Category Archives: Decision Optimization

What Do Bid Optimization and Corporate Strategy Have in Common?

Last month, Pierre penned a very interesting piece over on Spend Matters when he asked “What Do Bid Optimization and Corporate Strategy Have in Common” and answered Everything. SI doesn’t entirely agree, but definitely agrees that bid optimization and corporate strategy have a significant amount in common.

Pierre is 100% correct when he says that people who say sourcing optimization is too complex and a nice area barely used and needed by most sourcing folks are dead wrong. As Pierre says, ignorant statements like this throw out the philosophy, methodology and techniques that are behind the tool. Optimization is more than a mathematical model embodied in a piece of software — it is an encompassing process designed to make sure you achieve the most value from your efforts. It starts during the problem definition phase where you define your objectives and then figure out the appropriate model, data elements, options, methodology and measurements of success — not after you’ve collected a bunch of semi-random data.

Furthermore the methodology employed is critical and relates to corporate strategy. Pierre says the overarching methodology is corporate strategy, but SI believes that the relationship is a little more subtle — corporate strategy limits the methodologies that can be used. The objective of corporate strategy, which is a fact-based process of developing strategic scenarios and then determining how to best allocate finite resources to support various business objectives and a balanced scorecard, is to define strategies that should be “optimal” in the sense that they minimize trade-offs and are doable by the various stakeholders. The chosen strategy limits the methodologies that can be employed because only so many methodologies will support the strategy, and only a subset of those will be capable of delivering an optimal result. For example, if the corporate strategy is to dominate a foreign market, then the methodology employed must support getting more knowledge and demand for the brand, getting the product on more shelves in the market, and making it affordable for the consumers in that market. The methodologies would have to support developing advertising congruent with the market, logistics efforts that work on the ground, and cost control to insure the product could be priced to maximize sales. If the corporate strategy is to reduce costs to improve the bottom line, the methodologies would have to support better sourcing, more efficient production/distribution, and better supplier relations. With respect to better sourcing, we’re limited to strategic efforts — tactical or catalog buys are out. And with respect to cost minimization, depending on the product being sourced, where it is being sourced from, and the market dynamics, this may dictate an open auction, a multi-round negotiation, or decision optimization based upon final best bids, logistics costs, incurred and usage costs, and/or other value drivers. Optimization doesn’t always mean the most complex model you can imagine, but it does mean insuring everything you do is optimal and that every sourcing event is driven off of a lowest-cost baseline, which is easily calculated by a decision optimization solution built on a proper model. (This is because you only spend more if you get value back — whether it is better quality, marketing power through the supplier’s brand, or joint development efforts — that is at least equal to what you spend.)

And when you apply the proper methodology and process to a category, as Pierre explains, it will improve how Procurement will tap supply market power to help the stakeholders meet their objectives. After all, resources are limited, stakeholder requirements are diverse, and trade-offs and constraints are plentiful — which is the precise problem strategic sourcing decision optimization was designed to solve. And when scenarios are developed with stakeholders during the planning processed and then used to improve the robustness of the category strategy, how can you not win?

In short, corporate strategy has a lot to do with bid optimization, as it drives the sourcing strategy and model objectives, and you really can’t succeed in one without the other. They have a lot in common. Not everything, as some aspects of strategy have nothing to do with bid optimization (such as advertising tactics employed or market positioning, although other types of modelling will be used to determine the expected effects of each potential strategy), and some aspects of optimization and based purely in math and logic (which not all strategies are).

And using the two hand-in-hand makes perfect sense. So, just like Pierre, I have to ask why is it when we take this approach to a specific market basket and sourcing project, it somehow becomes an obtuse technology thing rather than just doing good strategy work? It just doesn’t make sense. Without both, you are playing a game of win, lose, or draw with a greater chance of losing or drawing then winning.

Remember, the power of “collaborative sourcing” or “market informed sourcing” is not the tool, but rather the philosophy of cross-functional teams doing scenario planning, defining what they really want as an objective, reducing or eliminating unneeded constraints, and fully tapping supply market power. The optimization tool is just an enabler, albeit a critical one.

Go, Pierre, Go.

While You Were on Summer Vacation, Vendor Posts, Part II

While you were on summer vacation, SI was powering away with daily posts and continuing to cover some of the leading vendors in the space, presenting a number of deep dives on their technology platforms. Here is a short recap of some of the coverage you might have missed!

Trade Extensions

In our post on Trade Extensions (TE), where we noted that there is still no rest for the wickedly powerful, we told you that their coders never sleep (or at least not very often) and that, since SI’s last coverage in 2011, they have added more powerful fact sheets, enhanced browser-based reporting and visualization, and a formula analyzer – that pacts a much bigger punch than you’d expect. It’s often the case that a user has no clue why one model solves in a second and an almost identically sized similar model is still being processed an hour later. This is because the more complex the models get, the harder it is to pin down why they aren’t quite doing what they are supposed to be doing. The TE formula analyzer allows a user to analyze a formula and see how it is defined, how long it is taking to calculate with respect to the other formulas in the model, and what is affected by the formulas or changes to the formula. In addition, if they exist, it can suggest formula modifications that would allow the model to solve faster. However, just knowing where the problem lies is a great help if a model is solving slow.

Coupa

In our post about how it’s 24/7 for Robbie and the Coupa Factory, Part III, we noted that Coupa had completed Release 9, were on their way to finishing Release 10 (now available) by the end of the quarter, and had just released a new e-Sourcing module, which made them one of the first providers to offer an integrated end-to-end e-Sourcing and e-Procurement solution. Their new sourcing offering, which is e-Sourcing 1.0 with RFPs, RFQs, RFIs, basic reverse auctions, and basic project management and not much more than you’d find in any basic e-Sourcing suite, is still enough for an average mid-market company and impressive in that it’s as easy to use as the rest of the platform. It’s a quick way for a company using Coupa that does not have a sourcing solution to transition from Procurement to Sourcing. Plus, when you add the new expense management capabilities and catalog functionality, it’s a very quick way for a mid-market organization behind the sourcing and procurement curve to get closer to where they need to be quickly.

Kinaxis

In our posts about Kinaxis and their new paradigm for real-time end-to-end supply chain management (Part I, Part II, and Part III), we described how this extremely unique Supply Management vendor offers a single platform to take your Supply Management Operations to the next level once you have implemented e-Procurement and put your spend under management, optimized your strategic sourcing, mastered e-Transportation and Trade Management, achieved e-Visibility to manage your risk, and optimized your network design. This platform, which is successfully used by product, risk, and change managers in Supply Management to manage demand, do S&OP, undertake supply & capacity planning, do production benchmarking and scheduling, manage inventory, handle new product introduction (NPI), perform order analysis and planning, manage supply, improve profitability, and collaborate with suppliers, among other things, is designed to allow supply management professionals to get answers to strategic planning questions like the following in real time:

  • what is the impact of a supplier shutdown due to a fire in the plant?
  • how can I launch a new product a quarter early?
  • what if a user mistakenly changes an inventory parameter?
  • what would happen to our ability to fulfill demand to our other customers if we accelerate fulfilment of an emergency order for a preferred customer?
  • how can we effectively reengineer our planning processes

The Kinaxis solution supports very complex, but easily generated, what-if scenarios that will allow a user to ask these questions and get an answer in a few hours, as compared to the days, or weeks, it would have taken them in the past.

Come back Monday and we’ll tell you about three more recently covered companies you might have missed!

Supply Chain Network Design – It’s Not a Five-Year Plan

Last fall, Supply Chain Digest published a piece on Supply Chain Network Design Where the Real Money Is that noted that many companies limit the scope of a supply chain network study to distribution centres and customer service targets and fix everything for the next five years. As such, they leave a lot of money on the table. Why?

The answer is obvious if you think about what you’re shipping. Generally, CPG. And what’s the lifespan of the average CPG product these days? A heck of a lot less than five years. So even if you optimize your supply chain to the penny, as consumer tastes shift, and manufacturing locations shift as a result of technology (or natural disasters or bankruptcies that shut a plant down), your optimized supply chain begins to fall apart quickly.

Supply Chain Network Design needs to be continuous. And while it doesn’t have to be re-optimized with every new award, it should be re-analyzed and tweaked annually. This is one reason why you should consider leasing versus buying and signing shorter term contracts, even if there is a small price premium to do so. It’s also a reason why you should avoid locking in too many long term Freight or 3PL contracts (especially when you can BuyTruckload when you need to).

As SI said back in 2007, the nature of distribution network optimization is that it cannot be optimized within a single sourcing scenario, and any attempt to do so is likely to do more harm than good. To truly optimize your network, you have to optimize across all of your buys, and even in any given year, you’re likely renegotiating less than a third of your major contracts and a quarter of your buys, and you don’t expand into new countries overnight. That’s why it should be regular and pseudo-continuous.

Furthermore, like SI said back in 2007, the way to start to optimize your distribution network costs is to semi-annually or annually analyze all of your projected transportation needs over the next 6 to 12 months using all of your projected shipments (based upon current contracts, forecasts, and current patterns), aggregate volumes across lane groups (defined as the set of lanes that take a product from region A (such as a set of posts on the southwest coast) to region B (your major re-distribution center outside Chicago), bid out the appropriate lanes or lane groups to one or more carriers, and optimize a transportation award to these carriers who quote rates based upon minimum volume guarantees (such as 75% of expected volume across the lane). Then you should be re-optimizing the flexible aspects of your distribution network. You start by re-evaluating warehousing space that you are leasing or that is highly liquid and could be easily sold, re-evaluating the air and ocean freight options to you, re-evaluating the ports you are using, and re-evaluating your shipment consolidation strategy (should you always wait for shipments from multiple suppliers to fill the container or should you use a third party that can consolidate shipments for multiple buyers to fill the container). Finally, when fixed assets free up and can be renegotiated, you should be re-optimizing the distribution network to the extent possible.

And when you optimize continuously, you identify savings over the long term.

Trade Extensions: Still No Rest for the Wickedly Powerful

In our last posts on Trade Extensions (No Rest for the Wicked-ly Powerful, Part I and Part II), we talked about how Trade Extensions (TE) added real-time decision optimization auctions, award management (that allowed a user to fix the award for part of a scenario and re-run a smaller model for what-if), built-in OLAP reporting, and supplier feedback mechanisms to their platform to increase the power, usability, and friendliness of their platform. Since then, as per our recent post on Optimization: Is it Time to Move Beyond Sourcing, Trade Extensions has been toiling away to increase the power, flexibility, and usability of their platform to take it beyond sourcing.

Trade Extensions has made significant improvements in the following three areas:

More Powerful Fact Sheets

Back in Trade Extensions Trades Up to a Fact Sheet User Interface, we talked about how Trade Extensions had built the capability for the end user to provide data in d-dimensional fact sheets, which include 2-dimensional spreadsheets and 3-dimensional workbooks, in order to allow the user to define models in a familiar format. Fact sheets could be used to define any model data element in simple row-column data format. In addition, a user could define certain values as simple formulas on other values in the sheet. Since their initial introduction three years ago, Trade Extensions has extended the capability to allow users to define more complex models with more complex formulas that can reference not only values, but formulas, and values and formulas in other fact sheets. Models can get as complex as they need to, and this is the foundation that allows Trade Extensions to define models that go beyond sourcing.

Formula Analyzer

The more complex the models get, the harder it is to pin down why they aren’t quite doing what you think they are supposed to be doing, why they are taking so long to solve, or what is driving the sensitivity. That’s why Trade Extensions built a formula analyzer that allows a user to analyze a formula and see how it is defined, how long it is taking to calculate with respect to the other formulas in the model, and what is affected by the formulas or changes to the formula. In addition, if they exist, it can suggest formula modifications that would allow the model to solve faster. However, just knowing where the problem lies is a great help if a model is solving slow.

Enhanced Browser-Based Reporting and Visualization

OLAP is good, but the ability to do real-time drill-downs, data segregation, reformulation, and graphing in the browser is even better. Noticing that a number of clients were exporting the scenario results and importing the results into a third-party reporting tool with more powerful data analysis and graphing capabilities, Trade Extensions built their own full-fledged rules-based data analysis package (like TS Insight and IQub and a host of others) that allows a user to do the real-time drill-down analysis required to understand complex models in the browser so a user never has to leave the Trade Extensions application. The ability to drill down and reorganize dimensions equals what you will find in the more advanced data analysis applications.

Put these new capabilities together, and a user is truly able to build, analyze, solve, and explore more complex beyond sourcing optimization models than they would have ever thought possible just a few years ago.

Optimization: Is It Time to Move Beyond Sourcing?

A big focus of this blog is, of course, Strategic Sourcing Decision Optimization (SSDO), one of the few advanced sourcing methodologies guaranteed to save your organization, on average, 12% if correctly applied (as demonstrated in two back-to-back studies by Aberdeen) and the doctor‘s speciality. But it’s not the only place you can apply optimization in Supply Management to save money. Another area, as covered a number of times on SI, is Supply Chain Network Optimization (SCNO). And, of course, some companies just focus on the intersection and do Logistics optimization. But this is not everything that can be done, or should be done, especially in an age where many industries now see The End of Competitive Advantage and don’t actually own physical assets, leasing them as need be to create the products and services desired by their prospective customers.

In this situation, what matters is Asset Optimization, where you optimize a one-time dynamic network to minimize sourcing, network, and logistics costs to minimize the total supply chain costs associated with the product you wish to produce. This is easier said than done. In sourcing, you are mainly considering bids, lanes, and associated costs to compute the optimal TCO (Total Cost of Ownership), and if lifetime costs and metrics are available, or TVG (Total Value Generated) with respect to a fixed situation. In network optimization, you are optimizing the location of owned factories, supplier production centers, warehouses, and retailers to optimize the distribution costs. But in asset network optimization, you have to simultaneously consider the network and associated distribution costs, the sourcing requirements and associated production costs, and the costs of using, or not using, the resources you already have available and contracts you have already negotiated. In addition, you have to consider the risks associated with each potential supplier and location, the sensitivity of the overall asset network to each supplier and location (and is there a single point of failure), and the ability to dynamically alter the network should a failure occur or customer demands change.

Plus you have all of the difficulties associated with each type of optimization. With respect to the network, there will be many alternatives for production site, each site will have multiple, and different, asset lines, and each asset will be qualified for a certain operation with respect to a certain product. In addition, some assets will be more efficient and cost effective, and unqualified assets will have a qualification/certification step, which will require limited manpower – a variable that does not need to be modelled in traditional sourcing or SCNO models. It’s a very difficult problem that requires modelling of multiple types of variables and constraints at multiple levels at multiple times. And this last requirement makes the model even more complex. In a traditional sourcing model, you don’t really need to consider “time”, as it doesn’t matter how often the trucks deliver your product, just how many trucks are needed to deliver your product as you are billed FTL or LTL by the delivery. And it doesn’t matter what production schedule the supplier(s) use(s) as long as your products are ready on time, so only the total volume need be considered. But when you are dealing with production models, especially when trying to dynamically construct and optimize an asset network, production schedules are significant. If a certain location only has 30% of capacity left available and can only schedule it during a given timeframe, that has to be taken into account. If some of the products have to be delivered before they can complete the first production run, then there has to be a location that is able to do so. And if a continual supply is needed over nine months, the production cycles should more or less line up with minimal overlap as, otherwise, inventory costs would soar.

It’s a complicated problem, but one that is becoming more and more important in fast moving industries such as fashion and consumer electronics — and one that most SSDO providers can’t address. But I’m happy to report that there are a few optimization vendors in the space who can. One is Algorhythm, in India, that has been doing SCNO for many years, and who has built up a lot of this capability over time while working for it’s global multinational clients such as Unilever. Another, newer entrant, is Trade Extensions, that has been doing SSDO for many years and, at the request of its major multi-national clients, including P&G and Coca-Cola, built up the capability in their solution with innovative new platform enhancements since SI last reviewed their solution in 2011 that make it very easy to define the models, run the scenarios, compare and navigate the results. A few of these enhancements will be described in a future post. Stay tuned!