Category Archives: Decision Optimization

Why Bidding Flexibility Is Important to e-Auction Success

Regardless of what you want to call it — expressive bidding, lotting, market baskets, informed sourcing, etc. — the ability to let a supplier bid the way they can give you the best price is very important to e-Auction success. If all you can support is simple auctions on an item by item basis, and quotes on an item by item basis, you are not going to get the best deal.

This is rather easily illustrated. For example, let’s say your business is clone computer assembly for mid-sized businesses who don’t want the Dell or HP premium. Let’s also say that you buy six different components for these computer assemblies: cases, power supplies, motherboards (with on-board everything to keep it simple), memory, hard drives, and cable packs.

If you are forcing a supplier into separate bids by item, and the level of detail they can quote is price per unit, shipping per unit, and extended warranty per unit, you’re probably going to end up with quotes looking like this:

Supplier 1 Supplier 2 Supplier 3
Component Unit Freight EW Unit Freight EW Unit Freight EW
Case 20 5 1 22 4 1 18 6 0
Power Supply 40 3 6 36 4 3 38 4 2
Motherboard 199 5 24 195 5 19 189 5 30
Paired Memory Pack 49 2 4 47 3 6 51 3 4
Hard Drive 78 4 12 74 3 8 81 4 7
Cable Pack 22 4 0 24 3 1 19 5 0
Total 49 2 4 305 12 30 37 11 0
Grand Total 450

Not bad for a clone server, but if you bid out the basket and allow the supplier to bid on just the components they want and do so as a bundle, you might find that you get this result:

Case Power
Supply		 Mother-board Memory Hard
Drive		 Cables Freight Warranty
S-1 B-1   19   38   20   8   5
S-1 B-2   45   71   5   8
S-2 B-1   20   33   6   2
S-2 B-2   195   14   5   10
S-3 B-1   45   72   5   9
S-3 B-2   185   14   7   30
Grand Total 414

An 8% savings by allowing a supplier to bundle bids according to their operational efficiencies!

Get it now?

If I Succeed in Destroying Dashboards and Razing Report Writers, What Next?

In yesterday’s post, where I responded to the smart alecks, I noted that, once dashboards are destroyed and report writers are razed, there was about a half-dozen next logical steps that could be taken to improve today’s spend analysis solutions, even if that solution was BIQ.

Should cost modelling, award optimization based on historical data and business rules, and federation across related data sets for deeper dives are pretty obvious. Are there somewhat less obvious advancements we should also be thinking of?

Of course. One rung up the ladder, three of them are:

Predictive Modelling

Once you have should-cost modelling, the next logical step is predictive modelling. Use historical data to extract pricing trends and predict likely future prices for the commodity. Use this to determine not only the best time to (re) source the category as well as using deep-dive analysis to determine the best strategy.

Optimize Supplier Relationships

Once you have optimized all of the awards based on historical data and business rules, you also have the optimal allocation by supplier. Once you have the optimized set of awards for each supplier, you can optimize the re-order schedule, shipping arrangements, and even production and sourcing schedules on behalf of the suppliers and take costs out one level down in the supplier chain. Helping your suppliers help you goes a long way to building good supplier relationships and increasing supplier performance.

Simultaneous Drill Across Multiple Data Sets

Once you have true federation, you want to split the screen and update the views to only contain the relevant data in each data set as you drill down through the data. Going back to our previous example, you start in the Payment cube drilling into the goods receipts associated with the wonky widgets, then switch to the Order History cube to find the initial requisitions, but when you drill on the user in the second cube, the first cube is updated to contain only those goods receipts associated with the user. The user can drill through either cube to find the data she wants, whichever is easiest, and both cubes update. She doesn’t have to go back and forth.

These are just a few more things that can be done, and all would simplify the life of an analyst. More to come at a later time but first, this time I’m going to insist that you tell me what you would do. :-;

If I Succeeded in Destroying Dashboards, How Else Would I Improve Spend Analysis.

The smart alecks are correct — technically destroying dashboards is not adding anything to spend analysis so I didn’t actually provide a way to improve spend analysis technology, just the results you get from using it.

So if I succeeded and dashboards bit the dust, what would I do? (Besides banning integration points for report writers for all OLAP-based spend analysis products?*) Good question. Especially since there’s about a half dozen logical next steps.

Three things that would be useful if you had a true spend analysis product like Opera’s BIQ would be to:

  • Integrate Easy Should-Cost Modelling CapabilityThis way you can define a cost breakdown for a product or service you are looking to source and have the tool automatically generate an expected cost based upon current data, as well as a price-range, with confidence, based upon low, average, and high prices paid for the raw materials, energy, labour, etc. (provided that the should-cost model permitted base-cost definitions for any cost components you weren’t buying that were bought entirely by your supplier)
  • Optimized Awards Based on Historical Data and Business RulesYou don’t have to send out an RFX to get base market pricing if you are already buying a product, it’s in your transaction store. Nor do you have to run a complex event to determine the lowest cost providers for a market basket. Moreover, if you are buying commodity products and services with list prices, and all your suppliers do is give you a discount of X% for a guaranteed award, you don’t really need optimization to determine the lowest cost as it’s just a simple formula against current pricing. And if your only business rule is 2 or 3 way split, it’s just the 2 or 3 lowest cost suppliers with the appropriate risk mitigation. In this situation, it would be easy for spend analysis tools to build in some simple optimization capability to tell you your lowest cost buy, and if it’s close to your should-cost model, you can just cut a contract without going through a time-consuming sourcing event.
  • True Federation across Related Data SetsMost spend analysis tools are only capable of working on one cube built on one data classification at a time. This means that even though a user can pick the drill dimension order, only one set of data can be viewed at one time. But sometimes you want to drill into greater detail (such as who requisitioned all those widgets from the wonky supplier), and that’s not in the transaction file — so you need another cube with more detail on the invoice (history). Then you drill in on the augmented AP (cube) data until you get to the invoices associated with the supplier, switch over to the new cube and drill down to the line items of interest and retrieve the requisitioners. Another situation is where you are getting a lot of warranty returns, and you want to figure out what batches the returned items are in so you can determine whether or not the batches were bad and it will be cheaper to do a mass replacement (by just putting out a recall) than dealing with one breakdown at a time. In this case, you need to drill into the warranty cube and then branch over into the invoice cube to get the batch numbers associated with the appropriate goods receipts that are associated with the invoice.

These are just a few things that can be done, and all would simplify the life of an analyst. More to come at a later time but first, what would you do?

* If you don’t know why, you don’t know your spend analysis product limitations!

In What Way Would I Improve Spend Analysis?

When it comes to spend analysis there is at least one particularly powerful tool out there that will meet the majority of the needs of any organization and probably at least one tool that will do, with elbow grease, just about any analysis an analyst can think of. Since businesses have wanted reports and analytics since the days of the first spreadsheets, analysis tools are always advancing and most are beyond the ability of the average user to fully utilize their functionality.

So, given this fact, how would I improve spend analysis? And given that this question may imply that I may only make one improvement, just what would that improvement be? Especially since most tools don’t do (true) federation, don’t support full reg-ex (regular expressions), don’t understand semantics, and don’t run fast enough on large data sets — indicating that, as a PhD in CS with deep expertise in analysis, modelling, optimization, and semantics, there are theoretically a number of advancements I could bring to the table if I put my mind to it?

Despite the plethora of options available, today there is only ONE thing I would do to improve spend analysis. I’d make it impossible to do anything but spend analysis. Specifically, I’d make it illegal to include dash-boarding capability in any (spend) analysis product.

Why would I do such a thing? Besides the fact that I’ve been ranting since 2007 that dashboards are dangerous and dysfunctional, I would do such a thing because, among other things, they give you a false sense of security that, if mismanaged, could be so grave that, like the myth of Nero, you would fiddle while the factory burned.

Why would I ditch the dashboards and make it a crime punishable by any fate one could devise that was worse than death to include any capability whatsoever designed to support a dashboard? Because I just read this post on Purchasing Insight on “the inordinate cost of poor spend analytics” that said that it’s reckoned that more than 50% of businesses employ between 2 and 5 people to prepare and create procurement dashboards and spend reports. This is ludicrous. (No, not Ludacris.) If these people are senior analysts, then a large organization is spending more than 500,000 a year on salary and overhead to create dangerous and dysfunctional dashboards that spit out shiny spend reports that, after being analyzed the first time for inefficiencies, provide zero value to the organization. Once the report is analyzed, the inefficiency identified, and the problem corrected, and once this is verified in the next report, no subsequent report is going to tell the analyst, or management, anything new.

As SI has said again and again, the value of spend analysis is actually doing spend analysis, again and again, testing new hypothesis every time they pop into the analyst’s head. Yes, most hypotheses will yield nothing, but that’s not important because it only takes one insight to yield 100,000 worth of savings. If the tool is flexible, powerful, and configured appropriately, the user will be able to explore dozens of different analyses in a week, and if even one yields 10,000 of savings, that’s an (amortized) ROI of (at least) 5X. Spend analysis is analysis. Not dashboards and reports.

So if you really want to improve spend analysis — ditch the dashboards and focus your talent on real analysis. Otherwise, just download a free reporting engine off the internet. You’ll get the same worthless result, without forking out six figures for a tool you’re not really using.

Why Don’t We Hear Anything About Market Informed Sourcing?

Because the acronym is a MIS!

While I agree that we need more discussion around Sourcing Optimization, I don’t think calling it Market Informed Sourcing is going to help any just because people are afraid of the “O” word. Even though good optimization is “market informed” and uses up-to-date market data, people don’t really understand what “market informed” really is. When people hear informed, they tend to not only think of fact but opinion, and optimization is all about fact, not fiction.

In a recent 3-part series on Sourcing Optimization (Part I, Part II, and Part III) by Alan Geelson over on Spend Matters UK, of Keelvar, a company which SI reviewed in its recent post on Strange Name. Uncommon Results, he notes that while there is no consensus on the name that best describes the approach — Market Informed Sourcing (MIS) Sourcing Optimization, or Collaborative Sourcing, the view at the heart of these propositions, namely, that suppliers should be afforded the opportunity to have greater flexibility as to how they engage with buyers, is an important one. And optimization is what enables this.

Furthermore, it also enables all parties to play to their strengths without discriminating against any one group. Suppliers can submit “sealed bid” bids bundling and packaging lots together as they see fit, offering price breaks and rebates for certain volume purchases. And buyers can weight the advantages and disadvantages of aggregating spend with less suppliers, finding the right balance between economies of scale and risk mitigation should one supply source go down.

However, sourcing optimization still has not enjoyed mass adoption. Some reasons for this, as noted by Alan, include:

  • lack of process, technology, and key benefit understanding,
  • cost, which is believed to be prohibitive, and
  • perception of complexity.

In other words, as SI pointed out in its recent post on how, When It Comes to Optimization, You Need Every Insight You Can Get!, it all comes down to

  • misinterpretation and misinformation,
  • cost, and
  • fear.

Even though it should all come down to, as Alan points out,

  • finding savings without “squeezing” suppliers,
  • gaining greater control over outcomes with business constraints,
  • while keeping acquisition cost and operating costs down.

Maybe some day the truth will be known and accepted, but, until then we have to keep spreading the truth.