Category Archives: Guest Author

Spend Analysis III: Crosstabs Aren’t “Analysis”

Today’s post is from Eric Strovink of BIQ.

Pivot tables are great, no question about it. They’re also a pain in the neck to build, so any tool that builds a crosstab automatically is helpful. But crosstabs are where many “spend analysis” systems declare victory and stop.

Pivot tables are most useful when built in large clusters. Hundreds of them, for example, booked automatically down some dimension of interest (like Cost Center by Vendor, booked by Commodity). They’re also best when they’re created dynamically, inserted into existing Excel models, with the raw data readily available for secondary and tertiary analyses.

It’s also useful to see a breakdown of all dimensions by a single dimension — i.e., hundreds of thousands, even millions, of pivot table cells calculated automatically on every drill. For example, here’s MWBE spend, broken down against each of 30,000 nodes, re-calculated on every drill.


Click image to enlarge

Not to belabor the point, but there’s a big difference between (1) dumping a single crosstab to an HTML page, and (2) inserting hundreds of pivot tables into an existing Excel model, or calculating 120,000 crosstab cells automatically on every drill. The former is interesting. The latter supports serious analysis.

Are pivot tables the most useful way to present multidimensional data? Often, they aren’t. The Mitchell Madison Group’s Commodity Spend Report books top GL accounts, top Cost Centers, and top Vendors by Commodity, with a monthly split of spend, showing share of total category spend at each display line. Is a static report like this one “analysis?” No, of course not. But in this case the multi-page “report” isn’t static at all. It was built with a simple extract from the dataset, inserted directly into a user-defined Excel model. The output is trivially alterable by the user, putting analysis power directly into his or her hands. For example, with a simple tweak the report could just as easily be booked by Vendor, showing Commodity, Cost Center, and so on — or adapted to an entirely different purpose.

What about matching externally-derived benchmarks to internal data? Is it useful to force-fit generic commodity benchmark data into an A/P dataset, as some spend analysis vendors try to do, and pretend that actionable information will result? Or is it more productive to load relevant and specific benchmark data into a flexible Excel model that you control, and into which you insert actuals from the dataset? The former approach impresses pie-in-the-sky analysts and bloggers. The latter approach produces concrete multi-page analyses, like this, that demonstrate how “best price” charged for an SKU, per contract, might not be “best price” after all (who ever heard of a PC whose price was flat for 12 months?)1


Click image to enlarge

Next installment: User-Defined Measures

Previous Installment: Why Data Analysis is Avoided

1 This example is based on disguised but directionally accurate data. A similar analysis on actual data identified hundreds of thousands of dollars in recoverable overcharges.

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Spend Analysis II: Why Data Analysis Is Avoided

Today’s post is from Eric Strovink of BIQ.

If I have learned one thing during my career as a software developer and software company executive, it’s this: contrary to what I believed when I was a know-it-all 20-something, there are a lot of clever people in the world. And clever people make smart decisions (for example, reading this blog, which thousands do every day).

One of those decisions is the decision NOT to perform low-probability ad hoc data analysis. It’s a sensible decision. Sometimes it’s based on empirical study and hard-won experience, and sometimes it’s a gut feel; but either way, the decision has a strong rational basis. It’s just not worthwhile.

A picture is helpful:


Click image to enlarge

The above shows the expected value of an ad hoc analysis of a $100K savings opportunity. On the X axis is the number of days required to prepare and analyze the data; on the Y axis, the probability that the analysis will be fruitful. I chose a $700 opportunity cost per analyst-day; choose your own number, it doesn’t really matter.

Note that the graph is mostly “underwater”; that is, the expected value of the analysis is largely negative. Unless the probability of success is quite high, or the time taken to perform the analysis is quite low, it’s simply not a good plan to undertake it.

We are all faced with the decision, from time to time, whether to explore a hunch or not. However, an analyst can only work for 220 days per year. Sending a key analyst off on a wild goose chase could be a serious setback, so it’s a risky decision, and we don’t do it, and so our hunches remain hunches forever.

But what if it wasn’t risky at all?

Nothing can be done about the “probability that the analysis will be fruitful”; that’s fixed. But plenty can be done about the “number of days required to prepare and analyze data.” Suppose a dataset could be built in 5 minutes, and analyzed in under an hour? This turns the expected value of speculative analysis sharply positive. Suddenly it is a very good idea indeed to perform ad hoc analysis of all kinds.

And that’s good news. Because there is a ton of useful data floating around the average company that nobody ever looks at. Top of the list? Invoice-level detail, from which all kinds of interesting conclusions can be drawn. Try this experiment: acquire invoice-level (PxQ) data from a supplier with whom you have a contract. Dump it into an analysis dataset, and chart price point by SKU over time. Chances are, like most companies, you’ll find something very wrong, such as prices all over the map for the same SKU (ironically, sometimes this happens even if you have an e-procurement system that’s supposed to prevent it). If you have a contract, only one of those prices is correct; the rest are not, and represent money on the table that you can recover trivially.

Of course, please don’t spend weeks or months on the exercise, because then it won’t pay off. Instead, find yourself a data analysis tool with which you can move quickly and efficiently — or a services provider who can use the tool efficiently for you (and thus make a contingency-based analysis worthwhile for both of you). Bottom line: if you can’t build a dataset by yourself, in minutes, you’ll end up underwater, just like the graph.

Next installment: Crosstabs Aren’t “Analysis”

Previous Installment: It’s the Analysis Stupid

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Spend Analysis I: “It’s The Analysis, Stupid.”

Today’s post is from Eric Strovink of BIQ.

James Carville is not my favorite person, but he’s a funny man. And the above bastardization of his (in)famous Clinton campaign quote seems quite apropos, given the current frenetic level of marketing activity around “spend analysis” (I’m always amused by vendors using this term, because… excuse me for asking… “where’s the “analysis?”)

So why is there so much spend analysis marketing activity, all of a sudden? I suspect it’s “Oracle Terror”. For the last nine years, I’ve watched spend analysis vendors promote their “product” — typically a service masquerading as a product — using the same tired strategy: “We classify data better than [those other guys].” Problem is, when you spend so much time and effort dumbing down spend analysis to a simple-minded premise, you open the door for almost anyone, even a sleepy ERP vendor, to steal your lunch. And that’s exactly what has happened. Oracle has neatly synthesized all of the “classification” messages together, packaging them up with some Silicon Valley marketing magic, and the legacy spend analysis vendors are in a panic. You’re absolutely right, folks, Oracle’s messaging is better than yours. Smarter, more sophisticated, priced innovatively — it’s both ironic and funny. The only surprise is that this didn’t happen years ago.

But here’s the point: real spend analysis is so much more than classification, that the whole classification discussion is absurd. It has always been absurd. Classification-centrism is the Titanic of spend analysis, aiming squarely at a snowball on the top of the iceberg, while completely ignoring the massive value beneath. Nevertheless, relentless classification-oriented marketing over many years has warped end-user perceptions, and carried analysts right along with it. Current analyst firm surveys are spending over 90% of their time on classification questions, Pandit’s hopelessly off-target book (previously dissected and dismissed by Sourcing Innovation) is garnering new attention, and so on.

My iconoclastic point of view has been outlined in these (and other) pages before, but put very simply, it’s this: Classification is easy. Armed with appropriate tools, any intelligent person (your admin, for example) can be trained to do it effectively, in about an hour; and the rules they generate can be applied automatically to new transactions, forever after. When you stop to consider that sourcing consultants have been performing effective spend analysis for years, using nothing more than pencil and paper, it’s obvious that the classification Emperor really doesn’t have any clothes.1

In fact, true value lies in the analysis that you perform. Value is about results, and results come from analysis, not from a data classification process that is just a baby step toward value realization, and one that may not even be relevant. For example, consider that spend classification is really only useful for A/P data. There are many higher-value sources of data lying around, and many datasets can be built from them. In most of those datasets, classification has no place at all. By the way, how many spend datasets do you plan on building? One? Just on A/P data? Then you are missing out on value, by a wide margin.

In this series, I’ll discuss the requirements for ad hoc data analysis, and the very real value that results from it. Spend analysis, at the end of the day, is just data analysis; so it’s critical that your data analysis tools provide the necessary power and flexibility to make you successful.

Next installment: Why Data Analysis Is Avoided

1Ironically, based on the datasets we’ve seen from customers who have walked away from their classification-centric vendors, talking a great game on classification doesn’t necessarily mean delivering great classification.

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Rudimentary Heuristics to Support the Concept of Optimization in Negotiations

Today’s post is from Dr. Lloyd M. Rinehart, an Associate Professor of Marketing and Logistics in the College of Business Administration at the University of Tennessee and author of numerous publications including “Creating Reality Based Relationships Through Effective Negotiation: Academic Concepts and Research Support”, “Creating Reality Based Relationships Through Effective Negotiation: Understanding the Negotiation Process”, and “Effective Negotiation: Understanding the Negotiation Process – A “Road Map” to Successful Sales and Purchasing Negotiation Performance in the Value System”. Lloyd can be reached at Rinehart <at> utk <dot> edu.

This post is based on my presentation at the 2009 MPower BPX roundtable and subsequent thoughts that arose out of my resulting discussions. The concepts that I introduced in the session included the definitional parameters of seven relationships that evolve out of negotiations. These seven relationships, which were covered in the doctor‘s review of my presentation in What Relationships Do You Have With Your Suppliers, include:

  • Non-Strategic Transactions,
  • Administered Relationships,
  • Contractual Relationships,
  • Joint Ventures,
  • Specialty Contract Relationships,
  • Partnerships, and
  • Alliances.

I am going to expand on the concept of definitional parameters of relationships in one form, but in order to do so, I am going to consolidate the seven relationships into three relationship categories:

  • Transactionally Driven (Non-Strategic Transactions and Administered Relationships),
  • Contractual / Investment Driven (Contractual Relationships and Joint Ventures), and
  • Relationally Driven (Specialty Contract Relationships, Partnerships and Alliances).

Generally, whether or not it is actually the case, managers perceive that between 30% and 40% of their relationships fall into each of these general categories. Before we continue, Let me define the characteristics of these relationships. They are built on the three dimensions of trust, interaction frequency, and commitment to the relationship. In other words:

  • Does the party trust the other party?
  • How much does the party interact and exchange with the other party?
  • How committed is the party to the other in terms of dependence and investment?

Transactionally Driven Relationships are low on trust, low on commitment, but can have a range of interaction and exchange.

Contractual / Investment Driven Relationships are “slightly” higher on the trust, interaction frequency, and commitment dimensions than the Transactionally Driven Relationships.

However, those that are Relationally Driven are significantly higher on the trust dimension, while that other dimensions have a range of values.

That brings the discussion to one of today’s hottest terms in business — “collaboration”! Unfortunately, that term, like many others, means about whatever the author would like it to mean (and, consequently, that leaves the readers to interpret the concept as they desire as well!). Herein, I am going to constrain “collaboration” to be situations in which trust in the other party is HIGH. That means that of the relationships listed above, “collaboration” occurs about 30% to 40% of the time.

Now wait a minute! I said that this post is the result of my thoughts and subsequent discussions, which included a discussion with Michael. My understanding is that some of Michael’s contributions to the space deal with the concept of “optimization” in sourcing and procurement. My definition of “optimization” includes the attempt to minimize or maximize inputs that capitalize on the best outcomes across the integration of the inputs. My first exposure to the concept of “optimization” was in mathematics and micro-economics. The micro-economics applications focused on how companies could optimize the characteristics of their operational inputs and outputs.

However, here we are talking about negotiation, which means that at least two parties, rather than one entity, need be optimized. Here is the problem with the percentages given in this post. Those relationship assessments were originally generated from the perceptions of only one of the parties to the relationship. Therefore, the original data does not actually represent the “dyads” (perceptions of both parties on the relationship.) While most managers view negotiations as being too sensitive to allow external researchers to become involved, we can successfully simulate similar relationship perceptions in contrived environments. The contrived environment allows the opportunity to pair up the parties into “dyads” for dyadic assessments.

Outcomes of those assessments indicate that, in reality, only 13% of the relationships reflect situations where BOTH parties perceive high trust in the other party. In this situation, both parties feel comfortable enough in the negotiation to share information with the other party and work together for the purpose of “optimizing” the joint inputs to the relationship between the “two parties”. That is how I define “collaboration”, and the data indicates that it probably does occur 13% of the time. It is also important to recognize that the process of “working together” in the negotiation process involves a “collaborative” strategy in which the parties are attempting to “optimize” the outcome in a “Win – Win” sense.

However, there is another situation, that constitutes 1% of relationships, where balance in the negotiation occurs. That is when both parties approach the negotiation and relationship from a “competitive” strategy perspective. In this case, both parties are very skilled and effective negotiators and collectively drive each other to outcomes that are similar to the “collaborative” outcomes, but instead reach that position by pushing the other party “hard” to achieve a mutually beneficial outcome. In other words, both parties are approaching the relationship from a Transactionally Driven perspective. Therefore, I believe two diametrically opposite relationship perspectives can lead to similar outcomes, even though the negotiation strategies are very different. However, regardless of the strategy implemented, the parties must thoroughly understand the negotiation process.

Before concluding, one other problem must be identified with this discussion. The 13% of the original 30% to 40% of relationships that were perceived to be “high trust” and the 1% of the relationships that were perceived to be low trust leaves 86% of the relationships unaddressed. Those are relationships that are unbalanced in the level of trust between the parties. If one trusts the other party less, then that party will most probably implement opportunistic strategies which will be “self” beneficial and, of course, at the expense of the other party. Therefore, it is critically important that both parties in a negotiation fully understand the negotiation process and know how various strategies can contribute or detract from the desired outcomes of the negotiation.

I hope these thoughts stimulate discussion (both pro and con) that may advance the quality of decision making in your organization.

Thanks, Lloyd!.

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Kate Vitasek on Game Changing Rules for Outsourcing (Vested Outsourcing)

Today’s guest post is from Kate Vitasek, a lead researcher and faculty member of the University of Tennessee’s Center for Executive Education and the founder of boutique consulting firm Supply Chain Visions.

For the past two years, I have had the opportunity to participate in a University of Tennessee research program, funded by the Air Force, to formally study companies that were employing performance-based approaches for outsourcing. The research has uncovered that there is a set of unwritten rules companies can apply to develop mutual symbiotic performance partnerships where both parties in the outsourcing relationship unlock win-win solutions to achieve much higher levels of performance and cost savings.

We have distilled our lessens and approach into what we call Vested Outsourcing — because it is typified by an outsourcing relationship where both parties have a stake in maintaining the arrangement and where both parties work together to create a performance partnership which takes both the company outsourcing and the service provider to new levels of cost, service and profitability not realized by traditional outsourcing models.

While no two Vested Outsourcing partnerships are alike, all good ones achieve a performance partnership based on optimizing for innovation and improved service, reduced cost to the company outsourcing, and improved profits to the outsource provider. This is what we call the performance pyramid. This trend towards performance partnerships has evolved to where outsourcing companies and service providers work together to develop a performance-based solution where both parties’ interests are aligned — and both parties receive tangible benefits (either through tangible or intangible incentives).

The heart of a Vested Outsourcing contract is an agreement on desired outcomes that explicitly states the results on which both companies will base their outsourcing agreement. A Vested Outsourcing agreement clearly defines financial penalties, or rewards, for not meeting, or exceeding, agreed upon desired outcomes. In the agreement, regardless of what is being outsourced, the outsourcing partner has the ability to earn additional financial value (e.g., more profit) by contractually committing to achieve the desired outcomes. Simply stated: if the outsource provider achieves the desired outcomes, they receive a bonus.

While many organizations tout they have “partnerships” — our experience and research found that most organizations have an internal desire to optimize their own self interests. This is often known as a WIIFMe approach (What’s in it for Me). How could they not when we are ingrained with “winning” from early childhood and most business schools and law schools focus on “winning”.

The very word partner implies that there are two sides. The progression towards a Vested Outsourcing agreement must focus on creating a culture where both parties are working together to ensure the ultimate success of each other. The mentality should shift from an “us vs. them” to a “we” philosophy, or what we call a What’s in it For We (WIIFWe) philosophy. For many companies, a win-win approach is a learned behavior — and they have to unlearn their conventional approaches and ways of thinking. In a Vested Outsourcing relationship, the organizations must work together upon a foundation of trust where there is mutual accountability for achieving the destined outcomes.

Five key rules set the stage of a sound outsourcing partnership.

  1. The business model is established based on outcomes versus defining transactions.
  2. The company outsourcing needs to feel comfortable describing the “what” and delegating the “how” to the outsource provider — and the outsource provider must be comfortable signing up to take the risk to deliver the “how”. Both organizations must constantly seek to overcome roadblocks in the processes, infrastructure, technology and people that prevent mutual success.
  3. Carefully aligned, clear and measurable performance objectives are used to monitor the desired outcomes.
  4. A balanced pricing model that includes mutual incentives and rewards, optimized for cost versus service trade off.
  5. The relationship is based on insight, versus oversight governance, that empowers both parties to pursue improvements that will deliver better performance, higher profits, and lower total cost of ownership.

The five key rules of a sound outsourcing partnership set the stage for companies to take their outsourcing relationships to the next level — a true vested performance partnership.

In Vested Outsourcing, the organizations work together upon a foundation of trust where there is mutual accountability for achieving the outcomes. Through the careful alignment of performance objectives, accountability, and control, the service provider, while absorbing additional risk, is empowered to pursue improvements that will deliver improved performance, higher profits, and lower total cost of ownership.

Vested Outsourcing uses the power of free market innovation to improve the outsourcing relationship. This can be challenging to achieve, but the Vested Outsourcing journey should always strive to arrive at this idealized end state to achieve the performance pyramid — where both the company outsourcing and the outsource provider are consistently applying a WIIFWe foundation and applying all five of the Vested Outsourcing rules.

Companies with a desire to explore Vested Outsourcing further, can visit the Vested Outsourcing website, hosted by the University of Tennessee, and download an excerpt of the upcoming book being published by Palgrave Macmillan titled Vested Outsourcing: Five Rules that will Transform Outsourcing.

Thanks, Kate.

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