for a bullcr@p LLM, by the way). That way, you can select the right solution for the right problem at the right time.

You are going to have to jostle with the rest of the business, especially Supply Chain who believes they are the most critical function (when they are only one side of the critical coin with Procurement being the other half), IT who believes they have the best technical knowledge and should make the decision (and don’t really understand what the tech has to do for the rest of the business, just what is easy for them to maintain and fun for them to play with), Finance who wants to ensure they have better visibility and control over every dollar, Sales and Marketing who want to pretend the age of the Mad Men never ended, and so on.

Moreover, while many of the activities carried out by Procurement functions will be automated, these are just the data entry, transformation, and output functions that we were promised would be automated 40 years ago when computers started entering the average business. We’re not going to see the automation of the strategic functions, the relationship management, or the consensus building that is critical to success. While the authors may claim that many Procurement activities will soon be managed directly by the business, that will be a step back as we’ve seen, and Hackett has catalogued, the significant difference in performance in businesses that have standalone best-in-class Procurement departments and those that don’t have any Procurement departments. The reality is that, in the economic and technological climate that is coming, businesses without Procurement likely don’t have the same chance of survival as businesses with Procurement.

According to the authors, all that will remain are the commercial hub activities that require human expertise and intervention: change management, innovation, and sophisticated market engagement. While these will definitely remain, the reality is that you can’t turn supplier discovery, qualification, onboarding, relationships, performance management, development, and innovation over to a machine. You can’t build organizational consensus through emotionless algorithms. Not all award decisions can come down to the results of lowest cost computations after automated negotiations based on bid rank or classic game theory (or even modern game theory as many of the game theory “experts” get this wrong regularly, as the author has poked holes in claimed “optimal” solutions presented on LinkedIn and vendor websites more than once, because there’s always an assumption as to what optimal is, and it’s usually one sided or wrong). Technology will reduce the time requirements for a lot of these processes as it will fully automate the data collection, transformation, analysis, and recommendations for you, as a human expert, to one-click accept or deny, but a human will still be needed. All that will disappear is the 80% to 90% of the work that is tactical data processing. While this will displace people, as all technological evolutions do, we need to remember that each evolution has ultimately resulted in the creation of new jobs as old ones get automated. Not only will people be needed to maintain the automations and hardware supporting them, but new strategic and creative jobs, some of which we can’t yet predict, will emerge as a result.

Moreover, the authors believe that since such capabilities are found across the business, the other functions … will want to move into this commercial space and that unless Procurement develops the skill sets to a higher standard than the other functions, it will be outpaced.

While they are not wrong, and while this will make life difficult for Procurement if other functions get ahead of them in terms of value delivery to the business, Procurement is more than just change management, innovation, and market engagement. However, without this core, Procurement’s differentiation will be limited and its overall influence over the business not what it should be.

Moreover, while there will be a battle to evolve and survive post AI, we’re not there yet because what we have now is not AI, it’s the latest instantiation of Silicon Snake Oil with grandiose, false, claims and no real value. We’re still a few years away from widespread application of real, useful, AI, and more than a few years away from post-AI. In other words, the likelihood of Procurement being phased out as-is by 2035, as the author’s claim, is not too likely at this point. However, depending on how fast we get to, and through, the Fake AI crash and on to real AI, Procurement could be in deep trouble in the early 2040s. Which is why you have to start learning about real AI today, where you can apply it safely and effectively, and how you can implement it bit by bit for stable, guaranteed, success. Learning — not rushing in to an incomplete, half baked, solution guaranteed to make you the next casualty among the 95% error rate — is key.

So for those of you who asked, that’s my initial response as to whether or not there is a Functional ExtAInction Battle in Procurement. There’s the same battle there’s always been, but without real AI, and without the rest of the business having the deep Procurement knowledge necessary for real Procurement success (which goes well beyond what can be automated today), there’s no chance of ExtAInction in any Procurement department which is a leader in its operation.

However, even though the conclusion is wrong, the majority of the observations and analysis in the paper is right. In fact, it’s one of the best the doctor has read yet in terms of analysis and insight. So in our next post(s) we are going to discuss that. Despite a few mistaken conclusions, which can be forgiven because it’s really hard to understand the reality when it takes a very advanced understanding of mathematics to understand the tech, which is necessary to understand the reality, it does a great job of figuring out how Procurement needs to be seen and why. So download the white paper and read it today for the insights within (without having to worry about an extinction that won’t happen … at least not yet).

Technology Has Improved, But It’s Still Not Solving Fundamental Problems!

In a recent Procurement Insights posts, THE REVELATOR tells us that a 2007 challenge is finally being addressed in 2025, and he’s right in that it’s being addressed, but parts of the problem are still not being solved. But before we can dive in too deep, let’s review the four points from his 2007 post on the Change Management Myth.

The core of his eighteen year old post was the statement that many failures stem not from resistance to change itself but from deeper systemic issues in how technology is deployed, which is often the case because, when the system selected is the one with backing from the core team, there is obviously some desire to change, but something is preventing that change from happening. Based on interviews and discussion with third parties, including one with a professional who had over a decade of public sector Procurement system implementations at the time (remembering that the first procurement system only went live twelve years before his post eighteen years ago), he identified four key reasons why automated procurement systems fell short and resulted in poor adoption and outcomes.

These four reasons were:

  • lack of technical savvy and cultural understanding
  • procurement module was an ERP afterthought
  • lack of process mapping/improvement before automation
  • discrepancy between promises and delivery

While technology has improved greatly, as far as I’m concerned, two of these still aren’t being solved because the technology that is addressing the issues are not solving the fundamental problems. In THE REVELATOR‘s post, he points to an AI-powered “digital team member”* agent solution (and one custom built for the SAP ecosystem) as an example of a technology that addresses the four problems (but we will not name it as we don’t want to be negative on a particular technology that does offer some value to customers in Ariba jail). Our goal of this article to address the statements he is making and the fundamental requirements to solve the problems that still plague our space).

According to THE REVELATOR, each of the problems are addressed for the given reasons:

  • lack of technical savvy and cultural understanding because these platforms minimize the need for advanced skills with conversational interfaces and email integrations that don’t require extensive training and that “implicitly teach the why” by delivering immediate value
  • procurement module was an ERP afterthought because this technology is purpose built for procurement, enhancing the across-the-board experience by implementing and supporting “best practice” out of the box
  • lack of process mapping/improvement before automation because it inherently improves processes by AI-triage, prioritization, and workflow embedding while analyzing data in seconds, eliminating manual entry, and supporting iterative testing
  • discrepancy between promises and delivery because seamless integration allows for instant impact, results, and measurable ROI

And each of these approaches is an approach that addresses the problem. However, it does not solve two of them, and that can lead to even worse errors being made then before. Namely, it doesn’t do anything for:

  • lack of technical savvy and cultural understanding because guiding a person through a process, which is the one statistically estimated (i.e. guessed) to be the correct one, does nothing to address their lack of technical savvy or Procurement understanding, and, in fact, if it makes the process too easy or, on the easy test cases, gets the process too right, it leads the user into a false sense of security, just like vibe coding (which results in over half of the code being produced having serious security issues) or vibe physics (which sometimes results in delusions and sometimes even early stage “ChatGPT” psychosis), except in this case the user will happily authorize a million dollar purchase for the wrong product if the system doesn’t detect it’s the wrong product
  • lack of process mapping/improvement before automation is not solved by slowly “learning” processes post implementation, and letting the system guide you on “corrections” because probabilities are not certainties, and if you don’t do pre-implementation process and data mapping, and understand the state of your data (and, if necessary, cleanse and enrich it), the system could make very wrong decisions (because it can only compute on the data it has, and if that data is bad, the recommendations will be very bad)

Not only does too much AI not solve the problem, but it actually exacerbates it. While we do want Augmented Intelligence, we want carefully designed, selected, evaluated, and implemented Augmented Intelligence where we can have very high confidence in everything it does because we pre-verified it, understand its limits, validated its data, and never apply it inappropriately. Plus, we want it to support our thinking and analysis, not have us support it when we have no clue where it’s coming from.

At the end of the day, we want better educated and trained personnel, because then they will know what tool to use where, how reliable the answer will be, and when a process can be fully automated vs. when you need manual checks. And then we want to give them the technology that makes them up to 10 times as efficient at their job by automating all of the tactical data collection, processing, analysis, and summarization so they can review everything they need to make the right decisions, select the right options in the system, and then have the system automate the tactical processes that come after. That’s not being guided by AI, that’s guiding the AI. That’s not just a semantic difference, it’s a significant process difference that can have a significant impact on Procurement efficiency and effectiveness.

* Let us remind you that AI Employees Aren’t Real!

EOQ Part II: The Quantity You Need the Computer To Calculate, Though You Can’t Depend On It Until You’ve Calculated and Verified it First!

In our last article, I noted how I was reminded that most of today’s so called Supply Chain and Procurement experts could not pass a basic EOQ exam question, and that the reason for that was lack of real supply chain (cost) knowledge, math, and the intricacies of inventory carrying cost that are swept under the rug in the classic EOQ formula, which, sometimes, is not at all accurate.

I say reminded because this was something I explored in depth about 17 years ago when Charles Dominick, the founder of Next Level Purchasing (now a part of Certitrek) and co-author of The Procurement Game Plan approached me about the severe inadequacy of the EOQ formula (and how it often leads to Procurement Managers ordering too little, or too much, and never truly understanding the true cost of inventory or the levers available) and asked if I could help him come up with a better equation that he could teach to his students in his (advanced) Procurement courses.

The answer was yes, we did, but then he decided that he didn’t want to use it at the time because getting it close to right was a bit more involved than he thought and, well, math. It was a bit beyond what an average student could be expected to do in a spreadsheet. That’s because an extended EOQ equation has to take into account, at a minimum:

  • actual warehouse space utilization of an item (screws don’t take up much space, but control system boxes do), and
  • the cost of capital

Moreover, while I fully agree with Mr. Mr. Koray Köse that you shouldn’t re-invent the wheel, there’s nothing wrong with strengthening the rubber, improving the tread, and reinforcing the rims — which is where we started out. We started with the classic equation and just extended it to take into account the above.

We started by determining the actual inventory cost of a single unit of an item and the cost of capital tied up in that unit since the most inaccurate part of the classic equation is the average across-the-board CCP. This gave us this equation:

  • EOQV1 = √ ( (2 * ACPO * AUU) / ((AWC * UV / UWV) + (UC*WACC)) )

where

  • ACPO = Acquisition Cost Per Order
  • AUU = Annual Usage in Units
  • UC = Unit Cost

as before, and

  • AWC = Annual Warehouse Cost (which is the FULL operating cost, including staff)
  • UV = Unit Volume
  • UWV = Usable Warehouse Volume (i.e. just because your warehouse is 100*100*10 or 100,000 cubic ft, doesn’t mean you can use it all; it will depend on your layout, since you can’t store in the aisles and the shelves themselves take up some space; in reality, less than 50% will be usable)
  • WACC = Weighted Average Cost of Capital

To gauge how good this equation is, let’s recalculate our Spacely Sprockets example.

As before, we’ll assume:

  • ACPO = 3,400
  • AUU = 5,068
  • UC = 1300, 1250, 1200, or 1175, depending on volume

And we’ll assume the following costs and dimensions (which are reasonable given the Finance CCP):

  • AWC = 4,000,000
  • UV = 1 cu ft (they are gears for industrial moon mining equipment)
  • UWV = 40,000 cu ft
  • WACC = 0.10 (10% cost of capital)This gives us:
    • EOQV1 = √ ( 34,462,400 / (100 + 0.1*UC) )

    and we calculate for each price break:

    • 1300: √ ( 34,462,400 / (100 + 130.0) ) = √ (149837) = 387
    • 1250: √ ( 34,462,400 / (100 + 125.0) ) = √ (153166) = 391
    • 1200: √ ( 34,462,400 / (100 + 120.0) ) = √ (156647) = 396
    • 1175: √ ( 34,462,400 / (100 + 117.5) ) = √ (158448) = 398

    which is a bit closer to the EOQ, but not by much, but helps us understand that when it comes to EOQ, the primary factor to consider is often going to be the cost of capital, because, whether you use all of your warehouse space or not, your warehouse costs are relatively fixed for a year, which means your inventory costs per unit are relatively fixed as well (whether it’s in for a few days or a few months), and when you’re buying, you’re ultimately trying to balance the WACC with the fixed inventory allocation (which is typically just averaged to get the total inventory cost that is used to compute the average carrying cost percentage that goes into the standard equation).

    Moreover, the cost of capital is dependent on how long the item is in inventory! This says the equation needs to be modified to take this into account:

    • EOQV2 = √ ( (2 * ACPO * AUU) / ((AWC * UV / UWV) + (UC*WACC*EDIR)) )

    where

    • EDIR = Expected Days in Inventory Ratio (the WACC is annual, if the item is only expected to be in inventory for half a year, the ration is 0.5)

    Moreover, this also allows us to build in some consideration for price breaks! Since each price break dictates a maximum number of orders per year based upon the AUU, we can simply define

    • EDIR = (MBQ)/(2*AUU)

    where

    • MBQ = minimum break quantity

    This slight revision gives us:

    • 0400@1300: √ ( 34,462,400 / (100 + (130.0 * 0.04) ) ) = √ (34,462,400 / 105.2) = √ (327589) = 572
    • 1000@1250: √ ( 34,462,400 / (100 + (125.0 * 0.10) ) ) = √ (34,462,400 / 112.5) = √ (306332) = 553
    • 2000@1200: √ ( 34,462,400 / (100 + (120.0 * 0.20) ) ) = √ (34,462,400 / 124.0) = √ (277923) = 527
    • 5000@1175: √ ( 34,462,400 / (100 + (117.5 * 0.50) ) ) = √ (34,462,400 / 158.8) = √ (217018) = 466

    Which is closer, but certainly no cigar! The fact of the matter is that the rule of thumb of (2 * ACPO * AUU) that was developed to make the formula easy to understand and calculate while still providing a curve that is relatively cost-insensitive near the optimal quantity, is not perfect. While it will often get you close, it could still be a ways off that becomes significant when costs get into the millions. If there are no price breaks that can cause significant swings, we can see it works pretty well, but when there are, it doesn’t do as well. So we need to address this too!

    Without rewriting the equation entirely, this isn’t as easy to do as our fixes so far because the equation was designed to give a good curve that balanced inventory costs with acquisition costs without requiring deep, precise modelling. Still, when we have volume breaks, we can note that our order costs will be higher at lower volumes (which means more orders) and lower at higher volumes (which means less orders) and replace that 2 with a parameter that is defined on the volume breaks.

    More specifically, we will use:

    • EOQV3 = √ ( (C * ACPO * AUU) / ((AWC * UV / UWV) + (UC*WACC*EDIR)) )

    where

    • C = 2 x AVV/XBQ
    • XBQ = maximum break quantity (= maximum order quantity for final tier)

    This final slight revision gives us:

    • 0400@1300/c=10.0: √ (172,312,400 / (100 + (130.0 * 0.04) ) ) = √ (172,312,400 / 105.2) = √ (1637950) = 1279
    • 1000@1250/c=5.00: √ ( 86,156,400 / (100 + (125.0 * 0.10) ) ) = √ ( 86,156,400 / 112.5) = √ ( 765831) = 875
    • 2000@1200/c=2.00: √ ( 34,462,400 / (100 + (120.0 * 0.20) ) ) = √ ( 34,462,400 / 124.0) = √ ( 277923) = 527
    • 5000@1175/c=2.00: √ ( 34,462,400 / (100 + (117.5 * 0.50) ) ) = √ ( 34,462,400 / 158.8) = √ ( 217018) = 466

    Which, will still not perfect, gives us an even stronger indication that the EOQ is at the first price break, because at the 0 break, we’d have to order more than we are allowed to hit our EOQ based on our high orders, but at the first price break, our EOQ is slightly less, which means we round up and use that breakpoint, which we found out in Part I is optimal.

    So, does this mean you should scrap the classic, simple formula of

    • EOQ = √ ( (2 x ACPO x AUU) / (UC x CCP) )

    and replace it with:

    • EOQV3 = √ ( (C * ACPO * AUU) / ((AWC * UV / UWV) + (UC*WACC*EDIR)) )

    The answer is no. Even though this formula works well if there are no price breaks, the point of this exercise was not to develop a better EOQ formula, but to demonstrate that classic EOQ (still used in many inventory and classic MRP/ERP systems) is broken.

    Moreover, there is no one EOQ formula because EOQ is 100% dependent on inventory levels and costs when you order, shipping times and costs when you order, and supplier and carrier breaks. It changes all the time.

    That’s why, if you want to get it right, you need a modern optimization-backed SCP system to help you dynamically calculate it in real time so you put your orders in (against your contracts) at the right time to balance cost and risk.

    What does that system look like? We addressed some of it in our prior posts on optimization here on Sourcing Innovation and will likely address it again in the future, but for now, follow @Koray Kose for insights on what a modern supply chain solution should address from a business viewpoint and @Jennifer Rouse for what it should do from a systems viewpoint.

EOQ Part I: The Quantity You Can’t Depend On The Computer to Calculate!

I was reminded of this while reading Mr. Koray Köse’s great piece on how our supply chains are literally drowning in wannabes who mistake theory for expertise where he accurately and astutely noted that most of today’s so called “experts” could not pass his Economic Order Quantity (EOQ) exam question. And I totally agree. Because

1) Math (where competency in many Western nations decreases every year and where the US is literally becoming math stupid, as reflected in the latest OECD ranking which puts it 25 out of 31 “developed” countries that were globally measured with countries like Croatia coming in ahead of it).

2) No real understanding of supply chain or total supply chain cost!

3) Even less understanding that your EOQ (Economic Order Quantity) is not your suppliers EPQ (Economic Production Quantity) and for high cost/complex products, this can sometimes (but not always) be much more important (and impactful) than the classic EOQ formula would dictate.

Mr. Köse illustrates this deftly when he shared one of the questions he uses to gauge whether or not his MBA students truly understand EOQ. The core variant of the problem he shared with us was this:

  1. The purchasing manager for Spacely Sprockets orders mechanical gears from an industrial supplies distributor, Cogswell Cogs.
  2. Spacely Sprockets uses 5,000 gears per year.
  3. Annual inventory carrying costs are 20% and order costs are 3,400 per order.
  4. The following order discount price schedule is provided by Cogswell.
    • 0,200-0,999 $1300 / unit
    • 1,000-2,999 $1250 / unit
    • 3,000-4,999 $1200 / unit
    • 5,000+      $1175 / unit
    

    5. 

  5. Determine the optimal order quantity, total cost, and actual per unit cost (once order costs and inventory carrying costs are taken into account).
    6. 



Now, if you were a prepared student, you might have memorized the classic EOQ formula:


    

  • EOQ = √ ( (2 x ACPO x AUU) / (UC x CCP) )
    • 



where


    

  • ACPO = Acquisition Cost Per Order = 3,400
    • 

  • AUU = Annual Usage in Units = 5,000
    • 

  • UC = Unit Cost
    • 

  • CCP = Carrying Cost Percentage = 0.20
    • 



and this leaves you with


    

  • EOQ = √ ( 34,000,000 / (0.2 * UC) )
    • 



and you can work this out at each price break:


    

  • 1,300: √ ( 34,000,000 / 260 ) = √ (130,769) = 362
    • 

  • 1,250: √ ( 34,000,000 / 250 ) = √ (136,000) = 369
    • 

  • 1,200: √ ( 34,000,000 / 240 ) = √ (141,666) = 376
    • 

  • 1,175: √ ( 34,000,000 / 235 ) = √ (144,680) = 380
    • 



which indicates the first price bracket is the correct one for you, and you should be making 13.8, rounded to 14, orders every 26 days (and net a total volume of 5,068 units over the year) and, on average, you will carry each unit of inventory for 13 days.


    

  • unit cost: 5,068 * 1,300 = 6,588,400
    • 

  • inventory carrying cost: 13/365 * 0.2 * 6,588,400 = 46,931
    • 

  • order cost: 3,400 * 14 = 47,600
    • 

  • total cost: 6,682,931
    • 

  • unit cost: 1,319
    • 



But this is NOT an EPQ for the supplier, which means that you might be paying more than you need to. To figure that out, you have to analyze the costs at each breakpoint that is reasonable for you.


These are:


    

  • 362, your computed EOQ, with 14 orders per year
    • 

  • 1014, the first discount tier, at 5 orders per year every 73 days, with 36.5 days of inventory on average
    • 

  • 5,068, at the third discount tier, at 1 order per year every 365 days, with 183 days of inventory on average
    • 

  • … because you can’t hit the 2nd tier more than once
    • 



First run the calculation at 5,068, because your greedy executives only understand unit discounts:


    

  • unit cost: 5,068 * 1,175 = 5,954,900
    • 

  • inventory carrying cost: 183/365 * 0.2 * 5,954,900 = 595,490
    • 

  • order cost: 3,400 * 1 = 3,400
    • 

  • total cost: 6,553,790
    • 

  • unit cost: 1,293
    • 



You quickly see that you clearly want the discounts even if your inventory costs shoot up because 633.5K in savings is greater than 595.5K in expected inventory carrying costs.


But you’re not done yet. Now you have to run the calculation at 1,014 units an order over 5 orders, because it’s also a valid option and captures the suppliers first EPQ point:


    

  • unit cost: 5,068 * 1,250 = 6,335,000
    • 

  • inventory carrying cost: 36.5/365 * 0.2 * 6,335,000 = 126,700
    • 

  • order cost: 3,400 * 5 = 17,000
    • 

  • total cost: 6,478,700
    • 

  • unit cost: 1,278
    • 



which is your actual EOQ because it not only takes advantage of the supplier’s EPQ level but does so at the breakpoint that is closest to that given by your traditional EOQ calculation!


Now we’ve now clearly demonstrated why most of today’s so called experts couldn’t calculate EOQ with a computer because it’s not always the classic EOQ formula (or whatever pseudo-random formula happens to be in the forecasting system they try to use), or the supplier’s optimal EPQ level (if that leads to a significantly high storage cost for you — JIT is a core tenet of lean for a reason, inventory is costly, and while you need a safety stock, too much not only presents too much obsolescence risk but shoots your carrying costs way up), but usually somewhere in between (where the optimal curves intersect closest to their respective minima). Good luck doing that if you can’t do math, don’t know supply chain, and think Chat-GPT holds the answer to everything.


What we didn’t demonstrate is why, in reality, you often need a computer to calculate it (and that comes down to the inventory carrying costs which are often much more involved than Finance believes) and your associated supply chain costs. The reality is that you might have to re-write your formulas, which really will require a computer to constantly calculate and recalculate your true inventory carrying costs, but the reality is that you will only be able do this AFTER you understand what the proper order volumes should be (because you need to check that you worked out the formulas and calculations right for your supply chain)! We might tackle this in another article, because the only way to get costs way down is to help Finance and Operations understand the true costs and how to tackle them (because if you’re still running on an average ICC of 20%, or even worse, 25% to 30%, someone, somewhere, is performing pretty poorly in their profession).