Category Archives: Decision Optimization

Algorhythm: Still Pounding Out the Optimization Rhythm on the Tabla (Part I)

Since I last covered Algorhythm and their supply chain optimization rhythm, they’ve been pounding out a steady beat and extending the breadth and power of their unique supply chain optimization platform. Not only do they have extensive optimization capabilities in production planning, network planning, and logistics planning — with specialized solutions for oil, steel, and packaging, but they now have a best of breed multi-echelon inventory optimization capabilities and a best of breed distribution network design optimization platform that can take multi-echelon inventory requirements into account and allow you to optimize your distribution network around your detailed inventory requirements, which can be specified at daily demand levels if you desire. This is a very powerful capability that sets their platform apart from the other solutions on the market, as most of the other supply chain optimization platforms focus on inventory, or network design, but not both simultaneously.

To understand just how powerful their new solution is, we have to start by discussing how hard it is just to optimize inventory. There’s a lot more to inventory than just the carrying cost that is recorded on the books. There’s the cost of replenishment, the cost of a stock-out, and the cost of missed service levels, for starters. If your planning is poor and you’re always having to rush inventory, or if you’re not maximizing truckload volume, you’re spending a lot more on inventory replenishment than you should be. If a stock-out results in lost sales, that’s missed revenue opportunities which go straight to the bottom line. And if you keep missing your service level targets, your customers might just find a new source of supply at contract renewal time. (And on the flip side, if you are constantly carrying too much inventory to make sure you don’t miss service levels, your carrying costs will go through the roof.)

To optimize inventory, you have to take into account the many layers of your distribution network: factories, (first tier) national warehouses, (second tier) regional / provincial warehouses, and (third tier) local warehouses; storage space at each location; valid flows from one tier to another, as well as valid flows between nearby warehouses at the same tier; transportation options available; stores or end-use facilities that require the SKUs; the individual SKU demand patterns (and [expected] forecast accuracies); lead times (and variabilities); service levels; and costs associated with storage, transportation, and stock-outs at various inventory levels. (Transportation costs in particular will vary.)

This is because you don’t need the same service level at every node in the network to achieve that service level at an end customer location, especially if a customer location can be serviced by multiple distribution centres. For example, if an end customer location can be serviced by three different distribution centres, you can achieve a 98% service level (defined in terms of SKU availability) as long as each individual distribution centre has a 75% service level (as the chance of all three distribution centres being simultaneously out of stock and unable to service the customer location is 0.25 * 0.25 * 0.25 or 1.5625%). Furthermore, as the lead time from each DC to each customer location will vary depending upon distance, transportation options, and local routes, and so on, the inventory levels at each DC can vary and still allow you to meet your target service levels, which can in fact vary by location (as you’ll want a higher service level at a high-profit location than you will at a low-profit location as service levels drive inventory which drive costs). In fact, the deeper you dive into inventory, the more complex the cost equation becomes and you see that you really do need to take into account all of the elements supported in the Algorhythm Xtra Sensory Inventory Optimizer, inventrhythm, if you truly want to optimize your inventory costs.

But this is just the beginning. Since your distribution network design will ultimately dictate your inventory costs, to truly optimize your inventory costs, you have to simultaneously optimize your network (to the extent that you are able). Algorhythm’s platform can do this, and we’ll discuss what’s involved in Part II.

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Is 2010 The Coming of Age for Sourcing and Supply Chain Optimization?

In the beginning, there was the reverse auction. Industry visionaries applied reverse auctions to their sourcing events for commodity and competitive categories (in the mid nineties) and saved a small fortune (which sometimes exceeded 30%, 50%, and even 70% of previous category costs). They were heroes and the world was good.

 

Then, a couple of years later when they circled back to the first categories and held another auction, something unexpected (to them) happened. The total savings shrunk considerably. The average savings, expressed in terms of percentages, dropped from the mid double digits to the (low) single digits. The savings often equalled what they would have expected from a traditional RFX / negotiation process. But the market was a seller’s market and the total event time, and thus the total event cost, was low, so with the right spin, they still looked quite successful. The world was still good.

 

Another couple of years passed, and they circled back to the first categories again. But this time, the market was a buyer’s market again and savings were bound to equal those seen in the initial category reverse auctions, right? Wrong! Instead, something really surprising (to them) happened — instead of saving money, total costs increased — sometimes in the double digits! The world was a dark and scary place. What happened? Could it have been avoided?

In short, as I explained in A Brief History of Optimization (published in By the Buy, the TradeExtensions Newsletter), reverse auctions are not the panacea that many auction platform providers still make them out to be and the identification of real savings through auctions can often be elusive at best. A new technology is needed, and as I have been saying (well, shouting from the rooftops) for years, that technology is optimization.

But, even though the technology is now a mature technology (as strategic sourcing decision optimization turns 10 this year, which makes it middle-aged in Internet years and a senior citizen in dog years), only the true market leaders (which generally account for 10% of the total market) have even tried it, and, in my estimates, less than half of those have truly adopted it on an organization level, even though the analysts have consistently found that strategic sourcing decision optimization consistently saves an average of 12% above and beyond what you’ll get from the best reverse auction.

Simply put, optimization is instant ROI. Guaranteed. In the absolute worst case, your allocation is already perfect and you won’t save any money. But I’ve NEVER seen this happen in practice. Even the most dismal events generally return 3% to 5% savings. Even if we’re only talking a 50M category, that’s still about 2M in savings. And now that you can run an event for (considerably less than) 100K, that’s still at least a 20X ROI!

And it doesn’t take a PhD to use it anymore. Now that most of the platforms offering true strategic sourcing decision optimization have easy to use GUIs, wizard-based constraint definition, and scenario and costing templates built right in — with full Excel integration for data collection, modification, and reporting, optimization is as easy to use as an auction platform. (And in Trade Extensions’ platform, it’s built into the auction.) And while it might still take a couple of days of training to master the advanced features, any of your senior analysts should have no problem picking it up quickly. And once they learn it, they can modify the templates for your organization and train your more junior staff, who will probably only need a couple of events to master most of what they’ll need to do on a daily basis for an average category.

And after reading this recent piece in Industry Week that says “Transformation is Out; Optimization is In” that pointed out that while organizations still want to ‘transform’ how they deliver back-office services, they typically want to move in pragmatic, incremental steps and focus on achieving best-in-class, standardized and optimized delivery models and said that while many organizations remain keen to avoid the costs of new capital and migrating to new suppliers, investment is being made in ensuring existing suppliers and internal processes are delivering optimum value, I’m starting to think that maybe optimization might finally begin to come of age. It appears that the term has finally entered the daily vocabulary of supply management professionals, who should now be more open to at least reviewing optimization solutions. And once they see the savings to be had, and the power that they can have at their fingertips, I can’t help but thinking that the followers are finally going to start to adopt this technology and become leaders in their own right. (The laggards will ignore it for years to come, but that’s okay. Most are still hunkered under their desk waiting for the recession to be over and will eventually go out of business anyway, so let’s not worry about them.)

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Another Way Supply Chain Optimization Increases Profits

A recent article in Supply Chain Brain on “Planning and Managing Demand: A Modern Supply Chain Imperative” provided a great example of how you could use scenario-based what-if optimization to slash costs and increase profit at the same time.

Another approach [to maximize the profitability of a given inventory investment] is for operations to look at the sales pipeline and see that a customer is currently in the pipeline and is expected to order 50 widgets. What if sales approached that customer with an offer of $1/widget price reduction on 20 widgets if they made a decision within two weeks? Assuming the customer accepted the offer, how much does it impact revenue and profitability?

[Let’s say that] in the original factory order, total revenue would have been $1,500 (100 at $15/widget). Cost of the factory order is $630 (90 at $7/widget) plus $90 (10 at $9/widget) for a total of $720. Margin is thus 52 percent [because a widget costs $5, a $60 container holds 30 widgets and a partial container cost $4 a widget].

[But] what is the situation if the discount offer is accepted? Total revenue for the order would be $1,500 (100 at $15/widget) plus $280 (20 at $14/widget), or $1,780. Total cost of the order would be $840 (120 at $7/widget). Margin increases to 53 percent. By cutting prices the company ends up making more money. Furthermore, it does not impact total demand (since the customer would have made the purchase anyway), but rather it affected profitability and cost, since the customer saved $20, and the company saved an equal amount.

And this is something you could easily figure out with a good scenario-based what-if decision optimization solution that allowed you to adjust prices to see what offers you could make that would benefit you and your customer(s).

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Trade Extensions Demonstrates Optimization is Not Just for the Private Sector

As I just finished my recent series on The Role of Optimization in Strategic Sourcing, I wanted to run a few recent case studies to demonstrate the power and benefits of optimization to make it clear just what you’re missing by not using this wonderful piece of sourcing technology. Since I talk with some of the people at Trade Extensions regularly, I decided to ask them since it seemed like the quickest (and easiest) way to get what I wanted.

Now, I must say that I was a little surprised by what I received, and you might be as well. Now, many of you probably know that Trade Extensions powers BidSmart by Schneider Logistics, that it is used by A.T. Kearney in many of their high-profile consulting engagements and that, like their peers, they have several of the largest Fortune 500 companies in the world as clients. What you may not know is that they also have a significant number of public sector clients in Scandinavia, including the cities of Stockholm and Gothenburg, Greater Stockholm Public Transport and The Swedish National Traffic Agency. The case studies I received detail just a few of their successes within this sector.

Even though optimization isn’t restricted in terms of applicability, when you consider that:

  • most public sector operations, at least in North America, are woefully behind the private sector
  • most public sector operations, at least in North America, require the “lowest bidder” to win the award, no matter how unattractive their bid might be or how poor their past performance was
  • most public sector operations, at least in North America, have so much red tape and politics at play that getting the cross-functional team on-board necessary for success is a pipe dream

the last thing I was expecting was a set of public sector case studies.

So what did optimization do for the very forward-thinking Swedish public sector?

  1. It reduced the cost of cleaning services by over 6%.
    This amounted to a savings of over 200,000 Euros of up-front saving plus considerable on-going administrative savings as the ability to accept a package bid reduced the number of contracts that had to be administered from 42 to 1!
  2. It reduced the cost of bus services by over 1,000,000 Euros.
    While the average cost reduction was only 2.4%, in the public sector where union wages rise every year (with the cost of petrol [gas])), that’s pretty good — especially when the routes for a bus service are fixed!
  3. It reduced the cost of road resurfacing (while reducing the risk of possible collusion between suppliers) by over 1,000,000 Euros!
    Again, while the average cost reduction was only 2.7%, since union wages and the cost of materials rise every year, this is also quite good! Also, the design of the event (a large number of contracts were split into 2 separate contracts, one for the production and delivery of asphalt to a specific site, and one for the laying of the asphalt) had the desired effect in terms of allowing smaller suppliers to participate in the event.
  4. It reduced the cost of domestic travel (w.r.t. flights) by over 55%!
    Before the Trade Extensions event, which allowed bidders to submit bids on single contracts or a combination of contracts, the average contract cost for the Swedish National Public Transport Agency for the long distance public transport system was about 13,500,000 Euros a year. After the combinatorial event which considered 27 bids from 8 bidders, the cost was reduced to about 6,000,000 Euros a year! Incredible!

If you want more information, feel free to contact Chetan Raniga, Business Development Manager (Americas) at your convenience. He’ll be happy to discuss these, and other, sourcing categories (and case studies) with you.

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Will the Tigers Truly Latch On To Analysis and Optimization?

This is the Year of the Tiger (in more ways than one) and, according to a recent article in the SCMR on “Supply Chain 2010” which quoted a recent AMR Research Survey on 184 companies that found that performance management was considered the most strategic supply chain technology investment, software applications in 2010 will focus on analysis and optimization.

I hope so, because it would be great if companies

  • actually knew how much they were spending,
  • who was getting the money,
  • what they were getting for it,
  • how much they should have paid vs,
  • how much they were invoiced, and
  • how much could have been saved with better information and more leverage.

And it would be wonderful if companies could clearly see that

  • lowest bid is not lowest TCO,
  • lowest landed cost is not lowest TCO,
  • lowest acquisition cost is not lowest TCO, and
  • even the lowest Total Cost of Ownership is not necessarily the best value because
  • Total Value Management means that you need the ability to simultaneously analyze cost, risk, and non-price factors to make the best buy decision.

So will it happen? Or will those few of you smart enough to understand the incredible value these technologies have to offer continue to outpace your competition by leaps and bounds for another year?

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