Category Archives: Forecasts

Roughly Half a Trillion Dollars Will Be Wasted on SaaS Spend This Year and up to One Trillion Dollars on IT Services. How Much Will You Waste?

Before we continue, yes, that is TRILLION, numerically represented as 1,000,000,000,000, repeated twice in the title and yes we mean US (as in United States of America) dollars!

Gartner projects that IT spend will surpass 5 Trillion this year. When you consider that 30% of IT spend is usually for software, and that one third (or more) of software spend is wasted (for unused licenses, which is why we have a whole category of IT and SaaS specialists that analyze your out-of-control SaaS and software spend and typically find 30% to 40% overspend in a few days), that means that roughly half a trillion dollars will be wasted on software this year.

Even worse, Gartner projects that spending on IT Services will reach 1.5 Trillion. And the waste here could be two thirds! Now, we all know that you need IT services to implement, integrate, and maintain those IT systems you buy. But how much do you need? And how much should you pay? Consider that an intermediate software developer should be making 150K a year (or 75/hour), that says that an intermediate implementation specialist shouldn’t be making any more than that, and not billed at more than 3 times that (or 225/hour). But how much are you being billed for relatively inexperienced implementation consultant, with maybe a few years of overall experience and maybe six months on the system that you are installing? the doctor knows that rates of $300 to $500 are not uncommon for these resources that are oversold and overcharged for.

But this isn’t the worst of it. As per our upcoming article Fraud And Waste Are Not The Same Thing, many implementation “partners” will try to get all they can get and make sure that when you go in for a penny, you go in for a pound and they will push for:

  • frequent change orders during implementation, usually billed at excessively high day rates as they have to “divert resources” or “work overtime”
  • unnecessary customizations or real-time integrations that are an extensive amount of work (and cost) when out-of-the-box or daily flat-file synchs are more than sufficient
  • extensive “process evaluation” or “process transformation” processes that are well beyond what you need to eat up consulting hours
  • extensive “best practice” education when your practices are good enough for now and/or those best practices are already encoded in the system you just bought and paid a pretty penny for and just following the default process gives you the same education

That will often double to triple the cost. But that’s not the worst of it. As per comments the doctor has made on LinkedIn, he regularly hears stories of niche providers losing 200K deals because customers said their quote was too low because all the Big X companies quoted over 1,000K for 100K worth of work. Literally. This is because, as the doctor has noted in previous posts and comments on LinkedIn:

  • they don’t have the talent in advanced tech (and even The Prophet has noted their lack of talent in areas of advanced tech in multiple LinkedIn posts, though he has been much more diplomatic than the doctor in discussing their lack thereof; but he did note in a 2024 advice post that consultancies are going to have a hard time attracting talent this year) — for every area, they’ll have a team leader who’s a superstar, two or three handpicked lieutenants who are above average, and then 20 to 40 benchwarmers who are junior and not worth the rate they are charging)
  • they have an incredible overhead — posh offices to house the partners making more than top lawyers who have a lifestyle to maintain
  • they don’t have the knowledge of, or experience in, modern tools — some of which are ten times more powerful than last generation tools; this, of course, means the Big X benchwarmers are using last generation tools which take ten times the manual labour to extract value from
  • etc.

There’s a reason the doctor said that if you want to get analytics and AI right, DON’T HIRE A F6CKW@D FROM A BIG X! and stands by it! Unless you want to pay 1K an hour, you’re not getting that one superstar resource trying to be the front end to two dozen projects that his three lieutenants are trying to manage, all of which are staffed by junior to intermediate individuals who can barely follow the three to five year old playbook.

There’s a reason that The Prophet predicted in his 9th prediction that SaaS Management Solutions [will] Start to Eat Services Procurement Tech and that many companies will go in house if they have tech expertise. Because he realizes that these consultancies will have a hard time not only hiring, but retaining, tech talent when they have hiring freezes, salary freezes, and reduced engagements as more and more companies can’t afford the ridiculous rates they’ve been charging recently. (Companies may not have had a choice during COVID where it was implement on-line collaboration and B2B tech or perish, but now they do.)

But there are still many companies who will, when they encounter a (perceived) tech need, immediately pick up the phone and call Accenture, CapGemini, Deloitte, McKinsey, etc. and bring them in to help them understand who to bring in for an engagement, instead of widening the net to niche providers who are 3 to 5 times cheaper, and who will deliver results at least as good, if not better.

Now, again, the doctor would like to stress that, despite how much he insists they are usually not the right solution for advanced tech implementation, that Big X are not all bad, and sometimes worth more than the high fees they charge. Most of these companies started off as management/operational/finance/strategy consultants and grew big because they were one of the best, and in certain domains, each of these companies still are. But being good at a few things doesn’t mean they are good at everything, and that’s very important to remember.

And while there will be exceptions to the rule (as every one of these companies has some tech geniuses), the reality is that when you need more bodies than there are talented bodies in an entire industry, you’re not going to get them and, because consultancies are not cool when you want to be a tech superstar (and join a startup that becomes a unicorn), the ratio of superstar to above average to average to below average talent in these organizations is much worse than in multinational tech companies (like Alphabet, Apple, Meta, Microsoft, etc.) where you know the majority of their employees are not the best of the best. (Because if they were the best of the best, there’s no way they’d lay off 10,000 employees at a time every time the market jitters.)

In short, manage that IT services spend carefully, or you’ll be double paying, triple paying, or worse and providing a big chunk of the roughly ONE TRILLION DOLLARS in IT services overspend that the doctor predicts will happen (again) this year. (Unless, of course, you agree with Doctor Evil who says, why make trillions when we could make … billions. Because that’s exactly what happens when you overpay for software and services. Don’t expect the Big X to say anything as they get the majority that overspend, and that’s how they stay so [insanely] profitable.)

How Not to Excel at Forecasting

This post originally ran four years ago. But since a critical mistake is still being made, it’s time for a repost.

How Not to Excel at Forecasting?

Simply put, use Microsoft Excel. It’s appalling that a survey by ToolsGroup and the Global Market Development Centre (GDMC) found that even though two-thirds of companies in the consumer goods supply chain consider demand volatility and forecast accuracy a high businesses priority, half still rely on Excel spreadsheets for forecasting.

Relying on Excel for forecasting is like relying on:


  • a Longship to get you across the Atlantic

  • your first guess on Let’s Make a Deal to be the right one

  • a shareholder proxy getting on the ballot at a Fortune 500

  • Florida surviving a hurricane season without any major city suffering damage

  • the price of fuel going down and staying down for an upcoming series of spot buys

  • natural resource supply to be consistent and predictable year-over-year

  • a flip of a fair coin to come up heads seven times in a row

Now, it’s true that:


  • the Vikings did make it across the Atlantic in a Longship, but a single storm could sink it

  • the first door you pick, with one-in-three odds, could be the right one, but the odds are actually twice as good if you switch

  • an activist shareholder can sometimes get a proxy on the ballot if he or she has enough time and money, but as pointed out by John Gillespie and David Zweig in Money for Nothing (How the Failure of Corporate Boards is Ruining American Business and Costing Us Trillions), examples are few and far between

  • even though no storms made landfall in Florida in 2011, this is Not a common occurrence

  • gas prices did consistently drop in the USA between September 2008 and December 2008, but have been otherwise steadily rising for the last five years

  • in some years the rice, sugar, and corn crops are almost the same as in the previous year, but given the increase in hurricanes, tsunamis, droughts, and other natural disasters in recent years, this is not a common occurrence

  • yes, heads can come up seven times in a row when flipping a fair coin, but the chances of this happening are less than 1%

In other words, you can forecast with Microsoft Excel, but your chances of doing well, especially given that 90% of spreadsheets have non-trivial errors (and collectively cost enterprises billions, as Fidelity and Fannie Mae found out), are (vanishingly) small (as the complexity of the forecast increases). One has to remember that there’s no intelligence behind a spreadsheet and they are just a source of peril that can cost your organization millions without anyone noticing.

Consumer Sustentation 74: Demand Planning

Demand Planning is a damnation. Why? As per our original damnation post,

  • traditional demand planning models require historical data
  • traditional demand planning models require market predictability
  • traditional demand planning models require market foresight
  • traditional demand planning requires knowledge of the expected price point

And how often in today’s constantly changing consumer marketplace, with new product releases coming faster and faster (to the point where your phone, laptop, and music device is out-of-date by a whole new release within a year), do you have good historical data, market predictability, and foresight? And how often can you be confident in the price-point, as a skunk-works product release by a competitor between sourcing and sale can force a price reduction to prevent inventory sitting on the shelves indefinitely.

So what can you do? (Besides burying your head in the sand like an ostrich?)

1. Get as much market data as you can.

Collect as much data as you can on your competitors imports, sales, and revenue using publicly accessible import data, analyst data, and company annual reports. It won’t be accurate, but with enough data you can often identify better trends than you could on the most similar product in your own inventory (which might not be similar, or recent, enough to be sufficiently relevant).

2. Have third parties conduct surveys on your behalf.

Sometimes the best way to gauge a market forecast is to actually conduct customer surveys and have a third party use the data to estimate demand for you. If you have no clue, the best thing you can do is admit it and get an expert to help you come up with a realistic demand forecast range.

3. Don’t focus a number, focus on a range and a potential rate of ramp-up or ramp-down.

If you know the demand is expected to be in the 100K to 200K units a month range, and the demand could double overnight, then you know that you need to contract for the low-end, but with a supplier that could ramp up to double production in a matter of weeks if necessary. And you have to negotiate a contract that allows orders to escalate, with pre-defined increases if the supplier is forced to work overtime (so you don’t get any billing surprises or animosity down the road).

4. Keep on top of sales data in real-time.

Be sure to get at least weekly PoS updates, and re-run the projections on a regular basis to detect an upswing or downswing early, so that you don’t get caught with your pants down, or, even worse, your pants off.

If you follow these tips, then you can get a reasonable grip on demand planning while your competitors flounder with the flounders.

Consumer Damnation 74: Demand Planning

Each group of customers are a damnation upon themselves, and they will get the attention they deserve, but demand planning to meet customer demand is its own damnation. Why is this?

Traditional demand planning models require historical data.

To be precise, they require a fair amount of historical sales or usage data in order to be accurate. And sometimes a lot of sales data. But with new product introductions coming fast and furious every day, there are so many categories without a decent amount, if any, historical sales data that it’s hard to make good predictions. Now, one can always use the most similar product, or the product the new product is expected to replace, but this weakens the model and the confidence in the result.

Traditional demand planning models require market predictability.

To be more precise, they expect that the market will not substantially change. That the needs will stay the same. The utilization or replacement curves will stay about the same. That a competitor won’t substantially increase or decrease their market share overnight. That a revolutionary new product won’t be released that causes a huge market shift.

Traditional demand planning models require market foresight.

In addition to requiring historical data and market predictability, traditional demand planning requires market foresight. Knowledge of potential competitor product introductions that could change the market demand. Knowledge of innovations that will begin demand shifts. Knowledge of general market conditions that could delay replacements or result in reduced demand due to cash availability.

Demand Planning requires knowledge of the expected price point.

Most products are services, especially in the end consumer market, are very price dependent. People will pay more if they perceive more value, which could be better quality, more functionality, or owning an iconic brand, but if they don’t perceive more value in your product which is priced higher than a competitor’s product, don’t think for a minute, even if they bought from you last time, they won’t shift. And price prediction is difficult if it is dependent on production cost, which can be variable if transportation can involve unpredictable fuel surcharges, raw material prices can skyrocket due to insufficient supply as a result of a disaster, and labour prices are dependent on contingent labour to meet demands at peak periods.

In other words, sometimes demand prediction models fall flat, and demand projections come from a place that can only be seen by a proctologist with a flashlight, so how do you effectively plan for those as a Procurement Professional? You don’t. It’s damnation.

Finally, a Prediction SI Can Get Behind!

By now, everyone should know how SI, and the LOLCats who live under the desks, feel about futurists and their predictions. (You need only scroll back to December 31’s post if you have forgotten.)

So, needless to say, as per prior years, SI is not going to be jumping on the prediction bandwagon (and risk getting trampled by fellow bloggers on the way) as the new year rolls in.

That being said, it has to give a shout out to one prediction from a fellow blogger who may just have it right. Specifically, Peter Smith of Spend Matters UK who, pressed for a prediction, made the amazingly accurate prediction that we predict that all predictions will be wrong.

SI could not have said it better if asked.

LOLCat approves!