Originally posted on on the e-Sourcing Forum [WayBackMachine] on Sunday, 27 August 2006

Friday we noted the effectiveness of decision optimization and how it can enable early adopters to identify average incremental savings of 12% above those that basic, price-focused auctions alone have generated, according to Aberdeen’s recent “Success Strategies in Advanced Sourcing and Negotiations: Optimizing Total Costs and Total Value for the Next Wave of e-Sourcing Savings” in June of 2005. Yesterday we discussed the factors that combined to downplay decision optimization’s importance to a successful e-Sourcing process. Today we will discuss how the market is changing and why decision optimization will soon take its rightful place at the heart of strategic sourcing initiatives.

Four major factors are currently combining to elevate the importance of decision optimization to strategic sourcing. Briefly they are:

• Extensive use of e-Auctions over the last 3-5 years by early adopters.
• Optimization Technology has evolved.
• Solution Providers are integrating optimization into their platforms.
• Solution Providers are recognizing that there needs to be a significant amount of sophistication under the hood.

(1) Extensive use of e-Auctions over the last 3-5 years by early adopters and market leaders have sucked all of the fat out of supplier margins, rationalized the supply base, and streamlined the process to the point where there is essentially no more money to be saved on auctions alone.

(2) Optimization technology, like all technologies undergoing an evolution, has become more accessible and easier to use. No longer does it take a heavily trained PhD to use today’s optimization products. A BSc with minimal training can be up and running with today’s tools in a few hours, and comfortable within a few days. (However, it still takes a PhD, or PhD team, to build the product, but that’s why market leaders are hiring very well educated and experienced teams.)

(3) Leading Solution Providers are recognizing that decision optimization needs to be an integrated part of an e-sourcing platform that supports the full range of e-sourcing activities and are incorporating these capabilities into their product suites.

(4) Leading Solution Providers are recognizing that a minimal amount of sophistication is required in the model and building this into their products. For example, whereas Iasta’s bid optimization 1.0 product contains the basic concept of Ship Tos by way of allocation groups, their forthcoming product, among other capabilities, will eventually support Ship Froms and discounts, which in turn support freight lanes and volume-based pricing.

I see a very bright future for decision optimization enabled e-sourcing platforms, both for solution providers and their clients who will now have the opportunity to maintain double digit savings in their sourcing events for years to come. But I’d like to know what you think. Who’s using it now, Who’s planning to use it, and Who’s not. And why?

For a more in-depth discussion of decision optimization, what it is, what it is not, how it enables decision support, the benefits it provides, and strategies for success, see the “Strategic Sourcing Decision Optimization: The Inefficiency Eliminator” wiki-paper over on the e-Sourcing Wiki [WayBackMachine].

Originally posted on on the e-Sourcing Forum [WayBackMachine] on

Yesterday I pointed out a recent report from Aberdeen in June of last year entitled “Success Strategies in Advanced Sourcing and Negotiations: Optimizing Total Costs and Total Value for the Next Wave of e-Sourcing Savings” where they determined that the application of optimization tools to analyze total costs, and of flexible bidding functionality to uncover creative supplier solutions has enabled early adopters to identify average incremental savings of 12% above those that basic, price-focused auctions alone have generated and discussed the fact that despite this result, optimization still is not used regularly across the board.

I also indicated that the lack of use of optimization across the board is likely the result of a number of factors that have historically combined to downplay the appeal of decision optimization, which has often been viewed as overly complicated unless absolutely necessary.

Specifically, I believe the lack of adoption of decision optimization across the board is the result of four key factors.

1. Early e-Auctions generated amazing returns.
2. Initial optimization offerings were hard to use and harder to understand.
   (In my view, MindFlow fell into this category.)
3. Many solution providers attempted to side-step the complexities by toning down their options.
4. A lack of integrated solutions on the marketplace.

I will now discuss each of these in more detail.

(1) Early e-Auctions generated amazing returns!

Many auctions generated double-digit returns, often in excess of 20%! This caused auction technology to be over-hyped as a technology for cost savings. As a former employee of an early provider of Strategic Sourcing solutions, I saw both the results and the buzz it generated. However, these results cannot be maintained indefinitely! Even if a supplier has a bloated margin of 100%, the most they will be able to give up and maintain viability is typically in the 60% – 80% range. In other words, after 3 events, there are no more margins to trim.

(2) Initial optimization offerings on the e-sourcing marketplace were hard to use and even harder to understand.

By its very nature, optimization, which is based in complex mathematics, is hard. Very hard. And many products had user interfaces to match. The underlying technology may be sophisticated, but this does not imply that the end product should be! Your car is a perfect example. Modern cars have very complex integrated mechanical, electrical, and electronic systems. But the user interface for an automatic is a gear shift (park, neutral, drive), a steering wheel, a gas pedal, and a brake. Decision Optimization should be the same – mind-boggling under the hood but easy as e-mail through the UI. Next generation systems will be. (I believe that this is one of the reasons that decision-optimization (only) companies like MindFlow never caught on beyond a few large CPG and Food Service companies. For example, even though there was a time when MindFlow could not be matched in terms of self-service optimization capability in the sourcing marketplace*, it was also true that it could cause the average user significant consternation. I believe that only sophisticated sourcing professionals with extensive training could take full advantage of the solution. *I’m sure a few individuals at CombineNet would disagree with this statement, but one thing I repeatedly heard from customers and prospects about their early solution offerings was that you needed one of their PhDs to run it for you. However, I should note that for certain areas, this is definately no longer the case with some of their recent releases.)

(3) Many solution providers attempted to side-step the complexities by initially toning down their offerings.

The proclaimed market leaders in the sourcing space provide us with examples. Whereas MindFlow built an extensive model (7+ logical dimensions) with ship-tos, ship-froms, built in lane support, complex cost structures, etc., some of the leaders went with simple point-based bid solutions. Bid 1 from Supplier 1 for Item X, Bid 2 from Supplier 2 for Item X, Bid 1 from Supplier 1 for Item Y, Bid 2 from Supplier 2 for Item Y, etc. with a couple of limit or allocation constraints. Although these products turned out to be much easier to use, they did not provide enough sophistication to model the real world supply chains and constraints of the companies that needed optimization the most! Interestingly enough, I believe that this is one of the reasons MindFlow lasted so long when many other optimization-based start-ups no longer exist (independently). MindFlow’s early market may have been small, but they were one of the pioneers of true decision optimization technologies and one of the few companies to offer the real power multinational CPG and food-service companies needed to accurately model their sourcing scenarios. (In comparison, CombineNet was one of the few companies that could handle their purely logistical models.)

(4) A lack of integrated solutions on the marketplace.

Many of the early providers of decision optimization only offered decision optimization. Furthermore, those companies that did offer other solutions typically weren’t best in class, especially from a usability perspective. However, leading sourcing professionals know that decision optimization is most effective when it is part of an integrated e-enabled strategic sourcing process and relatively ineffective on its own. (At least one of Iasta’s forthcoming solution briefs will elaborate more on this.) Decision optimization needs cost data (that results from auctions, possibly sealed bid), qualified award possibilities (that results from RFx and Supplier Scorecards), and an understanding of the supply chain strategy and appropriate commodity market (that results from spend analysis and proper processes). On its own, its capabilities are limited, integrated into an end-to-end e-sourcing platform, its capabilities are virtually endless.

Fortunately, market conditions are changing and I believe that the industry as a whole will not only be ready for this amazing technology very soon, but be hungry for it, especially when it is properly integrated into an e-Sourcing platform that provides best-of-breed technologies that support the end-to-end e-Sourcing process. The reasons I have for this forthcoming shift, and the reasons why some companies are working hard to build a best-of-breed decision optimization offering that is tightly integrated into an end-to-end e-Sourcing suite, will be illuminated in tomorrow’s post.

For a more in-depth discussion of decision optimization, what it is, what it is not, how it enables decision support, the benefits it provides, and strategies for success, see the “Strategic Sourcing Decision Optimization: The Inefficiency Eliminator” wiki-paper over on the e-Sourcing Wiki [WayBackMachine].

Originally posted on on the e-Sourcing Forum [WayBackMachine] on Friday, 25 August 2006

Even before Aberdeen came out with its “Success Strategies in Advanced Sourcing and Negotiations: Optimizing Total Costs and Total Value for the Next Wave of e-Sourcing Savings” in June of last year, some of us already knew that decision optimization was the future of strategic sourcing. Moreover, the fact that they determined that the application of optimization tools to analyze total costs, and of flexible bidding functionality to uncover creative supplier solutions has enabled early adopters to identify an average incremental savings of 12% above those that basic, price-focused auctions alone have generated was no surprise to those of us who had been developing such technology, and monitoring its implementation success, for many years. That’s why innovative sourcing companies like Iasta (your e-Sourcing Forum blog sponsor) were already working on Bid Optimization capabilities (with version 1.0 released in December of last year) and focussed optimization companies like CombineNet have been pursuing improved optimization technologies and algorithms for over a decade.

And as you read this, I can tell you that Iasta is investing heavily in the research and development of Decision Optimization 2.0, which it expects to complete by the end of the year. Decision Optimization 2.0 will be based on the theory of Total Value Management (TVM) and continue to run on market leading solvers such as ILog’s CPlex. TVM models attempt to go beyond the capabilities of LCO (Landed Cost Optimization) and TCO (Total Cost Optimization) models by capturing the value, and not just the cost, of an award. They support qualitative constraints, to allow you to ensure the award will meet your physical constraints (durability, reliability, timeliness, low defect rate, etc.), and allow you to capture the impact costs associated with an award (such as marketing value, low return rates and high customer satisfaction, etc.) through constraints, fixed costs, and adjustments. TVM is the next logical progression in sourcing cost modeling (and an extension of the TCO modeling capabilities that were found in many previous generation modeling tools, which included MindFlow). But I digress.

Many innovative service and solution companies in the e-sourcing marketplace have been betting for the last five years (or so) that optimization is the wave of the future, but the vast majority have met with limited success (often surviving by M&A, like MindFlow, as pointed out by David in a post earlier this year) and many more are out of business.

Furthermore, the companies that have succeeded, have done so primarily due to acquisitions and other strengths. For example, Ariba acquired many of its customers from Free Markets and its customers praise them for their market knowledge and end to end platform capabilities that support integrated best practice processes from start to finish. Emptoris essentially doubled its customer base from the Dicarta merger, acquired many of its initial customers from its auction capabilities, and retained them through its own end to end platform, beefed up by many acquisitions over the years. i2 just isn’t a name I regularly hear in any sentence that contains “strategic sourcing” and “decision optimization”, and many companies that have survived, like SCA Technologies, are still relatively small in terms of customer base.

MindFlow, once acknowledged by the analyst and research groups, including AMR and Aberdeen Group, as the provider with the most comprehensive self-service platform for decision optimization, especially for CPG and Food Service, with its complex modeling capabilities and numerous constraint categories, is now virtually non-existent since the acquisition. In fact, when you get right down to it, the only company that has been around for the long haul and succeeded on optimization alone is CombineNet, and it has historically made most of its inroads in logistics and transportation, not strategic sourcing award allocation (although its customer base and focus is broadening). In fact, in a recent web search, the only recent news of significant note for 2006 that I could find in the strategic sourcing optimization arena, is the announcement in January that CombineNet is partnering in a joint venture with the University of Pittsburgh Medical Center to provide advanced sourcing solutions to the healthcare industry, as per this press release.

It is well known that many of the market leaders, and many of the big companies, are using decision optimization as (a critical) part of their strategic sourcing processes, but, considering that leaders typically make up less then 20% of the market (and that I know for a fact that not all market leaders are using decision optimization), it’s fair to ask “Who else is using decision optimization?”.

More importantly, when research has shown that repeated auctions on the same category quickly lead to diminishing returns, and often to net 0 returns after only 3 or 4 auctions, and that decision optimization often leads to incremental savings of 12% above and beyond other savings opportunities, why aren’t more companies, especially the mid-market enterprises, making regular, constructive use of this technology?

I believe the lack of use of optimization across the board is the result of numerous factors that have combined to downplay the appeal and importance of this technology over the last few years when concentrated efforts should have been made to introduce this technology as an overall component of any value-based strategic sourcing process. Tomorrow, I will discuss those factors and Sunday I will discuss why I think the time of decision optimization for sourcing analytics is finally here.

For a more in-depth discussion of decision optimization, what it is, what it is not, how it enables decision support, the benefits it provides, and strategies for success, see the “Strategic Sourcing Decision Optimization: The Inefficiency Eliminator” wiki-paper over on the e-Sourcing Wiki [WayBackMachine].

Originally posted on on the e-Sourcing Forum [WayBackMachine] on Sunday, 6 August 2006

In Friday’s post we introduced you to demand driven supply, succinctly defined as a pull-based customer-centric approach to supply planning and indicated why DDS was important. Yesterday we defined the different stages of DDS deployment, reviewed AMR Research’s recent findings with respect to the 2007 DDS marketplace, and recounted some basic statistics that demonstrate the double-digit percentage improvements that DDS can deliver across the board. Today we discuss some of the challenges associated with the implementation of DDS, review some best practices, and indicate how DDS affects the traditional (e-)sourcing cycle.

As with traditional best-practice sourcing, the first major challenge will be bringing everyone together to work as a single cross-functional sourcing team. Everyone has to work off of the same processes, the same forecasts, and the same plan.

The second major challenge will be orchestrating the implementation of new collaborative web-based technologies that will interconnect your enterprise while connecting you to your partners on both sides. These new technologies must enable real-time capture of consumption information and new analytical capabilities that can use the regularly updated market data to refine forecasts using advanced prediction techniques.

The third major challenging will be implementing a paradigm shift that transforms reactive demand forecasting to proactive demand management. This extends beyond monitoring consumer consumption on a regular basis to joint promotion management with your distributors and retailers to allow you to anticipate demand changes before they happen.

Successful demand driven supply sourcing strategies are built on understanding. As an organization, you need to understand your customer, your product, your processes, your performance, and your competitors. Who is your customer, what influences their purchasing decisions, and what can you do to increase demand? What is the end-to-end lifecycle of your product and how can you improve it? How can you improve your processes, systems, and methodologies to allow you to be more flexible and agile? How are you performing as a whole and where are your bottlenecks? How do your competitors compete on pricing, features, functions, delivery, and service and where can they out-perform you? Answering these questions will help you define efficient demand driven sourcing processes.

These demand driven sourcing processes can also take advantage of the following best practices.

• Identify where you are in the demand driven journey and outline precisely what you need to do from a people, process, and technology viewpoint to get to the next level and work as a team to get there. Bring in external consultants who are experts in demand driven sourcing and change management if needed.
• Use an iterative demand management process that generates multiple “what if” scenarios at different demand volumes in the potential demand range to determine the supply strategy with the best overall value using optimization techniques. (See my post on Lead Time Optimization for a better understanding on why you should look at ranges and not fixed numbers early in the planning process.) The best buy is not the supplier mix that is optimal at a specific demand, but the supplier mix that is optimal for most of the potential demands. After all, this is the Foundation of advanced Total Value Management that incorporates Procurement Lead Time Optimization.
• Incentivize each unit on the cross-functional sourcing team on appropriate metrics that include forecast accuracy, inventory turns, and profit targets. This will insure that everyone works off of one forecast and works together to keep it updated.

As you progress through your DDS journey, you will continuously update each step of your sourcing process to have a demand-driven focus. From a macro-level view, the sourcing process will mature as follows.

(1) Spend Assessment, Strategy Formation & Opportunity Prioritization

Price modeling and simulation to determine which products have the most profit potential will be included as an integral component of opportunity prioritization. Opportunities will be prioritized according to which have the best value from a combined bottom line perspective when profit and savings are analyzed collectively.

(2) Project Data Collection & Strategy Formation

An integrated demand forecast that takes into account retailer and market inputs is prepared as well as a methodology for updating the forecast on a regular basis during each pull cycle.

(3) eRFX & Supplier Qualification

Suppliers are qualified according to their demand-driven abilities to participate in demand planning and respond quickly to changes in demand or product cycles in addition to their manufacturing capabilities. Suppliers are also asked about their ability to scale and if they could offer discounts if demand increased beyond a certain (guaranteed) baseline.

(4) Bid Collection & Negotiation

The forecasted demand from phase 2 is updated at the last possible instance before (sealed) bids are collected or the products are put up for auction. Suppliers are asked to bid at multiple demand levels and offer tiered bids or discounts if demand should increase (based on economy of scale).

(5) Decision Optimization

Multiple what-if scenarios are run at different demand levels to determine the “best” mix of suppliers and the optimal demand allocation between the “best” mix of suppliers. The “best” mix of suppliers is the mix that can provide competitive pricing across volume levels, assist in risk mitigation (by way of overflow capacity and the ability to respond rapidly to changes in demand), and work with you to facilitate improvements across the board on both sides of the relationships.

(6) Award & Contract

Contracts are defined against a demand range. The buyer will guarantee a certain level of commitment, at the low end of the predicted demand range, in return for the supplier guaranteeing additional availability and discounts or rebates if demand spikes, which will allow the supplier to take advantage of economies of scale.

(7) Contract Monitoring (Performance & Compliance)

Customer consumption will be monitored regularly, at least weekly, and immediate action will be taken if a significant spike or drop in demand is noticed. This will generally be a combination of forecast updates, pull modifications, and / or cycle length updates. Collaboration with suppliers will occur regularly in joint efforts to improve productivity, reduce costs, and increase margins on both sides.

This concludes our introduction to demand driven supply, a logical evolution of a Total Value Management (TVM) enabled (e)Sourcing process.

For more information on demand driven supply, see the “Demand Driven Supply: A pull-based customer-centric approach to supply chain planning and execution” wiki-paper over on the e-Sourcing Wiki [WayBackMachine].

Originally posted on on the e-Sourcing Forum [WayBackMachine] on Saturday, 5 August 2006

Yesterday we introduced you to demand driven supply, succinctly defined as a pull-based customer-centric approach to supply planning that allows demand to drive the process. We emphasized its importance by way of an AMR Research statistic that found that companies who fail to adequately focus on customer demand incur an average cost disadvantage of 5 percent of revenue due to poor forecast accuracy. In addition, AMR Research has found that the distortion in non-demand driven forecasts can often cost a manufacturer more then 10% of the COGS.

Today we are going to talk about the stages of demand driven supply and AMR Research’s DDS(N) implications for manufacturers, retailers, and software providers for 2007, as noted in a recent article (and a corresponding report). But first, we are going to review some compelling statistics that serve to stress the rising importance of demand driven supply and why you should take it seriously.

According to the “Demand Management Benchmark Report” issued in December 2004 by Aberdeen Group, companies that are best in class in Demand Driven Supply outperform their competitors according to the following table:

Furthermore, more then 85% of all companies that have implemented a program to improve demand management have generated significant improvements in performance across the board, including average improvements of 4.7% in gross margin, 24% in inventory turns, and 13% in forecast accuracy. In addition, you can expect to realize in-stock improvements of 2 to 8% (significant when the average stock-out rate is 8%, or higher), customer service improvements of 18 to 25%, productivity improvements of 13 to 20%, and purchase cost reductions of 9 to 13%.

A review of the literature indicates that there are essentially four steps or stages to DDS proficiency. Although each research group (Aberdeen, AMR Research, etc.) has their own set of terminology for the stages, they essentially agree on the definitions. We will use a generic categorization of novice, beginner, intermediate, and advanced, which will be sufficient for our purposes.

Novices have not yet begun the identification and integration of DDS processes and strategies into their supply chain planning and sourcing function. They still use traditional stovepipe forecasting techniques and are constantly having to react to stock outs and stale inventories.

Beginners have just set out on the DDS journey. They are still in the process of implementing basic (e-)sourcing best practices, they have just completed integration of their enterprise systems, and all key divisions, namely product management / production / R&D, sales and marketing, and sourcing, and are all starting to work collaboratively as a well-oiled team. Although they are still using traditional forecasting methods, each department is working off of the same forecasts and they are updating those forecasts before every pull cycle. Although they have yet to realize significant benefits, they are beginning to notice improvements in inventory turns, productivity, and customer service.

Intermediates have been on the DDS journey for some time. In addition to having interconnected enterprise systems and corporate experience working as one team, they are also externally connected to business partners with whom they are starting to collaborate. Specifically, they are working closely with key suppliers to insure that the suppliers can respond quickly to changes in demand forecasts and with their major distributors and / or customers to collect actual sales data on a regular basis. If necessary, they can shorten or lengthen their pull cycles to minimize the chances of stock outs or stale inventories. Intermediates notice a number of improvements from their demand-driven processes that include improved forecast accuracy, inventory turns, productivity, and margins, although these returns are not as good as their-best in class peers, who are at the advanced stage.

Advanced Practitioners have mastered DDS and have tight interconnected systems with their suppliers, distributors, and major customers with whom they collaborate on a regular basis. They have supply chain visibility on a daily basis and monitors in place that capture significant, unexpected, spikes or drops in demands and automatically alert the sourcing team that they might need to take action, which could be a combination of forecast updates, pull modifications, and / or cycle length updates. They are maintaining significant double-digit percentage improvements across the board.

In tomorrow’s post, we will describe the challenges you will face on your demand driven journey, some best practices that you can use to begin updating your traditional (e-)sourcing process to a demand driven one, and the major changes that will occur in each major phase of the sourcing cycle. But first, we would like to discus the recent article from AMR Research that summarizes DDS(N) implications for manufacturers and retailers in the year ahead.

In 2007, DDS(N) Manufacturers:

• will be focused on customer service,
• will face increasing supply network complexity,
• will still be more confident in supply processes than demand processes,
• will see supply chains as more strategic, and
• will have a DDS(N) maturity based on their application deployment.

In 2007, DDS(N) Retailers:

• will continue to be slightly better than manufacturers at demand sensing,
• will forecast more frequently, and
• will continue to focus on DDS strategies to cut costs.

Furthermore, in 2007 demand-driven strategies are expected to fuel a 4% increase in SCM spending and companies with >1B in revenue will fund 34% of software purchases in the UK and 27% in the US. Furthermore, the market is shifting towards best-of-breed products that support an ERP backbone.

For more information on demand driven supply, see the “Demand Driven Supply: A pull-based customer-centric approach to supply chain planning and execution” wiki-paper over on the e-Sourcing Wiki [WayBackMachine].