Category Archives: Decision Optimization

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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The Basics of Inventory Optimization

Inventory optimization can be defined as the act of balancing supply and demand uncertainty to meet desired services level at a minimum level of investment. In addition to all of the basic factors of inventory management covered in our last post (namely, production, stock, location, transportation, and information), inventory management also considers all of the associated costs — carrying costs, stock out costs, alternate distribution costs, and lead time costs, and tries to balance them.

As such, an inventory optimization solution will allow you to define:

  • your current production & distribution networks, and any flexibility you have
  • the modes of transportation available to you and associated fixed and variable costs
  • the lane options available to you and impacts on transportation costs
  • current and projected demands by SKU, family, and location by time period (month, week, or day)
  • storage and carrying costs
  • desired service levels by SKU, family, and location
  • projected cycle times
  • production capacity constraints and feasible schedules
  • network and storage constraints
  • current contractual commitments
  • upcoming promotions
  • compliance requirements

As well as your flexibility in terms of service levels and working capital and produces optimal inventory recommendations at different trade-off levels. You can then analyze costs and projected losses at 90%, 93%, and 95% service levels (defined in terms of product availability) and make the best decision that balances working capital tied up in inventory and revenue potential.

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

In Part I, we re-introduced you to Algorhythm, purveyors of a supply chain optimization rhythm solution platform out of Pune. In the day before yesterday’s post, we discussed their new Inventory Planning Module, inventrhythm, and indicated how it allows you to take your entire distribution network design into account, which is necessary if you truly want to minimize your inventory costs. Then we told you that if you were truly serious about getting the most bang for you inventory dollar, you had to go beyond inventory and also consider your underlying distribution network design, as it ultimately dictates how much your inventory is going to cost you. Just like a bad product design will lock in expensive commodity and engineering costs before it is sourced, a bad network design will mandate higher safety stocks and sub-optimal transportation methods, which will in turn lead to higher carrying and transportation costs. Thus, to truly optimize your inventory, you also have to simultaneously optimize your distribution network to the extent that you are able to do so.

With Algorhythm’s new Strategic Distribution Network Optimizer, which seamlessly integrates their netrhythm supply chain network design module with their new inventrhythm multi-echelon inventory optimization solution, you can simultaneously optimize your facility location, transportation methods, and inventory levels to achieve your end-customer service levels while minimizing your overall inventory-related supply chain costs.

Algorhythm’s netrhythm solution allows you to define the warehouses that are available to you at each level of your network (and to define the warehouses that must be used, or must not be used, in the solution) in addition to source factors and end customer locations; the transportation methods available; the transportation providers available (as well as any that must be used, or must be used, and minimum or maximum business levels); fixed, minimum and/or maximum lot sizes; available lanes, forecasted demand; target inventory levels; and network constraints (with respect to linkages, warehouses, product mix, mode, etc.) and produces a lowest cost distribution network design subject to your constraints that will achieve your target service levels at each location. In other words, it’s a very powerful network design model that lets you take all of the relevant components in your physical network.

But the integrated solution is even more powerful. In addition to the many layers of your distribution network, transportation modes, and logistics providers, you can specify detailed service targets by location, SKU, and period. You don’t have to use average demand levels — you can take into account your detailed forecasts by month, week, and even day. You can model all of your inventory related costs at different demand levels; segment inventory by SKU subgroup, group, and category; and analyze by cluster and channel. You can look at your various cycle times, load factors, and flow options and do so with respect to all of your network and inventory constraints (such as capacity and existing agreements) and cost components (fixed and variable). For example, you can take into account fixed truckload and variable less than truckload rates from a third party and compare that with fixed and variable costs of operating your own fleet (lease, maintenance, etc.). And when you’re done, you get the network design that minimizes your inventory levels and associated costs while ensuring that your service levels are met. The reports detail what inventory levels are needed where, when, and the replenishment cycles as well as what providers move the product, when, using what modes, and at what load factor. It’s a complete supply chain plan. Furthermore, it’s easy to work with because all the reports can be output to Excel — which allows you to drill and pivot to your heart’s content until you see the data in a form that’s most convenient for you to internalize. (And while spreadsheets are not supply chain solutions — especially where optimization and analysis is concerned, they are good for report manipulation, and everyone is already comfortable with them.)

And the results are beyond what you would get with either tool on its own because not only does your distribution network dictate your inventory costs, but changes in inventory requirements over time will dictate your network costs. (If a warehouse becomes unnecessary because customer locations move and new lanes open up, that’s a considerable fixed cost that is unnecessary.) It’s a viscous cycle, and unless you look at both in unison on a regular basis, you’re missing cost reduction opportunities. Consider the case study of a major (FM)CG company in India that typically maintained about 115 tonnes of inventory in its network in an attempt to meet service levels. Not only did every tonne of inventory, depending on the SKUs in question, represent anywhere between roughly ten thousand and a few million dollars of working capital tied up in inventory, but every tonne represented additional inventory costs that chipped away at margin and profit. When Algorhythm applied their basic SKU inventory model, they were able to present the CG company with a solution that trimmed 25 tonnes of inventory out of the system without affecting service levels. (In fact, the average service level was increased!) When they moved to a multi-echelon inventory model, which balanced inventory not just at each level, but across levels (and allowed inter-level shipments as well), they were able to trim an additional 26 tonnes of inventory. But when they applied the full Strategic Distribution Network Optimization model, they were able to shave an additional 5 million tonnes. In the end, they more than halved the required amount of inventory to meet the service levels, and halved the network related costs. That’s a very considerable chunk of change that went straight to the bottom line!

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