Category Archives: Spend Analysis

There’s More Than 50 Ways …

… to leave your lover. There’s more than 50 shades of grey (as there is infinite intensity to grey-scale). And there’s certainly more than 50 shades of pay … (see: 50 shades of pay spend analysis many profitable pleasures)

Over on Spend Matters, Pierre Mitchell is penning a series on 50 Shades of Pay: Spend Analysis’ Many Profitable Pleasures where he notes that spend analysis is not a quickie event and nothing could be closer to the truth. Spend Analysis is an on-going process that never ends. There’s always new spend, always new quotes, and always new ways to look at data. It’s wham, bam, spend cube and start all over again. And again. And again.

And it must be an evolving competency that is refined over and over again. There are many reasons for this, and, as Pierre pointed out,these include the facts that:

  • You can’t manage and improve what you cannot see
    and the more you see, the more you manage, and the more you’ll manage, the more you’ll see …
  • It’s a fundamental part of corporate strategy as the more you see and manage, the better you’ll be able to manage your resources and opportunities, which is what corporate strategy should be focussed on.
  • Managing your spending includes internal spending too even if you can’t control it, you still need to understand what it is, where it is going, who controls it, what could be done about it, and what the recommendations should be.
  • Spend analysis is a gift for your partners – not an IT project because it really is decision support for the organization.
  • Spend is the flip side of supply and it is about maximizing bang (supply value) for the spent buck (spend magnitude). It’s the other end of the source-to-settle process. And to optimize your Supply Management, you need to optimize your entire source-to-settle process.
  • Finance will get even more turned on by spend analysis than you if you do it right. They like shiny reports — and a good spend analysis solution can produce them en masse. (The reporting engine is actually the least useful part of a good spend analysis tool, but just like Sonny goes cuckoo for cocoa puffs, finance and the C-Suite love their reports.)
  • Spend analysis shines a bright light on the master data problems which can be cranked up to the point that it’s blinding. Some people may be embarrassed at the mess master data is in (because they spent millions on a broken ERP), but what’s more import, their pride or your bonus (which requires the company to be profitable)?
  • It’s incremental in nature – the Trojan rabbit of procurement transformation. Just like the rabbit of Caerbannog, it looks cute and sweet, but, in the right hand, spend analysis is a vicious killer of inefficiency and waste.

If Pierre manages to write 50 pieces, it will shape up to be a great series for those of you who have Plus membership. For those of you who don’t, I’ll remind you that SI co-wrote the book on Spend Visibility with Lexington Analytics 3 years ago, and this book, which has garnered over 10,000 downloads since its release, is still available for free. I’m sure Pierre will get to more advanced topics in the later part of his series then what we covered in the book, but it’s a great start. And maybe by then you can convince your boss to pay for the Plus membership to read Pierre’s posts.

Could You Be Doing It Right? Part III: Big Data

In last Friday’s post, we asked if you were doing it wrong. In particular, we mentioned category management, supply chain risk monitoring, and big data, and asked if you were doing them wrong. We noted that even though a number of companies have jumped on these runaway bandwagons, most have yet to grasp the reigns and take control of the wagon and get it on the right track.

Why is that?

Fundamentally, it’s the same reason that there are no world class Procurement Organizations in Asia Pacific — the classic Triple-T problem.

  • Talent
    the organizations don’t have the right talent to properly manage the initiative
  • Technology
    the organizations don’t have the right platforms to capture the right data and support the right processes
  • Transition Management
    the organizations don’t have the right processes in place to handle the necessary organizational shift to properly manage the initiative

Once the talent, technology, and transition management is in place, the organization has what it needs to fully embrace the initiative and take it to the next level. And do it right.

Where should your Supply Management Organization start? By identifying the core capabilities that are required in each “T” category and finding the right talent, technology, and transitions management for the initiative, the organization will be well on its way.

In the rest of this post, we’re going to talk about the requirements for an organization to get on the right category management track.

Talent for Big Data

Good big data scientists need the following hard and soft skills:

  • Algorithms
    there’s no magic algorithm where big data is concerned as every problem is unique and requires a unique (variant of an) algorithm
  • Domain Knowledge
    the scientist needs to know when she can be confident in the data and when she can’t; if there is not enough data, or the data is too random or skewed from expected patterns, then the scientist needs to know to trust judgement over data
  • Technical Skills
    the scientist needs to use sophisticated tools to perform her analysis
  • Logic
    the data, and algorithms, are very precise and the data scientist needs to be as well
  • Teaching
    since the majority of organizational employees will not understand what the big data scientist does, she will have to be able to explain what is needed data-wise, what the meaning of the results are, and how confident the organization can be in the results in simple terms
  • Perseverance
    since big data isn’t as simple as just dumping a bunch of data into an algorithm and accepting the result; the first, second, and tenth try won’t always generate a useful result — sometimes the data scientist, like an archaeologist, has to dig, dig, dig

Technology for Big Data

Appropriate technology platforms for big data will have at least the following features:

  • Big Data Stack
    You need an infrastructure that is scalable, replicable, and fault-tolerant.
  • Domain Specific Algorithms
    That can run on the stack and analyze the right data in the right way to generate some useable facts.
  • Powerful Reporting Engine
    That can not only generate reports useful to the scientist but to others in the organization.
  • Powerful ETL Middleware
    As you will need to extract, transform, and load data from a wide variety of sources.

Transition to Big Data

In order to transition to an organization that properly uses big data, the organization needs to hire someone with good change management skills and give that person the tools and C-suite support he or she needs to get it done. That person also needs to be a natural born leader and someone who can work with teams to get it done.

This isn’t a complete (laundry) list of what is required for big data, but it’s a good starting point. Get the right talent, technology, and transition management in place, and your organization will be well on its way to big data* success.

* Especially if you hire a good big data scientist who recognizes that sometimes the data doesn’t have to be all that big to derive a useful fact!

Are You Doing It Wrong?

If you’ve been following the media, you know that we have reached a point were most major business publications are now putting focus on Supply Chain as your top risk and your top opportunity.

You also know that these same publications, and the solution providers that follow, and reference them, have been preaching the following solutions to not only tame the risk but increase the opportunity.

Comprehensive Category Management

Spot buying individual categories at market lows or evening running reverse auctions at opportune times is not category management. And for that matter, neither is an event that covers the entire category. At this point you probably think that the doctor is losing it a little, because how could it not be category management if you are addressing the whole category?

It’s Simple. Category Management isn’t just about grouping all seemingly related items and running an event, it’s grouping items that have related characteristics that allow the items to be sourced effectively under the same strategy. For example, while it might make theoretical sense to group printers, ink, and paper together — because you use them together, from a sourcing point of view, ink and paper often go better with office supplies and printers with hardware. You can probably get them thrown in for free with a server purchase. But that’s just the start. If you source a lot of metal parts, you should probably group them by primary metal, since the price of steel, aluminum, etc. will largely dictate their prices and it might even make sense to not only source all of the parts from the same supplier but even buy the metal on behalf of the supplier with your better negotiating power and/or credit rating.

Supply Chain Risk Monitoring

Natural and Man-Made disasters devastate supply chains when they result in raw material or product unavailability for weeks or months. When a company doesn’t understand their dependence on a single source or the risks that single source is subject too, they can figuratively get caught with their pants down to say the least.

As a result, most leading companies in the Risk Management arena are now tracking and monitoring their tier 1 supply base for not only missed deliveries, but late shipment dates and inquiring immediately when something is late shipping. However, by the time a shipment is late, it’s often too late to go to another source if the reason for the lateness is the lack of an important raw material. So the smarter companies also ask their suppliers to let them know when their suppliers miss a delivery. This is better, but sometimes this is still too late. You need to track the primary sources of the raw material and their ability to produce. Not only the companies, but their locations. All natural and man-made disasters in the region and then evaluated for impact and if the producer of the primary raw material or part is potentially at risk, they make sure, or ask their tier 1 supplier to make sure, that the raw material or product can still be delivered on time and if it can’t, these leading companies immediately seek a secondary source (or lock up available supply pre-emptively) — not two weeks after the tier 1 supplier required the raw material to meet the commit date.

Big Data

The only buzzword on par with big data is cloud. According to the converted, or should I say the diverted, better decision are made with better data — the more data the merrier. This sounds good in theory, but most algorithms predict demand, acquisition cost, projected sales prices, etc. based on trends. But these days the average market life of a CPG product, especially in electronics or fashion, is six months or less, and the reality is that there just isn’t enough data to predict meaningful trends on. Similarly, every disruption impacts the cost, and these disruptions are as unpredictable as future sales predicted using trend models with insufficient data.

You use all of the data available to validate your operations, procurement, and financial situation. Not to blindly predict future sales or prices. An over-reliance on big data is often more dangerous than not having data at all.

Spend Analysis – How Do You Get It Right?

As an expert in optimization and analytics, I’m regularly asked, with respect to spend analysis, where do I start, which solution is best, how do I get fast savings, etc. All good questions, and as per the dozens of posts on this subject over the years, they all have multiple answers, highly conditional on the skill of the analyst, the solutions available, the data available for the solutions to act on, the categories that can be impacted in the short term (as some contracts are effectively unbreakable and some vendor contracts all but prevent cost recovery), etc.

More sophisticated analysts ask what built-in reports are necessary and the most useful, how much real-time slicing and dicing capability they really need, how much can be automated, etc. Also good questions, but questions which, depending on the particulars of the situation, also have multiple answers which are dependent on what the organization is buying, who the organization is buying from, where the products and services are coming from and going to, how much data is available with respect to the product and service cost components and composition, how powerful the data amalgamation and computation engines are, how variable their spend patterns are, etc.

Which leads many people to believe that it’s almost impossible to get it right, when the reality is that, with the right type of tool and the right mind set, it’s easy to find success, but very hard to predict where and when you will find success. It’s like being a skilled professional during a market boom where the demand for individuals with your skills outstrips supply. While you don’t know who will offer you a job, if you have good references, work hard, and apply yourself to finding a job, offers will come. Spend analysis often works the same way – if you have the right tools, work hard, and apply yourself to finding opportunities, they will present themselves. It’s just a matter of digging until they materialize.

As I’ve said time and time again, you’re not likely to find your best opportunities in the canned top-N reports which for many years were the staple of spend analysis solution vendors. Because, as I’ve pointed out for years, if you go to manufacturing and ask a handful of buyers who the top 10 suppliers are, you’re going to get a set of overlapping lists which will easily allow you to identify the top 7 or 8 suppliers. The same for categories and commodities. And the AP system tells you who the top departmental and individual spenders are. You may not know the exact spends or exact volumes, but you can quickly get a pretty good idea and focus your negotiation efforts on those categories while you work towards improving your data. The net result is that these reports will only identify a few opportunities that you missed, which you will quickly identify, attack, and capture within 6 to 18 months. That’s why the year-over-year return of most spend analysis efforts falls flat within two years. The biggest opportunities are not in what you know, they’re in what you don’t know. They’re somewhere in the next 20 or 30 suppliers or the next 20 or 30 commodities that can be consolidated into moderately high spend categories that when effectively rationalized and sourced can deliver 10%, 20%, and even 30% savings as well as process efficiencies that deliver further organizational savings still.

A consolidation that will only happen if you can define category groups and supplier families and analyze collective spend and compare it to market rates and identify patterns of overspending not detectable using only top n spend reports or traditional organizational categorizations and supplier codes (where the same supplier has four different entries in the vendor master because, as a non-top n supplier, no one bothered to make sure all spend associated with that supplier was with a single vendor entry in the ERP). And you can only do this if you can slice, dice, and rearrange the data on the fly in non-traditional ways only you, as a creative intelligent being, can do. A rule-based system can only check for previously identified transgressions. It can’t check for unknown transgressions or future transgressions by organizational buyers.

So if you want a short answer to the question of how do you get spend analysis right, you make sure you buy a tool that gives you a lot of different flexibility in the amalgamation, organization, and analysis of the data available to you. It should let you build, in technical terms, “multiple cubes”, allow you to create multiple roll-ups and reports on these cubes, using multiple analysis techniques. It should let you build a cube, appropriate roll-ups, and reports that answer any question you care to ask. If you see a spending pattern or trend that is not in line with what is expected, you need to be able to dive into the data and find out why. That’s how you save money. Flexibility, a toolkit of techniques, and the creativity to apply them in different ways until you stumble on the big savings or value generation opportunity that everyone else has missed.

Analyzing Indirect Spend … The Key To Success is to …

Over on Purchasing Insight, your blog-master extraodinaire, Pete Loughlin, recently ran a two part series on Analyzing InDirect Spend (Part I and Part II) from Michael Wydra of REL Consultancy.

In his two-part series, Michael correctly notes that it is often the case that indirect spend areas provide higher improvement potential that is often easier to realise. For most companies, this is non-strategic spend that is easy to overlook, but the lack of oversight often results in these categories not being managed in a professional manner, resulting in a lack of visibility and control. This can be very costly to a company as indirect spend typically accounts for 13.5% to 22% of revenue, depending on the industry. (If indirect spend is 20% of revenue, and the savings opportunity is 10%, the organization can quickly shave 2% off of the top by tackling indirect spend. If direct spend has been carefully managed for years, chances are the direct spend savings opportunity is only 3%. Even if direct spend is 50% of total spend, that indicates that the total savings opportunity on direct spend is a mere 1.5%, making indirect spend more valuable.)

According to Michael, the first step on getting a handle on indirect spend is a proper spend analysis — which might indicate that the spend is spread over thousands of suppliers with a high number of different payment terms, which adds an additional layer of complexity (that is often not necessary). One of the reasons this is important is that, on average, 12% of negotiated savings on indirect spend categories is lost because contracted rates were not adhered to.

This spend analysis should identify opportunities for cost reductions that are sustainable and that facilitate monitoring spend, improve supplier relations, lower transaction costs, and align service levels. If the right opportunities are identified, and the right programs are put in place, a company can become world-class in indirect spend management — and realize, on average, 45% lower indirect procurement process costs than its peers in addition to lower product and service costs.

Sometimes savings opportunities will be obvious — a dozen different suppliers across the country for janitorial supplies when one will suffice, no contract for toner cartridges and no standardization on office printers to allow bulk buys, and temp labour not measured against standard rate cards. But some opportunities will be less obvious — such as two of twelve offices, in the top four spenders, not switching to the new cell plan and overspending by tens of thousands, not matching invoices to rate cards for IT services, and not capturing the annual rebates from the office supply vendors. To find these opportunities, you have to dig, dig, dig — just like an archaeologist.