Spendata: The Power Tool for the Power Spend Analyst — Now Usable By Apprentices as Well!

We haven’t covered Spendata much on Sourcing Innovation (SI), as it was only founded in 2015 and the doctor did a deep dive review on Spend Matters in 2018 when it launched (Part I and Part II, ContentHub subscription required), as well as a brief update here on SI where we said Don’t Throw Away that Old Spend Cube, Spendata Will Recover It For You!. the doctor did pen a 2020 follow up on Spend Matters on how Spendata was Rewriting Spend Analysis from the Ground Up, and that was the last major coverage. And even though the media has been a bit quiet, Spendata has been diligently working as hard on platform improvement over the last four years as they were the first four years and just released Version 2.2 (with a few new enhancements in the queue that they will roll out later this year). (Unlike some players which like to tack on a whole new version number after each minor update, or mini-module inclusion, Spendata only does a major version update when they do considerable revamping and expansion, recognizing that the reality is that most vendors only rewrite their solution from the ground up to be better, faster, and more powerful once a decade, and every other release is just an iteration, and incremental improvement of, the last one.)

So what’s new in Spendata V 2.2? A fair amount, but before we get to that, let’s quickly catch you up (and refer you to the linked articles above for a deep dive).

Spendata was built upon a post-modern view of spend analysis where a practitioner should be able to take immediate action on any data she can get her hands on whenever she can get her hands on it and derive whatever insights she can get for process (or spend) improvement. You never have perfect data, and waiting until Duey, Clutterbuck, and Howell1 get all your records in order to even run your first report when you have a dozen different systems to integrate data from, multiple data formats to map, millions of records to classify, cleanse and enrich, and third party data feeds to integrate will take many months, if not a year, and during that year where you quest for the mythical perfect cube you will continue to lose 5% due to process waste, abuse, and fraud, and 3% to 15% (or more) across spend categories where you don’t have good management but could stem the flow simply by identifying them and putting in place a few simple rules or processes. And you can identify some of these opportunities simply by analyzing one system, one category, and one set of suppliers. And then moving on to the next one. And, in the process, Spendata automatically creates and maintains the underlying schema as you slowly build up the dimensions, the mapping, cleansing, and categorization rules, and the basic reports and metrics you need to monitor spend and processes. And maybe you can only do 60% to 80% piecemeal, but during that “piecemeal year”, you can identify over half of your process and cost savings opportunities and start saving now, versus waiting a year to even start the effort. When it comes to spend (related) data analysis, no adage is more true than “don’t put off until tomorrow what you can do today” with Spendata, because, and especially when you start, you don’t need complete or perfect data … you’d be amazed how much insight you can get with 90% in a system or category, and then if the data is inconclusive, keeping drilling and mapping until you get into the 95% to 98% accuracy range.

Spendata was also designed from the ground up to run locally and entirely in the browser, because no one wants to wait for an overburdened server across a slow internet connection, and do so in real time … and by that we mean do real analysis in real time. Spendata can process millions of records a minute in the browser, which allows for real time data loads, cube definitions, category re-mappings, dynamically derived dimensions, roll-ups, and drill downs in real-time on any well-defined data set of interest. (Since most analysis should be department level, category level, regional, etc., and over a relevant time span, that should not include every transaction for the last 10 years because beyond a few years, it’s only the quarter over quarter or year over year totals that become relevant, most relevant data sets for meaningful analysis even for large companies are under a few million transactions.) The goal was to overcome the limitations of the first two generations of spend analysis solutions where the user was limited to drilling around in, and deriving summaries of, fixed (R)OLAP cubes and instead allow a user to define the segmentations they wanted, the way they wanted, on existing or newly loaded (or enriched federated data) in real time. Analysis is NOT a fixed report, it is the ability to look at data in various ways until you uncover an inefficiency or an opportunity. (Nor is it simply throwing a suite of AI tools against a data set — these tools can discover patterns and outliers, but still require a human to judge whether a process improvement can be made or a better contract secured.)

Spendata was built as a third generation spend analysis solution where

  • data can be loaded and processed at any point of the analysis
  • the schema is developed and modified on the fly
  • derived dimensions can be created instantly based on any combination of raw and previously defined derived dimensions
  • additional datasets from internal or external sources can be loaded as their own cubes, which can then be federated and (jointly) drilled for additional insight
  • new dimensions can be built and mapped across these federations that allow for meaningful linkages (such as commodities to cost drivers, savings results to contracts and purchasing projects, opportunities by size, complexity, or ABS analysis, etc.)
  • all existing objects — dimensions, dashboards, views (think dynamic reports that update with the data), and even workspaces can be cloned for easy experimentation
  • filters, which can define views, are their own objects, can be managed as their own objects, and can be, through Spendata‘s novel filter coin implementation, dragged between objects (and even used for easy multi-dimensional mapping)
  • all derivations are defined by rules and formula, and are automatically rederived when any of the underlying data changes
  • cubes can be defined as instances of other cubes, and automatically update when the source cube updates
  • infinite scrolling crosstabs with easy Excel workbook generation on any view and data subset for those who insist on looking at the data old school (as well as “walk downs” from a high-level “view” to a low-level drill-down that demonstrates precisely how an insight was found
  • functional widgets which are not just static or semi-dynamic reporting views, but programmable containers that can dynamically inject data into pre-defined analysis and dimension derivations that a user can use to generate what-if scenarios and custom views with a few quick clicks of the mouse
  • offline spend analysis is also available, in the browser (cached) or on Electron.js (where the later is preferred for Enterprise data analysis clients)

Furthermore, with reference to all of the above, analyst changes to the workspace, including new datasets, new dashboards and views, new dimensions, and so on are preserved across refresh, which is Spendata’s “inheritance” capability that allows individual analysts to create their own analyses and have them automatically updated with new data, without losing their work …

… and this was all in the initial release. (Which, FYI, no other vendor has yet caught up to. NONE of them have full inheritance or Spendata‘s security model. And this was the foundation for all of the advanced features Spendata has been building since its release six years ago.)

After that, as per our updates in 2018 and 2020, Spendata extended their platform with:

  • Unparalleled Security — as the Spendata server is designed to download ONLY the application to the browser, or Spendata‘s demo cubes and knowledge bases, it has no access to your enterprise data;
  • Cube subclassing & auto-rationalization — power users can securely setup derived cubes and sub-cubes off of the organizational master data cubes for the different types of organizational analysis that are required, and each of these sub-cubes can make changes to the default schema/taxonomy, mappings, and (derived) dimensions, and all auto-update when the master cube, or any parent cube in the hierarchy, is updated
  • AI-Based Mapping Rule Identification from Cube Reverse Engineering — Spendata can analyze your current cube (or even a report of vendor by commodity from your old consultant) and derive the rules that were used for mapping, which you can accept, edit, or reject — we all know black box mapping doesn’t work (no matter how much retraining you do, as every “fix” all of a sudden causes an older transaction to be misclassified); but generating the right rules that can be human understood and human maintained guarantees 100% correct classification 100% of the time
  • API access to all functions, including creating and building workspaces, adding datasets, building dimensions, filtering, and data export. All Spendata functions are scriptable and automatable (as opposed to BI tools with limited or nonexistent API support for key functions around building, distributing, and maintaining cubes).

However, as we noted in our introduction, even though this put Spendata leagues beyond the competition (as we still haven’t seen another solution with this level of security; cube subclassing with full inheritance; dynamic workspace, cube, and view creation; etc.), they didn’t stop there. In the rest of this article, we’ll discuss what’s new from the viewpoint of Spendata Competitors:

Spendata Competitors: 7 Things I Hate About You

Cue the Miley Cyrus, because if competitors weren’t scared of Spendata before, if they understand ANY of this, they’ll be scared now (as Spendata is a literal wrecking ball in analytic power). Spendata is now incredibly close to negating entire product lines of not just its competitors, but some of the biggest software enterprises on the planet, and 3.0 may trigger a seismic shift on how people define entire classes of applications. But that’s a post for a later day (but should cue you up for the post that will follow this on on just precisely what Spendata 2.2 really is and can do for you). For now, we’re just going to discuss seven (7) of the most significant enhancements since our last coverage of Spendata.

Dynamic Mapping

Filters can now be used for mapping — and as these filters update, the mapping updates dynamically. Real-time reclassify on the fly in a derived cube using any filter coin, including one dragged out of a drill down in a view. Analysis is now a truly continuous process as you never have to go back and change a rule, reload data, and rebuild a cube to make a correction or see what happens under a reclassification.

View-Based Measures

Integrate any rolled up result back into the base cube on the base transactions as a derived dimension. While this could be done using scripts in earlier versions, it required sophisticated coding skills. Now, it’s almost as easy as a drag-and-drop of a filter coin.

Hierarchical Dashboard Menus

Not only can you organize your dashboards in menus and submenus and sub-sub menus as needed, but you can easily bookmark drill downs and add them under a hierarchical menu — makes it super easy to create point-based walkthroughs that tell a story — and then output them all into a workbook using Spendata‘s capability to output any view, dashboard, or entire workspace as desired.

Search via Excel

While Spendata eliminates the need for Excel for Data Analysis, the reality is that is where most organizational data is (unfortunately) stored, how most data is submitted by vendors to Procurement, and where most Procurement Professionals are the most comfortable. Thus, in the latest version of Spendata, you can drag and drop groups of cells from Excel into Spendata and if you drag and drop them into the search field, it auto-creates a RegEx “OR” that maintains the inputs exactly and finds all matches in the cube you are searching against.

Perfect Star Schema Output

Even though Spendata can do everything any BI tool on the market can do, the reality is that many executives are used to their pretty PowerBI graphs and charts and want to see their (mostly static) reports in PowerBI. So, in order to appease the consultancies that had to support these executives that are (at least) a generation behind on analytics, they encoded the ability to output an entire workspace to a perfect star schema (where all keys are unique and numeric) that is so good that many users see a PowerBI speed up by a factor of almost 10. (As any analyst forced to use PowerBI will tell you, when you give PowerBI any data that is NOT in a perfect star schema, it may not even be able to load the data, and that it’s ability to work with non-numeric keys at a speed faster than you remember on an 8088 is nonexistent.)

Power Tags

You might be thinking “tags, so what“. And if you are equating tags with a hashtag or a dynamically defined user attribute, then we understand. However, Spendata has completely redefined what a tag is and what you can do with it. The best way to understand it is a Microsoft Excel Cell on Steroids. It can be a label. It can be a replica of a value in any view (that dynamically updates if the field in the view updates). It can be a button that links to another dashboard (or a bookmark to any drill-down filtered view in that dashboard). Or all of this. Or, in the next Spendata release, a value that forms the foundation for new derivations and measures in the workspace just like you can reference a random cell in an Excel function. In fact, using tags, you can already build very sophisticated what-if analysis on-the-fly that many providers have to custom build in their core solutions (and take weeks, if not months, to do so) using the seventh new capability of Spendata, and usually do it in hours (at most).

Embedded Applications

In the latest version of Spendata, you can embed custom applications into your workspace. These applications can contain custom scripts, functions, views, dashboards, and even entire datasets that can be used to instantly augment the workspace with new analytic capability, and if the appropriate core columns exist, even automatically federate data across the application datasets and the native workspace.

Need a custom set of preconfigured views and segments for that ABC Analysis? No sweat, just import the ABC Analysis application. Need to do a price variance analysis across products and geographies, along with category summaries? No problem. Just import the Price Variance and Category Analysis application. Need to identify opportunities for renegotiation post M&A, cost reduction through supply base consolidation, and new potential tail spend suppliers? No problem, just import the M&A Analysis app into the workspace for the company under consideration and let it do a company A vs B comparison by supplier, category, and product; generate the views where consolidation would more than double supplier spend, save more than 100K on switching a product from a current supplier to a lower cost supplier; and opportunities for bringing on new tail spend suppliers based upon potential cost reductions. All with one click. Not sure just what the applications can do? Start with the demo workspaces and apps, define your needs, and if the apps don’t exist in the Spendata library, a partner can quickly configure a custom app for you.

And this is just the beginning of what you can do with Spendata. Because Spedata is NOT a Spend Analysis tool. That’s just something it happens to do better than any other analysis tool on the market (in the hands of an analyst willing to truly understand what it does and how to use it — although with apps, drag-and-drop, and easy formula definition through wizardly pop-ups, it’s really not hard to learn how to do more with Spendata than any other analysis tool).

But more on this in our next article. For The Times They Are a-Changin’.

1 Duey, Clutterbuck, and Howell keeps Dewey, Cheatem, and Howe on retainer … it’s the only way they can make sure you pay the inflated invoices if you ever wake up and realize how much you’ve been fleeced for …

The Best Way Procurement Chiefs Can Create a Solid Foundation to Capitalize on AI

As per our recent post on how I want to be Gen AI Free, the best way to capitalize on Gen-AI is to avoid it entirety. That being said, the last thing you should avoid is the acquisition of modern technology, including traditional ML-AI that has been tried and tested and proven to work extremely well in the right situation.

That being said, if you ignore the reference to Gen-AI, a recent article on Acceleration Economy on 5 Ways Procurement Chiefs Can Create a Solid Foundation had some good tips on how to go about adopting ML-AI with success.

The five foundations were quite appropriate.

1. Organize

A plan for

  1. exactly where the solution will be deployed,
  2. what use cases it will be deployed for,
  3. how valid use cases will be identified, and
  4. how the solution is expected to perform on them.

There’s no solution, even AI, that can do everything. Even limited to a domain, no AI will work for all situations that may arise. As a result, you need a methodology to identify the valid use cases and the invalid use cases and ensure that only the valid uses cases are processed. You also need to ensure you know the expected ranges of the answers that will be provided. Then you need to implement checks to ensure that no only are only valid situations processed but that only output in an expected range is accepted in any automated process, and if anything is outside the expected norms anywhere, a human with appropriate education and training is brought into the loop.

2. Create a Policy

No technology should be deployed in critical situations without a policy dictating valid, and invalid, use. Moreover, any technology definitely shouldn’t be used by people who aren’t trained in both the job they need to do and proper use of the tool. Even though most AI is not as dangerous as Gen-AI, any AI, if improperly used, can be dangerous. It’s critical to remember that computers cannot think, and only thunk on the data they are given (performing millions of calculations in the time it takes an average person to perform two). As such, the quality of output is limited both to the quality of data input and the knowledge built into the model used. Neither will be complete or perfect, and there will always be external factors not considered, which, even if normally not relevant, could be relevant — and only an educated and experienced human will know that. (Moreover, that human needs to be involved in the policy creation to ensure the technology is only used where, when, and how appropriate.)

3. Understand Your Platform(s) of Choice

Just like there are a plethora of Gen-AI applications, a lot of different vendors offer AI applications, and even if most are similar, not all are created equal. It’s important to understand the similarities and differences between them and select the one that is right for your business. (Consider the algorithms and models used, the extent of human validated training available, typical accuracy / results, and the vendor’s experience in your use case in particular when evaluating an AI solution.)

4. Practice

Introducing new tools requires process changes. Before introducing the tool, make sure you can execute the associated process changes, first by executing training exercises on the different types of output you might get and then, possibly by way of a third party who uses a tool on your behalf, using real inputs and associated outputs. While the AI may automate more of the process, it’s even more critical that you respond appropriately to parts of the process that cannot be automated or where the application throws an exception because the situation is not appropriate to either the use of AI or the use of the AI output. (And if you don’t get any exceptions, question the AI … it’s not likely not working right! And if you get too many exceptions, it’s not the right AI for you.)

5. ALWAYS Ask Yourself: “Does that Make Sense?”

Just like Gen-AI hallucinates, traditional AI, even tried-and-true AI that is highly predictable, will sometimes give wrong results. This will usually happen if bad data slips in, if the use case is on the boundary of expected use cases, or the external situation has changed considerably since the last time the use case arose. Thus, it’s always important to ask yourself if the output makes sense. For tried-and-true AI where the confidence is high, it will make sense the vast majority of the time, but there will still be the occasional exception. Human confirmation is, thus, always required!

With proper use, AI, unlike Gen-AI (which fails regularly and sometimes hallucinates so convincingly that even an expert has a hard time identifying false results), will give great results the majority of the time — so you should seek it out and implement it. Just also implement checks and balances to catch those rare situations it doesn’t and put a human in the loop when that happens. Because traditional use-cases are more constrained, and predictable, it’s a lot easier to identify and implement these checks and balances. So do it … and see great success!

Last Friday Was International Women’s Day. You Made a Big Fuss. Well, What Did You Do This Week?

This is taken from a LinkedIn post the doctor posted on Monday, March 11. It’s being reposted here for those who don’t follow LinkedIn and because, as expected, he hasn’t heard a single peep from any organization that was spewing platitudes last Friday as if praise one day a year was doing enough.

If you truly celebrate women, then please tell me:

What are you doing TODAY to

  1. increase the number of women in Management, STEM, Executive Suites, and Investment Firms,
  2. close the pay gap that is still 15% to 30% across these areas,
  3. encourage women to join your company to pursue their career, and
  4. enable the work life balance they need to be AS or MORE successful than their male counterparts?

As most of you are probably well aware from the deluge of “we support and honour our female leaders who … ” posts on LinkedIn last Friday, International Women’s Day was last Friday (2024-Mar-08). I stayed silent, as usual, because I found the majority of them very upsetting.

While some of the posts were very sincere, and some came from individuals I know had the best of intentions:

  1. Lip service does nothing to address the four major issues above.
  2. The lip service I saw in some of these posts was about as meaningful as a token thank you card at the annual Christmas party.
  3. Few addressed the real issues women still face in “traditional” workspaces run, and dominated, by men.
  4. Those few that honoured teams with equal representation or greater, or at least statistically average representation (in companies in fields where women are currently only 25% of the workforce, like STEM) have done nothing to educate their peers on how important this is and how successful they are because of it.

If you are a leader in a company (with actual employees) that truly cares, then I challenge you to celebrate their achievements and capability every day, and once a month make a post on efforts your company is taking to increase the number of women, close the pay and rank gaps, and support their work life balance, either through hiring, training, support for community programs that do such or at least make a post on the stellar accomplishments they have accomplished that would put an average salesman to shame.

And to keep doing this until they have the equality, and the respect they deserve.

The simple facts are

  1. women are half the population,
  2. are just as capable of men (as there is NO difference between average IQ scores), and
  3. should be half the workforce.

If women are not half the workforce at your company (or at least not represented statistically in line with the average representation in the field your company is in), it’s not their lack of achievement, dear men, it’s yours!

The Public Sector is Giving Procurement Integrity A Bad Name … Can the Private Sector Fix It?

A recent article over on Global Government Forum on Procurement Integrity: A Big Problem That’s Worse Than Most Organizations Think, pointed out that errors, fraud and abuse in procurement cost governments and organizations millions of dollars every year, and even though recent headlines in the US (TriMark, Booz Allen Hamilton), UK (NHS, Royal Mail), and Canada (ArriveCan) are starting to shine the light on the extent of (public sector) procurement fraud, the problem is still bigger than you think. Much bigger.

Current estimates are that organizations, across the public and private sectors, lose 5% per year due to procurement errors, abuse, and fraud. Given that Global GDP is about 85 Trillion dollars, at 5%, that’s 4 TRILLION dollars estimated to be lost annually to errors, abuse, and fraud. And that’s probably a low-ball estimate due to the fact that we just calculated that Over One TRILLION dollars will be wasted on IT software and services due, primarily, to lack of knowledge and/or outright stupidity (and not malicious intent, but if it’s easy for consultancies and third parties to considerably over bill for legitimate goods and services that you need, imagine how much they are fleecing you for goods and services that you don’t need and may not even receive).

It’s highly likely that the true cost of errors, abuse, and fraud (internal, collusion, and external) is closer to 10% of total GDP, or close to EIGHT TRILLION. That’s at least twice the GDP of every country on the planet except China and the United States. That’s a BIG PROBLEM, which is definitely not being helped by the 100M to Multi Billion Procurement Frauds being reported almost monthly across major western economies — and multi-million dollar fines don’t repair the damage. (They don’t even come close.)

This is damage which Procurement needs to repair — because Procurement is the only department that has any hope of putting proper procedures, processes, and platforms in place to minimize the errors; training the organizational employees on proper procedures and monitoring the implementations to prevent abuse; and putting in place proper detection systems to detect, and prevent, potential fraud and quickly identify and track it when it happens.

Unless all the bucks go through, and stop at, a modern Procurement department run by a CPO who puts in place proper people, processes, and platforms, loss is going to continue to run rampant. Which means that while the public sector is failing us daily, the Private sector has to step up and restore the integrity of Procurement. It can start by utilizing some of the the techniques in the linked article, and continue by continually learning and implementing the best technology and processes it finds to not only uncover significant savings in inflationary times, but return integrity and trust into big business, and give governments who have lost their way a model to follow.

And for more details on Bad Buying to avoid, and how to achieve Procurement with Purpose, the doctor suggests you start by following the great public procurement defender, Peter Smith.

The Power of Optimization-Backed Sourcing is in the Right Sourcing Mix Across Scales of Size and Service

the doctor has been pushing optimization-backed sourcing since Sourcing Innovation started in 2006. There’s a number of reasons for this:

  • there is only one other technology that has repeatedly demonstrated savings of 10% or more
  • it’s the only technology that can accurately model total cost of ownership with complex cost discounts and structures
  • it’s the only technology that can minimize costs while adhering to carbon, risk, or other requirements
  • it’s one of only two technologies that can analyze cost / risk, cost / carbon, or other cost / x tradeoffs accurately

However, the real power of optimization-backed sourcing is how it can not only give you the right product mix, but the right mix across scales. This is especially prevalent when doing sourcing events for national or international distribution or utilization. Without optimization, most companies can only deal with suppliers who can handle international distribution or utilization. This generally rules out regional suppliers and always rules out local suppliers, some of whom might be the best suppliers of goods or services to the region or locality. While one may be tempted to think local suppliers are irrelevant because they will struggle to deliver the economy of scale of a regional supplier and will definitely never reach the economy of scale of a national (or international) supplier, unit cost is just one component of the total lifecycle cost of a product or service. There’s transportation cost, tariffs, taxes, intermediate storage, and final storage (of which more will be required since you will need to make larger orders to account for longer distribution timelines) among other costs. So, in some instances, local and regional will be the overall lowest cost and keeping them out of the mix increases costs (and sometimes increases carbon and risk as well).

When it comes to services, the right multi-level mix can lead to savings of 30% or more in an initial event. the doctor has seen this many times over his career (consulting for many of of the strategic sourcing decision optimization startups) because while the big international players can get competitive on hourly rates where they have a lot of resources with a skill set, when it comes to services, there are all in-costs to consider, which include travel to the client site and local accommodations. The thing with national and international services providers is that they tend to cluster all of their resources with a certain skill set in a handful of major locations. So their core IT resources (developers, architects, DBAs, etc.) will be in San Francisco and New York, their core Management consultants will be in Chicago and Atlanta, their core Finance Pros in Salt Lake City and Denver, etc. So if you need IT in Jefferson City, Missouri, Management in Winner, South Dakota, or accounting in Des Moines, Iowa, you’re flying someone in, putting them up at the highest star hotel you have, and possibly doubling the cost compared to a standard day rate.

However, simple product mix and services scenarios are not the only scenarios optimization-backed sourcing can handle. As per this article over on IndianRetailer.com, retailers need to back away from global sourcing and embrace regional (and even local) strategies for cost management, supply stability, and resilience. They are only going to be able to figure that out with optimization that can help them identify the right mix to balance cost and supply assurance, and when you need to do that across hundreds, if not thousands, of products, you can’t do that with an RFX solution and Microsoft Excel.

Furthermore, when you need to minimize costs when a price is fixed, like the price of oil or airline fuel, you need to maximize every related decision like where to refuel, what service providers to contract with, how to transport it, etc. When it can cost up to $40,000 to fuel a 737 for a single flight (when prices are high), and you operate almost 7,000 flights per day with planes ranging from a gulf stream that costs about $10,000 to refuel to a Boeing 747 that, in hard times, can cost almost $400,000 to refuel, you can be spending $60 Million a day on fuel as your fleet burns 10 Million gallons. Storing those 10 Million gallons, transporting those 10 Million gallons, and using that fuel to fuel 7,000 planes takes a lot of manpower and equipment, all of which has an associated cost. Hundreds of thousands of associated costs per day (on the low end), and tens of millions per year. Shaving off just 3% would save over a million dollars easy. (Maybe two million.) However, the complexity of this logistics and distribution model is beyond what any sourcing professional can handle with traditional tools, but easy with an optimization backed platform that can model an entire flight schedule, all of the local costs for storage and fueling, all of the distribution costs from the fuel depots, and so on. (This is something that Coupa is currently supporting with its CSO solution, which has saved at least one airline millions of dollars. Reach out to Ian Milligan for more information if this intrigues you or how this model could be generalized to support global fleet management operations of any kind.)

In other words, Optimization-Backed Sourcing is going to become critical in your highly strategic / high spend categories as costs continue to rise, supply continues to be uncertain, carbon needs to be accounted for, and risks need to be managed.