Category Archives: Decision Optimization

Trade Extensions Trades Up its UI … Again

Last fall, I provided you with an update on Trade Extensions and how they traded up their UI across their sourcing suite, making it easier to use while making it easier on the eyes. Well, barraged by constant feedback from users who wanted it to be easier still for the creation of “simple” optimization models, as they transitioned from a “full-service” to a “supported” to a “self-service” model, Trade Extensions decided to trade up its optimization UI again, especially around rule generation and scenario creation.

The Trade Extensions UI and platform was impressive because it’s constraints, or “rules”, are template-based, which permit them to be saved, copied, and applied to any relevant scenario and because it’s filters, which can be used restrict application of the rules, can be defined on bidders, lots, bids, plants, lot fields, and any other defined dimension in the system. Unlike many platforms where the buyer is limited to fixed constraint templates, the Trade Extensions UI allowed the buyer to build her own. However, defining a complex constraint and adding it to the scenario could be a complex multi-step process. For example, if you wanted to restrict allocation to European suppliers to 40% of the total award in Europe and Asia, the buyer would have to:

  1. go to the filters screen
  2. add a new filter that defined the European suppliers
  3. add a new filter that defined the European and Asian locations
  4. go the rules screen
  5. create a new allocation rule that restricted total supply by volume to Europe and Asia by European suppliers to 40% by selecting the rule type, defining the limit, and selecting the filters
  6. go to the scenario screen
  7. add the newly created allocation rule

While certainly doable, the process was cumbersome for simple constraints like “limit the award to The Wonderful World of Widgets to 40%” or “spilt the award between 3 suppliers such that no supplier gets less than 20%”.

In the new UI, which is based on a lot of ingenuity and even more AJAX, you can define the constraint and add it to the scenario from the scenario screen, which lists all the currently associated rules, which can each be enabled or disabled with a single checkbox. Clicking the “New Rule” button brings up a new Rule Creation screen for the scenario which allows you to define a constraint by:

  1. selecting a constraint template from the drop down, which organizes constraints by category
  2. specifying the bounds
  3. adding or defining any required filters on the fly
  4. selecting any required modifiers by way of a drop down

So, in our example above, to define the constraint you’d:

  1. click the “New Rule” button
  2. select the “Allocation (%) to Specified Suppliers is at most X
  3. select the “European Suppliers Filter”
  4. fill-in-the-bound with 40(%)
  5. add the “Restrict To Lot” modifier
  6. select the “European and Asian” lots Filter
  7. save the constraint

Then you’re returned to the scenario screen, with the new rule at the bottom of the list, where you can edit the parameter and filter selections on-screen, as well as turning the rule on-and-off. It makes the creation of even moderately complex rules quick and painless. And if your constraint is complex, or not accounted for in one of the dozens and dozens of pre-defined templates, you still have the classic method where the complexity of the constraint is limited only to the confines of your consciousness.

They’ve also traded up their reporting as well. In last fall‘s post, I told you how they had just released the ability to view scenario results in their new OLAP engine, which is the basis of their spend analysis offering. In the current release, the entire reporting framework has been shifted over to the OLAP engine which not only allows the buyers to slice and dice the award scenarios any way they like, but, with the new report builder, build pretty much any cross-tab, pivot-table, or roll-up report they like on both award dimensions and derived dimensions (which can also be exported to Excel if the buyer so desires).

The UI for defining a new report, which is also based on AJAX, is as simple, and powerful, as the new rule creation UI. To create a new report, the user:

  1. gives the report a name
  2. specifies the bidders, lots, and bids to use, possibly by way of filters (from existing rules) (which can be inverted)
  3. selects the associated dimensions (which can include any associated dimension from the RFX, Auction, etc. such as brand name, division, and historical spend for the lot; name, location, and number of allocated bids for bidders; base currency, date, and bid number for bid)
  4. defines the facts (derived dimensions), such as total spend by supplier; year-over-year savings by category; etc.
  5. selects the scenarios and/or phases to include (which can range from 1 to n), depending on the type of (comparison) report

Plus, the user can also create reports by joining one or more report definitions. If the user wanted to see payment and savings by allocated bidder and the user had a Payment and Savings report and a Allocation per Bidder report, the user can simply run both reports at the same time. The system will calculate the appropriate union of bidders, lots, bids, dimensions, and facts and create the appropriate report.

Finally, they are converting all of the standard reports to templates that can not only be used to run the standard canned reports, but copied and modified to serve your buyers’ needs. It’s an impressive improvement in usability such a short time-frame.

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Optimization: The Only Solution to Complex Spend Management

Today’s guest post is from Paul Martyn, Vice President of Marketing of Bravo Solution.

Paul can be reached at p <dot> martyn <at> bravosolution.com or 312 279 6793.

Most organizations have a diverse spend portfolio that includes many simple, several moderately simple, and a few complex spends.

To address each spend appropriately, we need to understand the dynamics that make each event complex. For starters, let’s look to another ‘multi-faceted’ puzzle; the Rubik’s Cube.

Invented in 1974 by Ernö Rubik, Rubik’s Cube has puzzled generations around the world with its utter devilishness. The multi-faceted nature of the Rubik’s Cube makes for a good analogy to spend management and sourcing.

Read the very interesting Wikipedia article linked above and you’ll find out that a 3×3 Rubik’s Cube has over 43 quintillion starting positions. But, if you know the right combinatorial magic, ANY cube can be solved in 29 or fewer moves. Like spend management, the Rubik’s cube has an extraordinary number of possible starting positions but a logical process (algorithm) can elegantly solve the problem with minimal effort.

Complex Spend Management is another multi-faceted puzzle with even more complexity and ‘faces’ (internal and external to the buying organization) than Rubik’s famous cube. There are many factors which influence decision-making and, like a Rubik’s Cube, each factor of Complex Spend Management is related to the other factors. For example, let’s look at the supplier ‘facing’ decision factors inherent in sourcing decisions; price, incumbency, risk and timing:
If incumbency, supplier risk factors and timing are not important, the spend management puzzle is relatively straightforward to solve. We could use a reverse auction or simple RFI/RFP template and get the cheapest possible price, all other things being equal, pretty easily. This is akin to solving one face of a Rubik’s cube, something most of us have the skills to do.

But, if we are to focus on the multi-faceted nature of our negotiations and explore new, and potentially more efficient, ways of dealing with suppliers while balancing the satisfaction of our internal stakeholders (in operations, finance, marketing, etc) we must recognize how our efforts to solve one ‘face’ or dimension of the puzzle impact the other faces and work to find a solution that satisfies each dimension. We’ve all observed that the price visibility we see in a reverse auction inspires pricing creativity by suppliers. In the same way, if we can offer more visibility into other, non-price stakeholder requirements, we will stimulate suppliers to respond creatively in those areas as well.

Successful sourcing managers find creative ways to drive financial results for their company. Effectively reducing costs means challenging internal stakeholders’ assumptions, preferences, and processes with scenario analysis that quantifies trade-off costs. Buyers need to expand simple price analysis to quantify the total costs (of ownership) absorbed by the operational stakeholders. Complex Spend Management requires that buyers include the inventory and logistics impact in their financial analysis. Buyers are often challenged to explore a wider variety of options to redesign their supply plan while evaluating strategic considerations like ‘make versus buy’.

To address this explosion of complexity, many buying organizations have developed and maintain ‘big ass spreadsheets’ (BASS). BASS were often designed for a single project and then reused on subsequent complex spend events. This approach does not take into account the dynamic nature of Complex Spend Management. The BASS approach is akin to knowing the moves to solve one specific starting position of a Rubik’s cube and applying it to other starting positions – it simply does not solve the ‘new’ puzzle. In short, buyers need a more dynamic and flexible solution.

Fortunately, today’s optimization algorithms provide buyers with a technology that identifies the optimal sourcing solution for each combination of supplier pricing, buyer preferences, business rules and risk factors. This allows buyers to define a problem (starting position) and work with the suppliers in a collaborative manner to propose solutions. The buyers then use optimization to determine which combination of supplier proposals is best.

In essence, optimization technology provides the buyer with the ability to increase collaboration with suppliers and solve any starting position of a Rubik’s Cube by simply pressing the “Solve Now” button.

Thanks, Paul!

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MCA Solutions – Bringing the Aftermarket Forward, Part II

In Part I, we re-introduced you to MCA Solutions, a Philadelphia, PA company that specializes in after market service (and service parts) optimization, and noted that they were still going strong despite some recent shake-ups in the market (and the noteable acquisition of Servigistics and Click Commerce by Marlin Equity Partners, who also acquired Emptoris not too long ago). We noted that, in addition to completing a strong SAP integration, they’ve also added a considerable amount of new functionality in the last two years around reporting, plan analysis, and reporting management.

Since we covered their new reporting and plan analysis solution in the last part, today we’re going to cover their performance management solution. Since you can’t manage what you can’t measure, and the best way to measure is often with a balanced scorecard, it’s based on scorecards, but since managers don’t like columns of numbers, it’s implemented using a dashboard, but since MCA agrees with me that traditional dashboards are inherently dangerous and dysfunctional, they realized that the only way the application would be truly useful was if it clearly identified not what was right, but what was wrong (since a goal of after-market service is exception-based management so that you only expend resources where needed). More importantly, the scorecard dashboard would only be useful if it allowed you to quickly discern what was wrong and do something about it. So what MCA built is a dashboard scorecard that not only highlights any metric that is out of bounds in red, but an interactive graphical scorecard that allows you to drill down into the metric retrieve all of the data associated with that metric in a single click.

Just like you can drill into a spend cube, you can drill into any metric on the scorecard. The first level drill will bring up all of the metrics the high level dashboard is composed of, and highlight which metrics are a problem. You can then drill into those metrics and bring up all of the associated raw data. So, if you brought up the scorecard and saw on-time delivery was only 80%, when anything under 90% is unacceptable, you could drill in and see the problem ports are LA and New Orleans and that San Diego, Washington, Vancouver, Boston, and Halifax were all meeting or exceeding their on-time delivery targets. You could drill in again and see that at these ports, most of the late deliveries were from West Coast Warblers and East Cost Easies and instantly know that either these suppliers have performance problems or that you’re not allowing them enough time in your inventory network design to transport the parts require to replenish your North American stock from your foreign suppliers. But since you can also drill into the application and the underlying model associated with any part, location, or supplier you can quickly determine if it’s a performance problem or a network design flaw. For instance, lets say you only allow 14 days for replenishment of goods in your LA warehouses from Shenzhen. Considering that sailing time is typically 12-15 days, and that it probably takes at least a day to get your goods unloaded at the port, and another for them to clear customs, get loaded onto the truck, and transported to your warehouse, there’s no way you’re going to get that part in less than 14 days by sea and it’s probably going to take at least 17 days on average, especially if these carriers are running slower ships. Then you know you need to adjust your model, and measure the supplier against a more reasonable delivery time. But if you are allowing 21 days, and your third party carrier is consistently late, then you have a supplier performance problem.

Moreover, the scorecard dashboard is completely customizeable. Each component is actually a dashboard report, and with their new flexible reporting capability, you can build any report you want. So you can design the dashboard to focus only on reporting problems. That way you can ignore the 90% of your network that is running smoothly and dive right into the 10% that isn’t running right, analyze the situation, revise the model, analyze the revision, implement an improvement, and see if the situation improves over time. If not, you can dive right in and try again. And if everything looks too good, you can define more metrics, more sanity checks, and find new problems to work on. Which is precisely what an actionable scorecard should allow you to do!

And your suppliers in China and Japan can use it too. The product is double-byte Unicode compliant and, in addition to a number of European languages, has also been translated into Mandarin and Japanese. With these recent improvements, you should be able to plug it right into your follow-the-sun operation and, once it’s configured and your data is complete, close the loop on your end-to-end after market service (parts) operation.

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MCA Solutions – Bringing the Aftermarket Forward, Part I

MCA Solutions, a Philadelphia, PA company that specializes in after market service (and service parts) optimization, is still going strong despite the recent struggles of a few of its direct competitors (namely Click Commerce and Servigistics who were recently acquired by Marlin Equity Partners). If anything, the recession (although it did considerably lengthen the sales cycle) only bolstered the need for after market service (as no one could afford new equipment) and optimization thereof (as everyone is strapped for cash and every penny counts).

As I indicated in my first post on MCA Solutions and their strategic service parts management platform, many large manufacturing, semiconductor, high-tech, aerospace, defense, and oil & gas companies often have tens of millions, if not hundreds of millions, of dollars tied up in inventory in their attempts to meet specified service levels, and every dollar in inventory costs them money in overhead. Since many of these companies typically have 10% to 20% more inventory than they need, they’re tying up tens of millions of dollars in working capital needlessly as well as throwing away millions of dollars in inventory holding costs — a situation which is easily remedied by a service level optimization platform that can optimize your multi-echelon parts inventory storage network such that your contracted service levels are met but your costs are minimized. Furthermore, as per the value of after market service in a down economy, done right, this optimization will also improve cash flow by roughly 10%, reduce inventory by 15% to 50%, and even improve service levels by 5% to 20%.

Since the last time I covered MCA in depth, which was almost two years ago, they’ve made a number of significant enhancements to their platform, the most notable being flex reporting, performance management, and plan analysis. Of these, flex reporting and plan analysis excite me the most, because the former lets you construct any report you can imagine (if you’re willing to write some SQL*) and the latter lets you build, optimize, and compare as many what-if scenarios as you want, which is the (one of the) most powerful feature(s) of any good optimization platform.

Their plan analysis tool not only allows you to define your service parts strategy (fill rates, inventory/investment caps, number of echelons to consider simultaneously in stock planning, etc.) and run an analysis on that strategy (to determine total cost and inventory distribution), and not only allows you to compare one strategy against another (how much do I save by sacrificing 1% of fill rate? how does inventory distribution change? etc.), but also allows you to define a rules-based sanity check that can be run against every model and the resulting inventory solution. For example, if the inventory levels change by more than 20%, the overall investment changes by more than 10%, shortages or excesses at any location exceed pre-defined maximums, etc., the product will immediately warn you that the new model might not be an acceptable replacement over the current one. Also, each of these rules can be defined by location, SKU (or family), or segment (or lane), which gives you a lot of flexibility in your analysis and sanity checks. (Other checks can include replacement rate, forecasting model [parameters], export mode, horizon, manual overrides, time factors, intermittence, thresholds, and other relevant measures tracked and/or computed by the platform.) Furthermore, they’ve also added the ability to generate plans by Average Customer Wait Times, which is becoming important in aerospace and defense, oil and gas, and other sectors where you have equipment that can’t be unavailable for more than a very short amount of time and service (availability) levels aren’t good enough.

While we’re talking analysis, they’ve also added a new multi-period budget report which is a system generated report that is very useful as it not only calculates total forecast, condemnation forecast, repair forecast, overall metrics, TSL, average inventory position, scheduled demand, new buy, and cost across your entire operation to anywhere between 12 and 36 months in the future, but does so using a successive series of automated optimizations where the output of one period is used as the input to the next. It will take anywhere from a few minutes to a few hours to run, but it clearly allows you to see the long term effects of any change to your aftermarket service (parts) strategy.

In the next post, we’ll talk about their new performance management solution.

* Yes, I’ll admit that I’m not your average user but I have to applaud them for acknowledging their expertise is not in the creation of report builders, that no set of canned reports, no matter how extensive, will please everyone, and that the right thing to do is expose the schema and let power users do what they want — which isn’t dangerous when you also give them the ability to make as many copies (partial or full) of the database as they want and to mess around with the copies, and not the production data.

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SYSPRO: Forecasting and Inventory Optimization for Small & Mid-Sized Businesses, Part II

In Part I, we covered the inventory forecasting solution for SMBs contained in the new SYSPRO 6.1 solution suite, which is built on the Microsoft .NET platform and which is (tightly) integrated with Microsoft Office and other Microsoft products. We concluded that the forecasting solution, which allows you to create forecasts and the product family / grouping level as well as the SKU level, is quite robust and extensive for an SMB offering, as well as being quite easy to use, and left off noting that it provides the foundation for a true inventory optimization solution that is newly available from SYSPRO in SYSPRO 6.1.

Today we’re going to cover their new inventory optimization solution. Since I am assuming you read my recent posts on the Basics of Inventory Management and the Basics of Inventory Optimization, I’m not going to repeat them and dive right into an overview of the SYSPRO product.

The SYSPRO inventory optimization solution starts with a forecast and current stock levels, your storage (warehouse) network, and your inventory policies and produces an optimal inventory plan that will minimize your inventory requirements, and, thus, your corresponding inventory costs. The key to optimizing your costs in the SYSPRO tool is the definition of an appropriate inventory policy for each product family and/or SKU and the accurate definition of relevant warehouse information regarding stock levels and associated costs.

For each product family and/or SKU, you define the lead time, any necessary gross requirements or batching rules, the economic batch (order) quantity, and any maximum inventory levels (by count, value, or volume). In addition to these basic inventory policies, you can also define more advanced “risk” policies, using the distribution algorithm of your choice (normal, poisson, etc.), if you are forecasting a family or SKU where demand can be highly variable. And for each warehouse, you define, at a minimum, the cost and UOM cost, the stock on hand (available, and free, if different), in transit, allocated, and on (back) order. When this is combined with a forecast and a time period is defined, the system is able to compute an optimal suggested inventory plan by SKU and individual warehouse location. It can then become your draft inventory plan as-is, or you can manually alter it first. Once the draft plan is accepted, it becomes the new, current, inventory plan.

And since the module is tightly integrated with the forecasting module, it’s easy to go back to a forecast, revise it, and return to the inventory plan. (For example, if you simply click on the selected forecast in the inventory optimizer, up pops the forecast module.) And once you have an optimized inventory plan, you can immediately jump into the MRP (Material Resource Planning) module which will take the inventory plan (and the forecast it is based on), the sales order, WIP (Work In Progress) data, and other associated information and produce a raw material requirements plan if you also have the association material planning information (either in SYSPRO or another ERP or database you connect to).

Finally, between the integrated Crystal Report Writer, it’s support for VB scripting, and its direct Excel integration, you can product just about any report you want (although it might take some work on the part of the developer if it isn’t a report that’s already in, or similar to what’s already in, the system). As for getting data in, the system can accept CSV, XML, and SQL database scripts (as the schema is exposed). In summary, SYSPRO provides a solid forecasting and inventory optimization offering for SMBs.

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