Category Archives: Decision Optimization

Optimization: Is it at least time to move beyond logistics and indirect 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 last decade and discovered over and over again by organizations applying it properly) and the doctor‘s specialty.

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.  And track all of that information.

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.

Plus, 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 time-frame, 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. Why?

First of all, they don’t track the necessary data.

Secondly, they don’t have the right underlying optimization platform.

Third, they don’t have the skills to build the right model.

But recently, a few of the bigger players with optimization have started not only tracking all of the direct (material) sourcing requirements, but assets as well.  So the data is there.

Secondly, a few of the optimization platforms have become significantly more powerful and flexible (and now have the necessary computing power under the hood to support them) and could, at the very least, run a series of optimization models (according to different time-spans, which minimizes the need to consider complex timing constraints in a single model) to tackle problems such as this.

Thirdly, there are independent experts with decades of experience who can help design the right model.

So why are none of the big players doing it?  It seems logical, and soon necessary, if an organization wants to continue to identify, and capture, new sources of value year-over-year.

IS TCO a No Go Without Optimization?

At this point in time, very few people are still in the stone ages of Supply Management and buy on price per unit (PPU) alone, the first level of sourcing value. However, there are still a number of buyers in a number of organizations that still buy on landed cost or total cost of acquisition (TCA) and buy solely on the sum of price per unit, transportation, duty, tariff, temporary storage, and other costs that are incurred from the time an order is placed until the time the product is received. These organizations are still in the dark ages of Supply Management and need to find the light very, very quickly (especially with Trump Nation and Brexit on the way). And while most modern Supply Management organizations attempt to buy on total cost of ownership (TCO), the third level of sourcing value, not all succeed.

TCO is the most commonly used metric today by analysts, consultants, vendors, and (I’m sorry to say) bloggers alike. It is designed to be a comparative cost metric that quantifies the overall cost of each acquired unit from a direct, indirect, and quantifiable market perspective that takes a broader look at the cost of a product from an acquisition, utilization, and delivery perspective. In addition to the landed costs, it typically also considers indirect utilization, supplier switching, and transaction costs as well as cost adjustments for quality, waste, and brand power (if your supplier has a brand that increases the selling price of the product you create with the component).

TCO is designed to capture the ‘true cost’ of a product (or service) from a supplier and does a much better job of helping you to compare apples-to-apples when determining the best buy for your organization. And even though it’s not the ultimate metric, as that’s total value management (TVM), the next level (and pinnacle) of sourcing value measurement, you cannot apply TVM until you have mastered TCO (which is a big component of TVM just like total cost of acquisition is a big component of TCO), and you can’t master TCO until you can model it.

But most sourcing solutions don’t let you model TCO. And the few that do don’t let you optimize it. That’s why it’s important when selecting a strategic sourcing solution you get an optimization-backed solution with support for deep cost models and, preferably, bills of material. They might still be few and far between, but a few more hit the market in the past year, and we expect more will be coming due to the power, and utility, of such solutions.

So is TCO a no-go without optimization? Not necessarily, but it sure is a lot harder to do without optimization.

Are You Ready To Get Optimized But Don’t Know How? Read On!

Now that you’ve read our last two posts and understand that you need to get optimized (and analytical) if you want to get cognitive, hopefully you’re ready to get optimized but you just don’t know how.

The four-part answer is pretty easy.

1) If you are using a sourcing platform from a modern provider that offers optimization, acquire the module and start using it.

If you’re already using (SAP) Ariba, Coupa [Trade Extensions], EC Sourcing [with bidmode Inside], Jaggaer (Indirect/Direct/Advantage), Keelvar, or SynerTrade, acquire the sourcing module, turn it on, and start using it. We know that not all platforms are equal (as made clear by the Optimizer Persona in the Spend Matters Solution Maps), but all are more than enough when you are just beginning your sourcing journey. Plus, the majority of these providers are all actively developing their optimization solutions and should stay ahead of your optimization needs.

2) If you are not using a sourcing platform, get one that has decision optimization.

We gave you six names, and these six names can all help you. While we have our preferences, the right solution is utterly dependent on your organization size, industry, dominant categories, geography, and culture and which provider matches your profile the best. There’s only six names, and a relatively short RFI should allow you to quickly zero in on the 2 or 3 that are most likely the best for you.

3) If you are using another sourcing platform and it is not meeting your needs and can replace it, replace it with an optimization-backed sourcing platform.

A few of these providers have a large customer base that consist of those that have switched from another provider with a solution that didn’t meet their needs and, thus, have a lot of experiencing with change management, fear squashing, migrating your data over, and getting you up and running on the right processes quickly. Simply craft the right RFI and you will quickly zero in to the 2 or 3 providers that will likely be the best fit in this situation.

4) If you are using another sourcing platform and it is meeting your needs, can’t be replaced at the present time, or both, augment it with an optimization-backed sourcing solution just for those events where optimization is a must-have.

You just bought Source-to-Contract or Source-to-Pay Solution X a year ago and you know that Finance / Operations / etc. will not approve a new solution for at least a few years because they still believe systems should last five to ten years. In that case, you get a pin-point solution that you use to augment your current solution as a bolt-on. Two of the providers in particular that we mentioned — EC Sourcing with bidmode Inside and Keelvar — are small, mid-market focussed, pin-point best of breed optimization-backed RFX solutions that start in the six figure range (or five figures on an event basis) that can be used to augment a traditional Sourcing platform at a low cost and deliver a high value.

And, no matter what Don’t Say It’s Not That Easy. It is. Yes it’s work to create the technology RFX, reach out to the vendors, make the short-list, do the negotiations, select a (new) vendor, create a transition plan, create an integration plan, and get it done. But making the decision to get a platform that will save your organization an average of 10%+ year-over-year and taking action to do it is easy. And there’s no situation there isn’t an answer for. So, just do it. You won’t regret it.

You Want to Get Cognitive? First Get Optimized!

The new “cognitive” buzzword is getting a lot of people interested in modern Sourcing and Procurement technology, and that’s a good thing, except when it isn’t. (How can it now be? Not all providers truly offer cognitive capabilities, not all are equal among those that do, and not all are right for your organization.)

And unless you truly understand what cognitive sourcing can do, when it should be used, what technologies you need to power it, and how to properly apply it, the answer is no cognitive sourcing is right for you.

When it comes to sourcing, a sourcing solution must meet a number of requirements in order for it to be considered cognitive. It must be capable of:

  • supporting advanced cost models
    to allow for an accurate determination of should cost
  • supporting sophisticated automated data collection to populate those models from market indices, statistics bureaus, public (government) data repositories, etc.
  • supporting a large repository of trend analysis algorithms
    to help an organization understand market dynamics
  • support sophisticated analytics
    to help organizations slice, dice, and compare all the insights extracted by the cognitive platform
  • support advanced optimization
    to analyze the cost models and all the supply and logistics options available subject to business constraints

If you look at each of these requirements in comparison to an average Procurement organization with some semi-modern Supply Management technology

  • they have some cost modelling capability in their ERP
  • they have some automated data collection around risk and commodity costs through providers like D&B and Ecovadis and Market Index data providers
  • they have some familiarity with trend analysis in their inventory management systems
  • they have adopted a spend analytics platform, which may be a generation behind, but still gives them some cost insights
  • but they have no decision optimization at all

So if you really want to get cognitive, get optimized. Without a good understanding of what optimization can do, and how to use it, how do you expect to figure out when to apply, and not to apply, cognitive sourcing technology properly.

Will the Trade Wars Be Good for Advanced Sourcing?

Trump is imposing tariffs. China is retaliating. And this is just the beginning. As a result, supply risk and the need for spend forecasting is finally becoming real. But is it becoming real enough for organizations to take action? It’s hard to say. But one thing we do know is that the only way organizations can progress forward is to better understand not only the risks, but the costs.

What are the risks? Many. What are the costs? Significant. And how can you know of either? In the first case, you need to monitor the news, the sentiment of the responses in regards to the news, crowd-source some predictions, and run some advanced analytics on all this data to determine the probability of something happening — and sticking.

And in the second case, you build should cost models with current data, and projected data, to determine the impact of a tariff on the total cost of ownership of the product. This means that a simple RFX or Auction platform is just not enough – an organization needs a platform with deep should cost modelling and the ability to create what-if should-cost models based on projected and anticipated changes.

But even that’s not enough. If the projected increases are significant, then the organization will, at the very least, need to reallocate global supply chains to insure that products, which are currently sourced from multiple suppliers and/or locations, are being exported from and imported into the most cost effective locales the organization has access to. And if this is not enough to keep costs under control, then the organization may need to even source from additional suppliers (in different locations) or re-source the entire category (to the extent possible).

But it’s hard to figure all of this out without an optimization backed sourcing platform. Hopefully this is the kicker that is needed to get these powerful analytical platforms into the hands of more Sourcing and Procurement organizations, as these platforms are desperately needed and reduce spend on analyzed categories by an average of 10%+ year-over-year, making their ROI immense.

But, alas, only time will tell. But if bankruptcy could be on the line (when a tariff wipes out the entire profit margin), maybe this time these platforms will finally take hold.