Category Archives: Decision Optimization

Optimization Backed Sourcing Platform … Or Bust Part I


This is the first part of a five part series that revises and ties together key ideas outlined last year on Sourcing Innovation that were spread across multiple posts. Regular readers will be familiar with much of the content, but the integrated perspective should help to cement the ideas in regular readers and new readers alike.

This post is largely based on It’s Not a Suite, It’s Just Sourcing, Part I.

For a while now, Sourcing Innovation has been effectively saying that if you do not have an optimization-backed sourcing platform, you’re not ready for the modern era of complex sourcing. And SI means it. This isn’t to say that you can’t get value from a modern suite that covers the end-to-end sourcing lifecycle, or that you can’t get value from a first generation optimization platform, because you can — especially if you haven’t had these solutions before. However, every last-generation solution has a limit on the value it can deliver. Some of these limits are low, and some of these limits are quite high — so high, in fact, that it can take years, and sometimes a decade or more, for the average organization to hit the ceiling. But once that ceiling is hit, the organization has to know what comes next to continue extracting value from the supply chain. So this is a post about what comes next for the average organization and, most importantly, what comes now for the leaders who have already realized that their first generation optimization modules and / or first generation suites are failing to deliver the value they need today.

The reality is that, these days, Sourcing needs to be much more strategic and is thus not an activity that can be accomplished as a discrete set of loosely connected tasks where you can pick and choose what you need ahead of time. Strategic Sourcing is an activity that needs to both analyze the need and the market situation and respond to the stimuli the market is providing in a dynamic fashion.

This can not be done according to a pre-planned, limited set of tasks. To clarify, let’s take a hypothetical, but realistic situation. Let’s say that the company is a high-tech retailer selling custom assembled high-end development boxes to software development and engineering shops. This company will not be buying pre-configured Dell and HP machines, targeted to the consumer market, but custom configured boxes using high end motherboards, which may be manufactured by the same production houses that manufacture boards for companies like Dell and HP, high end Intel and AMD processors, ultra-fast high density DRAM, high-end solid state drives, mid-tower cases with extra fans, etc.

This might sound like a relatively easy sourcing event as there are a relatively small number of acceptable motherboard manufacturers, DRAM manufacturers, drive manufacturers, case manufacturers, and only two chip manufacturers, but even 5 * 5 * 5 *5 * 2 = 1350 and each manufacturer might have over a dozen acceptable options — and it’s hard to say up front how many of these combinations are not only viable, but acceptable (as even though it might be feasible to connect the components, there might be driver or other issues that affect compatibility or performance). In addition, new manufacturers arise once in a while and old manufacturers fail or sell out. Last year’s customer spend pattern is not the same as the spend pattern two years ago, and until year-over-year is analyzed for multiple years, you have no idea of the average deviation.

In other words:

  • you may or may not need a pre-event spend analysis to determine potential volume leverage points, the opportunities with supply base consolidation, and expected savings potential, all depending on when the last event was run, how much data you have, and current market data points
  • you may or may not need optimization; if you restrict the bid to pre-configured systems, because business is up 40% and you need a quick event to get through the rest of the year with plans to do a more detailed analysis in 6 months, you can probably get away with a weighted auction, but if bid options are open, you will probably need optimization to handle all the data
  • you may or may not need multiple RFX rounds, so you may or may not need a supplier portal to handle the communication necessary for a multi-round event

And this is all fairly obvious, so you are probably thinking

  • if I need the analysis, I invoke the spend analysis module, get my insights, and plan my strategy
  • then I invoke the RFX module to create the RFX
  • if I am doing multiple rounds — I have to configure the Supplier Portal instead of just sending out the Excel spreadsheets (which I would import on return otherwise)
  • when the data is retrieved, validated and cleaned up then I either
    • push it into the auction for a weighted auction or
    • push it into the optimization module for optimization-backed analysis
  • when I have my winners, I push the data into the contract management module for draft contract creation

… easy-peasy, right?

Wrong!

This is the real world, and it never works this way, as we will discuss in our next post.

RiverLogic: Bringing Optimization to the Enterprise

In our last two posts on Beyond Sourcing Optimization we noted that Strategic Sourcing Decision Optimization (SSDO) is just one area where optimization can be successfully applied in a progressive organization that is a leader in its industry. An average enterprise organization is ripe with opportunities for the application of optimization technology — optimization technology which can easily add up to 5% to the bottom line if properly applied.

RiverLogic, which is advertising prescriptive analytics technology and enterprise optimization, is one such company that is tackling enterprise optimization. Advertising holistic decision support that performs simultaneous optimization of the entire business model, the optimizer is capable of integrating demand, production, and inventory optimization into a single holistic optimization model that, when run, optimizes production and inventory against demand to maximize profit by optimizing revenue against production and inventory (carrying) costs.

As one may have gathered by our previous post, this is no easy feat. Production optimization requires one type of model that understands production line throughput, machine utilization overhead costs per hour or unit, associated workforce requirements, associated costs during regular and over time, raw material inventory costs, and logistics costs at different production levels. Inventory optimization is a different type of model that must take into account the myriad of costs that contribute to the amortized inventory carrying cost and that include, but are not limited to, warehouse overhead costs, labour costs, and depreciation costs and balance these against logistics costs from more or less frequent orders. Finally, demand optimization is its own beast as one has to have a relatively good understanding of the different types of marketing spend and how each will influence market demand, the costs of production at various volume levels and delivery commitments, the associated lifecycle costs including outbound distribution costs, warranty and service costs, and any end-of-life reclamation costs (if one or more locales in which the product is being sold have mandatory reclamation or recycle laws for the product or one or more of its components). Now, one can create a mega-model that encapsulates inventory and production costs into the demand optimization, but it’s not easy, and that’s why few companies have tackled this problem (just like few companies have tackled true SSDO). And most that have tackled this problem do so by building custom models for their clients that require individuals with advanced degrees in Mathematics or Operation Research to run.

However, the RiverLogic platform, like leading SSDO platforms, comes with this model “out-of-the-box” and all a user has to do to build a basic model is get the data. At this point you’re probably thinking this is a show-stopper as

  • the amount of data required to populate such a model is extremely extensive and
  • outside of the ERP, no one system has even a fraction of the data required.

RiverLogic understands this perceived dilemma as well and that’s why their platform integrates with over a dozen major ERP and Accounts Payable systems because when you get down to it, that’s where the majority of the cost data required for a holistic demand optimization model, that simultaneously balances inventory and production, resides. Once the proper integrations are done, the model can be run out of the box and the organization can instantly see relative to its demand forecast the optimal production and inventory levels (and, as a bonus, the optimal distribution plan and cost model as logistics are also accounted for in this holistic model). This will allow an average organization that has not simultaneously balanced these models before to shave at least an extra 5% to 15% off of overhead costs and, if one or more of these models haven’t been run before, even more. And these savings will trickle down straight to the bottom line the instant the plans are updated.

But the power of the platform doesn’t stop there, like the best SSDO solutions, it also supports powerful what-if optimization that allows the organization to see how production and inventory plans change if the demand projections were to change, if more money was allocated to marketing, or if the estimated impact of marketing campaigns were more or less successful than initial predictions. This model can be run any time and plans updated dynamically, taking effect with the next (automated) order upon publication to the demand / inventory / order management module of the ERP(s) that the platform integrates with.

Now that leaders like you are using decision optimization in your Sourcing and advanced spend analysis in your planning, you’re ready to apply that knowledge and capability across enterprise operations and, as such, are ready for the enterprise optimization (and prescriptive analytics) that innovators like RiverLogic are offering. RiverLogic is one of the handful of companies you’re going to be hearing a lot more about in the coming year
and one that should definitely be on your radar as you look to take cost control across the enterprise (because what good is saving 10% on a category if poor operations just eliminate that savings after the deal is signed?).

Beyond Sourcing Optimization: The Best Bundle is Only the Beginning Part II

As per our last post we recently discussed the criticality of optimization in
It’s Not Optimization, It’s Strategic Sourcing, explained that Even “Simple” Categories Hide Extreme Complexity, and pointed out Why Your First Generation Platform is Not Ready for Modern Sourcing in the hopes that you would understand that you need to be ready for Complex Sourcing.

But Strategic Sourcing Decision Optimization (SSDO) is only one area where optimization can be applied to add organizational value. There are at least half a dozen other areas where optimization can be successfully applied in a progressive organization that is a leader in its industry. As noted in our last post where we briefly discussed inventory optimization, production optimization, and demand optimization — three areas that can hide considerable cost savings when properly analyzed — there are a number of areas in an organization where optimization can identify considerable value. Today we will discuss three more areas.

Service (Level) Optimization

The goal of service level optimization is to find the right balance between customer satisfaction and service cost to maximize profitability while minimizing customer dissatisfaction. While everyone would like a robust high-quality product that lasts until they are done with it, that’s not always feasible. The very nature of many product lines — electronics, automotive, plastic-based CPG is that the products will break down with regular use and/or regular exposure to the elements. Better materials, better manufacturing, and better care will lengthen lifespans, but computing equipment, cars, and plastic boxes don’t last forever. And if the intended lifespan is 3 years, it’s not cost effective to build a product expected to last 13 years. However, the nature of things is that if you build a product expected to last 3 years, some units will breakdown and need to be replaced before the 3 year mark is up (and some will last longer). So what level of warranty do you need to offer, what level of service (in terms of repair / replacement window), how much will it cost, and how much will the market bear. Finding the right base / extended offerings and price points is key to maximizing both consumer demand and customer satisfaction.

Asset Optimization

Big organizations have a lot of assets, often assets they often don’t know that they have. For example, unused compute power in the data centre, unused production time at the factory, and unused equipment in the yard. This last category can be a huge burden to an organization that is paying a lease or amortized monthly payment on a 10-year plan for equipment that is only being used part of the time. (That’s why renting by the job or renting out might be a better solution.) And if an organization is continually renting the same equipment for multiple projects, it might make sense to buy and share the equipment between projects, even if it has to be transported between locations. Asset optimization can save an organization a lot of money and can often, when done properly, considerably increase working capital. This brings us to:

Working Capital Optimization

Optimizing the balance between assets and liabilities, working capital ensures that a company has sufficient cash flow in order to meet its short-term debt obligations and operating expenses without creating a crushing debt load or jeopardizing long term profitability. Working capital optimization is tricky as it involves balancing with DPO (days payable outstanding) with DSO (days sales outstanding), early payment discounts (on the inbound and outbound supply chain), supply chain and invoice financing, short and long term investment opportunities, and short-term gains vs. long term cost reductions. It’s not easy, and, like all of the other examples of optimization covered in this brief two-part series, often requires sophisticated optimization.

These are just a few of the examples where optimization can yield significant benefits beyond sourcing and where Sourcing can bring additional savings and value to the enterprise since it will be able to collect a lot of the data and intelligence that is required to build, and solve, the sophisticated models required.

In future posts we will discuss these types of optimization in depth as well as the new breed of providers tackling these types of supply chain optimization. Stay tuned.

Beyond Sourcing Optimization: The Best Bundle is Only the Beginning Part I

We recently discussed the criticality of optimization in
It’s Not Optimization, It’s Strategic Sourcing, explained that Even “Simple” Categories Hide Extreme Complexity, and pointed out Why Your First Generation Platform is Not Ready for Modern Sourcing in the hopes that you would understand that you need to be ready for Complex Sourcing.

But Strategic Sourcing Decision Optimization (SSDO) is only one area where optimization can be applied to add organizational value. There are at least half a dozen other areas where optimization can be successfully applied in a progressive organization that is a leader in its industry. In this post we’ll outline some of the best opportunities, a few of which have been covered before, and in future posts over the next year we will dive deeper as we introduce you to some of the companies exploring the use of optimization in these areas to bring your operations the same level of savings that your SSDO vendors are bringing the Sourcing and Procurement organization.

Inventory Optimization

Inventory optimization can be defined as the act of balancing supply and demand uncertainty to meet a desired services level at a minimum level of investment. But this is easier said then done. Not only do you have to consider the myriad of carrying costs that need to be balanced — warehouse rental costs, labour costs, and depreciation costs — but also take into account the costs associated with stock outs, alternate distribution costs if inventory is improperly distributed, and lead time costs, and try to balance them all.

Production Optimization

Optimizing inventory is a good start when it comes to reducing overhead costs, as inventory carrying costs can be as high as 25% by some estimates. However, production costs can also be unnecessarily high if production is not optimized. Production line down time is costly, and a production line goes down every time it is switched up to produce a new product (or a new variation). Thus, it’s not always best to plan production by order volume, but by total volume for a period, optimizing production runs to maximize throughput (and worker time), minimize downtime, and, most of all, minimize switching times. Especially if order volumes vary and part of the year would otherwise require overtime to meet demand.

Demand Optimization

The counterpart to production optimization is demand optimization. Not only does it cost the organization hard dollars to carry inventory unnecessarily or use poor production plans, but it also costs the organization hard dollars to product unprofitable product lines or cater to unprofitable customers. For each product line there is a production cost, a marketing cost to increase demand, a cost of goods solds (COGS), and an opportunity cost from not producing a potentially more profitable product line. Demand optimization is optimizing what product lines to produce, how much to invest to shape demand, and when to produce those product lines. It optimizes organizational profit by focussing on profitable product lines and marketing activities versus marginally profitable or unprofitable activities.

And these are only a few categories where optimization can increase performance, and profit. In part II, we will tackle three more areas. Stay tuned.

Why Your First Generation Sourcing Platform Is Not Ready For Modern Sourcing

First generation sourcing platforms, circa 2005, were a miracle cure for the average Sourcing organization that was drowning in data and demands to save, save, save without enough time or resources to tackle even a fraction of the categories that needed to be under management.

First generation Spend Analysis systems helped the Sourcing team identify the largest spend categories and the largest organizational suppliers, which were prime candidates for the first strategic sourcing evens put through the new sourcing platform.

First generation RFX systems helped the Sourcing team capture more data from more suppliers than ever before and not only better qualify potential suppliers but collect more detailed bid breakdowns for analysis.

First generation e-Auction systems helped the Sourcing team put non-strategic high-dollar categories with very little complexity out to bid for quick savings success.

And, most importantly, first generation decision optimization systems allowed the sourcing team to build realistic cost models, capture constraints, and devise realistic award scenarios that identified real savings.

Many organizations that acquired these suites and applied them successfully saw year-after-year returns of 10%+ on the spend brought under management. And a few are even seeing some savings today, but just like the second auction saw little savings and the third auction saw a price increase, the year-over-year return is dropping. Why? Because while these first generation platforms were infinitely more powerful than anything that had come before, they weren’t designed to capture the full extent of complexity in an average category — complexity that has been considerably increased since the early days of sourcing due to increased outsourcing, increased globalization, increased regulation, and a constantly evolving global marketplace.

The following staples of first generation sourcing platforms just don’t cut it anymore.

  • Limited Form-Based Data Collection
    that don’t allow the full breadth of responses a supplier could provide to be captured
  • Built-in Static Reports (with limited 2-D graphing options)
    that don’t evolve as organizational needs evolve
  • Single User Sourcing Events
    that don’t take into account that complex categories require entire teams
  • Limited Approximations
    that force high order cost approximations and don’t capture true cost definitions
  • Fixed Workflows and No Templates
    that don’t adapt to organizational needs

If you want to know what is needed in a sourcing platform to handle the full extent of the complexity in today’s categories, download Sourcing Innovation’s recent paper on Complex Sourcing: Are You Ready, sponsored by Trade Extensions, and find out once for all why optimization is the plan and not just something reserved for a handful of strategic events.