Category Archives: Market Intelligence

Benchmarks: Blessing or Bane?

Benchmarking, formally defined by Wikipedia as the process of comparing one’s business processes and performance metrics to industry bests and best practices from other companies, are typically presented by consultants as a boon for business managers and a reason to buy their services and/or solutions. After all, if you can’t benchmark, not only do you know how good you are doing (compared to the industry), but you do not know if you are improving or deteriorating, at what rate, and what the potential is.

And all this is true, provided the benchmarks are accurate, apples-to-apples, and actionable. This is not always the case, and when the benchmarks are poorly designed and implemented, definitely not the case. In fact, if the benchmarks are not accurate, they can cost the organization precious time, money, and resources and result in worse, instead of better, performance. And even though you don’t hear about it (as the last thing a Big 6 consultancy wants to do is scare you away from one of their most profitable service offerings — as it takes a long time to design the scorecard, collect the data, and interpret the findings [which translates into a huge number of top dollar billable hours for the House of Lies] — it happens more often than you think, and if you end up being one of the unlucky, you will be cursing benchmarks until the end of your Procurement career (and beyond if the word ever again arises).

the doctor is being dead serious here. Benchmarks (like dashboards) hide at least six serious dangers that can seriously hinder productivity, savings, and innovation. Three of these are very common to internal benchmarks, and three of these are very common to external benchmarks.

One of the most significant dangers of internal benchmarks is hidden opportunities due to false negatives. This often arises when monitoring best-price contracts. A classic example is that of enterprise desktop systems. Considering that technology depreciates the time it hits the market, just like a car depreciates from the time it leaves the lot, the price of these systems should decrease over time. If the benchmark says that the contracted configuration decreased over the 12-month contract by an average of 0.5% a month, for a total decrease of 6%, the buying organization might believe that the vendor is honouring the best-price clause. But if the buying organization isn’t aware that the average depreciation of these systems is 12% to 18% and doesn’t monitor market pricing, the buyer might not know that the pricing should have decreased an average of 1.25% a month, and would have lost 0.75% a month on purchases. If the organization was buying 500 systems a month as part of a phased replacement for 1.5K each, or spending 750,000 a month, that’s a loss of $5,625 a month for a total loss of over $60K, or another help desk resource! (And if all hidden opportunities were this small, it might not be too bad. But this is more of a best-case loss example.)

One of the most significant dangers of external benchmarks is wasted years due to lack of validation. One common example is that of contingent or manual labour spend analysis. For example, consider the analysis of warehouse (contingent) labour across the enterprise. An enterprise could quickly find that its paying, on average, a fully burdened rate of $17 an hour for workers to stuff boxes while its competitors are paying, on average, a fully burdened rate of $14 an hour for workers to stuff boxes. This might lead an analyst to believe that the organization is paying 30% more than it should be and that it should seek out a new contingent labour provider to get costs down, and waste months on RFX and analysis only to find out that the most it can lower its costs from the quotes is 10%. At this point, the analyst might go back and do an analysis of what it would cost to take the labour management back in house (which would require building a Contingent Labour CoE, staffing it, etc.) and still not see a savings when it replaces the outsourced management cost with the internal management costs applied to the total wages paid out. At this point the analyst would give up, or spend even more time investigating the reason only to find out that the organization’s main warehouses are in California, New York, and Massachusetts, the states with the highest minimum wages in the nation, while most of its competitors keep their warehouses in the mid-west / south-west states that only mandate the federal minimum wage of $7.25 (vs. minimum wages north of $10). Benchmarks only capture price and performance tiers, not the realities that led to them.

But these are only two of the six major hidden dangers that can ruin any benchmarking project (and the efforts that they will kick off, for better or worse). For a detailed insight into the other four, download the doctor‘s latest white-paper (sponsored by Trade Extensions) on The Dangers of Benchmarks and Trend Analysis (registration required) today. You need to know these inside out before even looking at a benchmark (which, when improperly constructed and improperly interpreted, can be just as deadly and dangerous as a dashboard).

Organizational Sustentation 53: Engineering

Engineering designs the products that represent a product-based company’s life-blood, as they generate the cash necessary for operations. No company exists without revenue (NO Sale, NO Store), and revenue only comes from the sale of products or services. And those have to be designed by someone, and that someone is typically an engineer. And while Engineers are the top talent in the company, as well as the best educated talent, they can also be stubborn rigid perfectionists.

As per our damnation post, each engineer has a process, a design, a set of approved raw materials, and that is the process, the design, and the set of approved raw materials. Trying to convince them that there is another process, alternate design, or other raw material that could be useable is like trying to force molasses to flow up a glacier, as this would mean that they would have to accept that there are better processes, designs, and raw materials, and that they exist today (despite the engineer’s expensive research and experience).

And even if they are willing to accept there are better processes, design, and approved raw materials — they are perfectionists. The cost model might say that 98% reliability is good enough because, in practice, only 1% of units will break down before the warranty period expires and the cost of flat out replacement will have little impact on profit margin, but Engineering will say otherwise. They will insist on the supplier with 99% reliability even with a 30% cost increase because a good engineer makes the best product they can make, cost be damned.

So how do you deal with this damnation so Procurement can achieve some sustentation? Education.

The first thing you need to educate is that reliability is not the number one concern, safety is. If a laptop, music player, TV, etc. stops working, it doesn’t harm anyone. The buyer might be annoyed, but if you immediately rush out a brand new replacement, the buyer won’t be annoyed for long. As long as the product doesn’t short out and electrocute the user, there’s no issue with a little less reliability.

The second thing you need to educate them is that sustainability trumps supplier longevity. A company has to plan for the future, not rest on past laurels, especially if those past laurels are suppliers that have never been questioned. While every supplier was likely a great choice for one reason or another at the time the supplier was selected, the supplier might not be such a great choice today. All suppliers have to be reviewed at one point in time, and if there are more sustainable suppliers, they have to be investigated.

The third thing you need to do is educate them that you can help them identify suppliers that could have better processes, designs, or raw material formulations and save them a lot of time searching for new alternatives, as you will be scouring the market on their behalf and only bringing them suppliers that might truly have a better, or different, option. As the gate-keeper, you will save them a lot of time.

Engineers are your best allies – they are educated, rational, and want to do the right thing for the organization, like you. So show them how you can help, and be willing to listen (and learn) from them, and you will be able to overcome this organizational damnation.

Keelvar: The Little Engine that Could

In case you haven’t guessed, this post is about The Little Engine That Could not only get up the big hill, but after scaling the hill, decided to follow the tracks up to Alaska, tackle, and climb, Mount McKinley (also known as Denali), which is the highest mountain in the United States at 6,190.5 meters (or 20,310 feet), and not stop until it reached the summit.

For those of you who missed our prior posts, namely Keelvar: Strange Name. Uncommon Results., Keelvar: Are They Right for You, and Re-introducing Keelvar, An Optimization-Backed Sourcing Platform, Keelvar, which is the newest, and still the smallest entrant, to the strategic sourcing decision optimization game, and one of the few (correction: two) vendors to provide a fully integrated optimization-backed sourcing platform (with integrated RFX and e-Auctions), has been making great strides since it spun-out of the 4C research laboratory (in the Department of Computer Science) at the University College of Cork a mere four years ago in 2012. Since then, it has been advancing faster than all of its peers except Trade Extensions, and has emerged to become a top contender for the provision of optimization-backed sourcing platforms. In fact, as hinted at in an upcoming Pro piece, the doctor expects that Keelvar will grow faster than 4 of its 5 five competitors over the next few years.

So what’s so great about this little upstart? The first thing to note is the ease-of-use of the platform. The platform, which embeds a simple-to-follow seven-step best practice sourcing platform, literally guides even the most junior of buyers through the most complex events the platform can handle, and the side-bar navigation makes it a breeze to quickly access any step in the process. (The tried-and-true best-practice methodology is strikingly similar to what MindFlow used back in the day, but it never had such an easy to use, clean, and modern interface.)

The second thing is the speed of improvement. Since SI last reviewed the platform last fall, a number of considerable of enhancements have been made that go well beyond usability. Extensive supplier self-service has been added (which allows the supplier to manage not only the response and bid process, but the team assigned to it – all the buyer has to do is invite one supplier rep, and that supplier rep can create the supplier organization’s records, add users, give them appropriate, fine-grained read/edit rights to the documents and bids, and manage all of their effort without any buyer involvement whatsoever). Single-sheet smart-load (which allows the platform to detect field-types, field-status, and other relevant information without a user having to define a lot of meta-data or use the cell-based encoding required by other platforms) has been developed. And parametric bidding is in quality assurance.

Parametric bidding is, in a world, cool. Often in the acquisition of fleets, computers, cell phones, etc., the buyer doesn’t precisely know the exact configuration details that are desired until the last minute. In this situation, the buyer has to either create a huge number of potential configurations for bidding, or pick a few and hope for the best. With parametric bidding, the supplier can bid on a base configuration and define all of the options they offer against that configuration as well as the price increments (or decrements) for that option. When the final configurations are selected, the system will automatically calculate the appropriate costs (and discounts) from the parametric sheet for the optimization model, with no effort at all required by the user. This is a feature that is jut not seen in first generation sourcing platforms. Watch for it.

Keelvar, which was first named as a SpendMatters company to watch last year (and which will soon be covered in depth on Pro by the doctor and the public defender), is a company that you should be keeping a really close eye on. Optimization-backed sourcing platforms are the future. and right now there are one of only two providers with a single, integrated, end-to-end, solution. We may see more in the future (with BravoSolution working on integrating its two product lines, SciQuest’s acquisition of CombineNet, and Determine’s acquisition of Selectica), but Keelvar (and Trade Extensions) have an early lead that gets larger every day their competitors work on integration (as opposed to innovation).

Now, you’re probably worried about adoption, because first generation platforms were, for the most part, so damn hard to use (to put it bluntly), but second generation optimization-backed sourcing platforms are actually quite easy to use and focussed around adoption. For more information on how to get Higher Adoption, check out the linked white-paper. And for more information on Keelvar, we recommend checking out their new, open, Keelvar support portal.

SourceMap: Striving to Bring Supply Chain Visibility to the Masses

SourceMap is a supply chain mapping tool that is designed to help an organization map out their end-to-end supply chain to help them gain critical insight and understanding into their performance, costs, sustainability, and risk. Especially risk. Most companies don’t understand the risks hidden in their supply chain — the sole-source parts, the over-dependence on high-risk geographic areas, or the ability of a single port strike to knock out multiple shipping lanes. (Nor do most companies understand the cost of risk, which is discussed in detail in Sourcing Innovation’s upcoming white-paper on Playing With Fire, but that’s a discussion for another post.)

SourceMap, born as a research project at the MIT Media Lab to publish and measure the environmental footprint of all the products on earth, was launched as a public platform for supply chain mapping in 2009 that allowed individuals to see every aspect of a product’s life — the good and the bad. Then, in 2011 it partnered with the MIT Centre for Transportation and Logistics to pursue opportunities in automating supply chain visualization and risk management. Shortly after, the 2011 Tohuku tsunami hit and wiped out over 45,000 buildings, damaged over 144,000 more, shut down all of Japan’s ports (including 15 that were located in the disaster zone). All told, it did over $300 Billion in damages and sent shockwaves throughout global supply chains. Companies were scrambling to understand the impact on their supply chains, SourceMap was approached, an incorporation followed, and the private sector solution was born.

Hands-down, SourceMap is the best visualization of the supply chain to hit the scene since Resilinc, which is, in Sourcing Innovation’s view, is still the leader in Supply Chain Risk Management solutions, but if all an organization needs is visibility and Supply Chain Visualization, SourceMap is now a leading contender in that arena. SourceMap has the ability to use an organization’s ERP data, public data sources, and survey data from the organization’s suppliers, the suppliers’ suppliers, down to the raw material suppliers, to create a complete point-to-point map of the supply chain that an organization can use to trace it’s products from source-to-sink on a (Google Earth) Map and visually see what is happening. This is a very powerful feature that allows an organization to gain insights into their supply chain that they never knew before. And just like an organization is typically shocked the first time they run a spend analysis (we spend that much with who?!?), they are typically just as shocked when they run a map and see that a number of distributors and tier 1 suppliers are using, or outsourcing a significant portion of, spend to the same tier 2 supplier and just pushing the single-source point of failure an organization is trying to avoid one step further down into the Supply Chain.

And the SourceMap solution, which only needs common location data points, can quickly import and combine all data sources an organization can get its hands on and SourceOne can often create a starting supply chain map for an organization in less than an hour. It’s not complete or perfect, but it allows the organization to quickly drill into the supply chain and see where the data, and focus, is needed.

SourceMap is quickly becoming the new supply chain visibility solution to watch, and for a real in-depth analysis, Sourcing Innovation would recommend the in-depth write-up that the doctor and the prophet collaborated on over on Spend Matters Pro (membership required) that provides four pages of deep insight into the solution.

One Hundred and Twenty years Ago Today

Mr. Charles Dow published the first edition of the Dow Jones Industrial Average, a mere 12 years after Mr. Charles Dow composed his first stock average (of nine railroads and two industrial companies). And while there have been many averages, including a number created by Mr. Charles Dow himself, the Dow Jones Industrial Average (DJIA), the second oldest US market index (after the Dow Jones Transportation Average) is the most famous of these. The only index that come close in fame is the S&P 500.

It’s famous as many investors believe it can be used to describe the market, but all it can really be used for is a baseline to compare the return on specific investments in a historical period to the index. A good investor should beat the index, and a bad investor doesn’t hit it. However, simply judging a price against a price weighted index doesn’t really tell you much about the market, just the historical performance of a small portion of it. It’s a useful measurement of past performance, but not necessarily a good indicator of future performance of the market overall. But all analysis has to start from somewhere, and this did give rise to a new era in stock market analysis, which, for better or worse, did lead to new advancements in analytics and computing, we have to at least recognize it.