Category Archives: Spend Analysis

Contract Compliance Trust But Verify: Part III Monitoring Demand

Today’s post is from Eric Strovink, the spend slayer of spendata. real savings. real simple. Eric was previously CEO of BIQ; before that, he led the implementation of Zeborg’s ExpenseMap, which was acquired by Emptoris and became its spend analysis solution.

When you join transaction data to contract data in order to validate contract price compliance, it is possible to discover lots of interesting information. Some if it can be quite surprising.

For example, you might notice that off-contract items make up a surprisingly large proportion of the spending. This may be trending up with time, so it is worth doing a time-series analysis. You might also notice a pattern of overcharges on particular items, which could be an easily-corrected disconnect at the vendor side on contract terms.

In Excel, these analyses require new pivot tables and, concomitantly, more maintenance effort on refresh. But in a spend analysis system, the model can be augmented with additional pivot-table-equivalents in seconds, with just a few mouse clicks. And, refresh is not an issue, because the spend analysis system updates everything automatically upon loading new transactions. So, much more interesting analyses become real possibilities — including monitoring demand.

The Who

Suppose that we have from the vendor not only the item pricing, but also an idea of who within the organization is doing the purchasing. This then enables us not only to identify off-contract spending, but also find the source of the leakage within the organization, so that corrective action can be taken internally.

There are a number of ways that “Who bought the items” can find its way into PxQ data. Sometimes it is present as a matter of course; sometimes it requires effort.

  • If the item is a catalog buy or punch-out, invoice items likely already contain the cost center.
  • If a PO number was provided to the vendor, invoice items should contain the PO. The PO can be easily translated to cost center (well, “easily” if the PO data can be linked in, as it can be with a spend analysis system).
  • If there’s a useful delivery address on the invoice, that can be mapped to a cost center using the spend analysis system’s mapping tools (of course, you need access to the mapping tools, and they need to be simple to use).
  • Your contract with the vendor could require a cost center to be provided on the invoice as a prerequisite for payment. No cost center, no payment.
  • Corporate purchasing cards are by definition associated with a cost center, so these can be mapped to cost center using the spend analysis system’s mapping tools.
  • Consultants put project codes on invoices; lawyers put matter numbers. These can be mapped to cost centers as well. Any invoice without a project code or matter number shouldn’t be paid.
  • Some spend already has a fixed cost center, for example with copiers. Each copier is assigned a cost center, which shows up on the invoice.

In a nutshell, if you want to have a cost center attached to each row of an invoice, it is very doable, and very worthwhile.

Let’s revisit the dashboard from Part II.

  • We can see a breakdown of overcharge buys by cost center (blue). A similar breakdown of off-contract items helps identify who is buying off-contract. There may be very good reasons for this, of course; and those reasons need to be understood, so that we can either get those items onto the contract, or channel the buying to similar items that are on contract.
  • We can see a time-series analysis of item buys by class, with an associated chart (red). Over time, fewer items are being bought with the contract price, which is not a good trend.
  • We can see all the buys, showing both contract and overcharged prices (green). This is all we need to show to the vendor — just dump it to Excel, email the spreadsheet, done.

Click to enlarge

The basic pattern of this type of analysis doesn’t change with the commodity. Providing that the goods or services can be standardized with a fixed price, and that a contract price is available, the technique is always the same — and the analysis always worthwhile, if only to prove that the contract is in place and actually working.

Thanks, Eric!

Contract Compliance Trust But Verify Part II: Monitoring the Vendor

Today’s post is from Eric Strovink, the spend slayer of spendata. real savings. real simple. Eric was previously CEO of BIQ; before that, he led the implementation of Zeborg’s ExpenseMap, which was acquired by Emptoris and became its spend analysis solution.

If you have a contract with a vendor, you should be paying the contract price. But until you check, you don’t really know — and what you find out may surprise you.

In Part I of this series we discussed the two pieces of data required — transactions from the vendor, and contract prices for the items under contract. The next step is to join those two datasets together, in this case by Part Number.

Here is what that might look like if we do it in Excel:

This was done by:

  • Sorting the contract prices by Part Number so VLOOKUP will work
  • Building a helper column K which is the difference between invoice price and VLOOKUP’d contract price (hidden)
  • Building a VLOOKUP to compare contract price to invoice price (shown)
  • Building a Pivot Table to roll up column L

Lots more could be done. For example, we could:

  • Add a computation of the amount of overcharge.
  • Add year-month to the pivot table, giving us an idea as to the distribution of the overcharges. Have they all occurred recently, or just in the relatively distant past?
  • Produce a table of only the overcharged items, in order to send it to the vendor with a request for compensation.
  • Identify “who” is buying the excluded items (more on this in Part III).

However, as the model becomes more complex, it becomes more difficult to maintain. What happens next month, when a new tranche of transactions is available? Who updates the model? Each of the formulas and pivot tables needs to be updated carefully — a process that’s irritating and time-consuming at best, as well as highly error-prone.

Make it Easy, not Hard

A spend analysis tool can make this a lot easier. Load the two datasets, and link them by Product Number. Then build a price difference column, set up a range, and you’re done. This requires no advanced Excel knowledge, and produces a model that updates automatically when new data are added. This dashboard was put together using Spendata, but there are certainly other options.

Click to enlarge

And now, adding next month’s data to the analysis is anticlimactic — literally a couple of clicks, and everything auto-updates. So, even if you could “do it in Excel”, you won’t, because it’s just too painful. But if you use the right tools, you can produce compliance models quickly, and you can maintain them with near-zero effort.

We’ll conclude our discussion in Part III: Monitoring Demand. Thanks, Eric!

Contract Compliance Trust But Verify Part I: Compliance Data

Today’s post is from Eric Strovink, the spend slayer of spendata. real savings. real simple. Eric was previously CEO of BIQ; before that, he led the implementation of Zeborg’s ExpenseMap, which was acquired by Emptoris and became its spend analysis solution.

If you have a contract with a vendor, that’s good news — you’re not paying list prices any more. At least, that’s what should be happening.

It’s fascinating what can really happen. We’ve recently seen a vendor raise prices in a distant region while maintaining contract prices in the headquarters region. This and similar disparities aren’t necessarily deliberate — mistakes can be made by anyone. Even items purchased through an e-procurement system can fall off the price-compliance applecart as a result of exception-handling processes. The lesson is that “Trust but Verify” is a necessity, not a nicety. And, since manual inspection of a large volume of items and invoices is impossible, this process must be mechanized.

The good news is that many goods and services can be standardized with a fixed price. These items can easily constitute 25-30% of spending. For these goods and services, contract compliance is (at least conceptually) straightforward. Examples include physical items, such as computers, office supplies, phones, furniture, MRO parts, facilities supplies, vending items, security equipment, mobile phone plans, stationery and forms, promotional items — even some types of software. Services examples can include cleaning, appraisals, training classes, recruiting, records management, armored car, overnight mail, hotel, and car rentals (when they are for a fixed unit of time or work).

If contract compliance for these goods and services is straightforward, why doesn’t everyone do it? As usual, the devil is in the details.

  1. Who builds the (usually spreadsheet) compliance model?
  2. Does the model show who is buying off-contract items from the vendor? Which items? When?
  3. Who loads next month’s data into the model, and adapts it accordingly? What’s the cost of this, versus the payback?

For these questions, invoice data, aka Price X Quantity (PxQ) data, is required.¹

Acquiring Data

PxQ data is best acquired directly from the vendor. It’s your data; you have a right to it; and you’ve a right to ask for it. Many vendors will supply it in a reasonable format, such as in an Excel spreadsheet, or as a CSV or DSV file. Some vendors, though, will attempt to discourage you by providing data in an unreasonable format — for example, by supplying every invoice they’ve sent, in PDF format, as an individual file (don’t laugh; we’ve seen this). You may want to consider whether doing business with that vendor is in your best interest moving forward. Certainly you should write into any future contract that the vendor must provide PxQ data in a reasonable format.

But, you also need contract data — that is, contract price by item. That data is probably already in a reasonable format, for example as an addendum to the contract. At worst, it can be keyed in manually or minimally edited into shape.

So, there are two datasets to consider. The first, consisting of invoice level PxQ data, comes from the vendor and resembles this:

Click to enlarge

The contract pricing, which you should already have, resembles this:

Click to enlarge

Once you have the data in this form, you can easily figure out whether the contract is leaky or solid. We’ll continue this discussion in Part II, Monitoring the vendor.

Thanks, Eric!

¹Accounts Payable-based spend analysis can help to determine what spend is definitely not under contract. But it is helpless to address contract compliance issues.

Do You Need Spend Analysis?


No. You don’t need spend analysis.


And don’t say you can’t afford it. Given that Spendata offers a single user annual license to a best-in-class do-it-yourself tool for $699, you can afford it. And when you consider companies like Spendency offer enterprise do-it-yourself solutions starting at the 3K/month price point BIQ used to start at (and SpendHQ isn’t that much more per month with their entry level offering) and once you set up the mappings, you’re set to go, you can. Especially when you can use it to identify an average savings of 10% year over year.

And don’t tell me that do-it-(mostly)-yourself is not an option. It always is! You just need a bit of training. And that can be obtained at an affordable price point as well. Contact Data-TrainingWorx limited about their SpendataWorx program, which can include an LMS consisting of 40 online interactive videos and over 800 two page “microbite” documents that is everything you need to know to get started and analyze your data … for years!

Remember, you need it, you can afford it, so just get it, and just do it.

Are You Ready to Get Analytical But Don’t Know How? Read On!

Now that you’ve read our last three posts and understand that you need to get more analytical if you want to get cognitive, hopefully you’re ready to dive deeper but just don’t know how to do that.

The four part answer is almost as easy as it was for optimization, just a bit more nuanced. What’s the nuance? Figuring out if your provider offers a modern spend analytics platform or is still a generation (or two) behind (when you are still behind yourself) is the nuance. So how do you determine if a vendor at least passes the sniff test? We’ll get to that, but first, let’s talk about where you start.

At a high-level, the four-part answer is almost the same as optimization. Just the vendor names change.

1) If you are using a sourcing or analytics platform from a modern provider with modern (next generation) analytics capability, use it (and acquire the module if necessary).

Who are the vendors? While we can’t say this list is thoroughly exhaustive, if you look at Spend Matters Deep Solution map, you see that the following providers make the map: AnyData, (SAP) Ariba, (Opera) BIQ, GEP, iValua, Jaggaer, Sievo, Simfoni, SpendHQ, Synertrade, and Zycus. Not all are equal, and this list is likely not exhaustive, but depending on your organizational needs, a sub-set of these providers is likely your starting point. (What Sub-Set? Depending on whether you are data, function, process, technology, configurability, or services oriented, the sub-set will vary. And practitioners who want to know which vendors match which subset can contact Spend Matters.) And if you are a do-it-yourself type, you could probably start with a platform like Spendata.

2) If you are not using a modern analytics platform or a modern sourcing platform with analytics, get a modern analytics platform or a modern sourcing platform with analytics, your choice.

Again, you can start with the dozen of providers above, which you can quickly narrow down depending on whether you prefer best of breed or sourcing suite and whether you favour technical orientations or service orientations. If the list is still too large, find the subset that bests fits your organizational size, industry, category focus, geography, and culture and focus in on those.

3) If you are using another sourcing or analytics (reporting) platform that is not meeting your needs, and can replace it, do so.

As with the optimization providers, a few of these providers have a considerable portion of their customer base that consist of customers that switched from another provider with a solution that didn’t meet their needs and, thus, have a lot of experience with change management, fear squashing, migrating your data over, and getting you up and running on the right processes quickly. Simply craft the right RFI and you will quickly zero in to the handful of providers that will likely be the best fit for your situation.

4) If you are using another sourcing platform or reporting platform that is otherwise meeting your needs, or can’t be replaced at the present time, or both, augment it with a pure-play deep-dive best of breed modern analytics solution.

So if you are in the situation that you just bought a best of breed Source-to-Contract or Source-to-Pay solution and can’t replace it, or you have a first generation BI tool that produces reports the executives love but doesn’t meet your needs, augment it with a point-based best of breed solution. From the above list,
AnyData, (Opera) BIQ, Sievo, Simfoni, SpendHQ, and Spendata fit that bill.

But what about the “sniff test”?

How do you differentiate a last generation solution from a current generation solution? Three tests. Have them, in front of you, in a live demo:

  • Build a Cube with Derived Dimensions and a new Report on the Cube on the Spot
    if they can’t do so (in 15 minutes), they are a last generation platform that can only work on pre-defined and pre-built OLAP cubes
  • Run a categorization exercise on at least 3 months of your transaction history / invoice data and at least 100,000 transactions
    if they can’t either use their AI, or powerful (collaborative) filtering and priority based rule definition, and get to the 95% mark in an hour, it’s not for you … (and, trust me, you don’t need AI to get to the 95% mark if the rule definition capability is appropriately defined)
  • Map the cube to a new taxonomy, create new derived dimensions, and create a set of filters that will allow comparison reports to be run between the cubes
    let’s face it, there is no one size fits all taxonomy for analysis, and this is the kicker test to see if the platform can support any taxonomy that is needed, run any analysis you want, and allow you to run comparison reports both as checksums and as differentials to figure out where the opportunities are hidden

All this should take less than a morning or afternoon. But it means the provider deserves to be on your short list.