Demand Forecasting in a “Build to Order” Company

The Smart Forecaster

Pursuing best practices in demand planning,

forecasting and inventory optimization

We often come into contact with potential customers who claim that they cannot use a forecasting system since they are a “build-to-order” manufacturing operation. I find this a puzzling perspective, because whatever these organizations build requires lower level raw materials or intermediate goods. If those lower level inputs are not available when an order for the finished good is received, the order cannot be built. Consequently, the order could be canceled and the associated revenue lost.

I agree that in such an environment, forecasting the finished good is not always possible or particularly helpful. Sometimes it’s helpful, but not sufficient. In any case, it is critical to make sure that the underlying raw materials and intermediate goods that go into the finished good are available. Demand for these can certainly be forecasted.

The organization’s goal would be to maintain service level inventories for these intermediate goods that are high but not unaffordable. Planners will need to set optimal stocking levels for these materials, balancing service level requirements against available budget. Since a given intermediate good could serve as an input to more than one finished good, the volatility of the demand for the intermediate good would be less than the volatility of the demand for a specific finished good. Hence, the safety stocks necessary to keep high service level inventories of the intermediate goods would be relatively lean.

Three companies, all users of SmartForecasts, serve as interesting examples. The first is a chemical company, Bedoukian Research, which manufactures custom chemicals for various clients. Each of these “finished goods” is a unique combination of intermediate chemical compounds. Bedoukian begins its demand planning with a finished goods forecast, which drives the production schedule and allocation of essential production resources. This requires exercising considerable judgment, as finished goods demand changes dynamically.

Once these finished good forecasts are created, raw material requirements can be estimated via a bill of material disaggregation. Bedoukian combines these results with safety stock estimates, based on actual utilization rates and service level objectives to be achieved, to generate the complete, service level-driven forecast for raw materials. This has allowed Bedoukian meet its production requirements with significantly less inventory.

The second company manufactures the internal components for mobile phones, where finished goods are specialized combinations of these components. For example, an order may call for a certain number of phones with unique labels on the case. This is the finished good for this order. Everything that goes into that order, except for the label, is built out of standard components. Again, SmartForecasts will be used to keep lean, high service level inventories of the components. This company thought that the only way to manage component inventories was via bill of material aggregations. They are now looking at the actual utilization rate for the components and setting much leaner inventories while maintaining high component availability.

A third company, NKK Switches, which explored this topic in their recent webinar (see CFO Bud Schultz’ guest blog post), considered their products to be “unforecastable”. You can read more about it below, but overall NKK Switches was able to forecast components and meaningful aggregations of product families. By tracking forecast vs. actuals over several months, NKK was able to demonstrate the accuracy of its forecasts to its Asian factory suppliers, and convince them to shift from a “build-to-order” model to “build-to-forecast.” This change has resulted in dramatic reductions in lead times, in many cases cutting them in half, increasing customer satisfaction and the overall sales close rate.

The bottom line here is that there is a perfectly viable—I would say essential—method of demand forecasting for build-to-order businesses, setting high service levels for pivotal input resources. If you would like to know more, please drop me a note, at nelsonh at smartcorp dot com.

Nelson Hartunian, PhD, co-founded Smart Software, formerly served as President, and currently oversees it as Chairman of the Board. He has, at various times, headed software development, sales and customer service.

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    Handling Extreme Supply Chain Variability at Rev-A-Shelf

    The Smart Forecaster

    Pursuing best practices in demand planning,

    forecasting and inventory optimization

    Does your extended supply chain suffer from extreme seasonal variability? Does this situation challenge your ability to meet service level commitments to your customers? I have grappled with this at Rev-A-Shelf, addressing unusual conditions created by Chinese New Year and other global events, and would like to share the experience and a few things I learned along the way.

    First, let me explain our situation. We import 60% of the parts we use to build our kitchen and bath accessories from China and Europe. Most of the year we were able to plan our inventory needs using a spreadsheet-based min/max approach. But not during Chinese New Year, which drives the planet’s greatest annual population migration. Chinese New Year shuts down production for up to two months, creating significant supply risk as we strive to meet our three day order fulfillment commitment.

    We solved our problem, introducing statistical demand forecasting with the flexibility to extend lead times when necessary, the ability to reliably establish safety stocks that achieve our required service levels and a continuous reporting system that lets everyone know exactly where we stand. However, success required much more than a new piece of software. We needed to change the way we view future demand, supply risk and safety stock. Here are a few key things we did that made all the difference.

    Stakeholder education and buy-in

    Regardless of the project, it’s always best to enlist the buy-in of all stakeholders. We knew we had to do something to solve our problem, but there was bound to be resistance. Senior managers, for example, had developed a healthy distrust of software and wondered whether demand forecasting software could help. Our buyers had developed their own perspectives and procurement methods, and felt personally at risk as we considered new approaches.

    People came around as they developed a common understanding of the problem and how we would address it. Education was a big part of the solution. We explained how forecasting works and key factors we should all understand: how to analyze trends, how to use “what if” scenarios, impact of shifting lead times, how to relate service levels to supply risk and safety stock and key performance indicators like inventory turns. Going through this process together, we all became stakeholders in the solution.

    Use the Right software

    When you have lots of part numbers and any sort of supply or demand variability, you just cannot forecast effectively with a spreadsheet. With our min/max forecasting system, we were planning to an average, and it wasn’t working. Average usage has inherent flaws for planning purposes—it’s always looking backward!

    You need software that looks ahead, recognizes seasonal patterns and enables you to determine how much stock you’ll need to meet required service levels over varying lead times.

    Fine-tune processes

    When the old ways don’t work, you need to be open to adjusting your assumptions. Think less about where you’ve been, and more about where you want to be. Take a look at your lead times and plan to your desired service level. Last year’s history may not be the best predictor of this year’s demand. The same forecast horizon may not be appropriate for all products or certain time of the year.

    Make the Forecast Actionable

    It’s not enough to produce an accurate forecast and estimated inventory stocking levels. You’ve got to develop a way to make the information actionable for those tasked with using it. We developed a set of reports that enabled buyers to leverage better forecast and safety stock information. Now, at the end of every month, we produce a forecast report that provides a clear picture of current inventory, safety stock, past usage, forecasted usage, incoming deliveries (PO’s) and recommended order quantities.

    Validate Results

    You can, and we did, test our new methods against our own demand history. Still, an authoritative outsider can make acceptance easier. We commissioned a study by a professor at Louisville University’s College of Business who set one of her graduate students to the task. Through them we were able to reinforce what we saw happening from our results, and feel comfortable that we were on a good path.

    All of these factors helped Rev-A-Shelf transform its demand planning process, to great effect. Today we are exceeding our service level targets, and our fill rate, based on a three day ship cycle, is showing steady improvement, and trending up. Overall, units-in-stock have stayed flat while supporting a 13% increase in sales.

    John Engelhardt is currently Director of Purchasing and Asian Operations for Rev-a-Shelf, LLC in Louisville, KY. He has held a variety of management positions both in private business and public organizations. At Rev-A-Shelf he held the position of International Sales Manager and Director of Sales Support before assuming his current position. He can be reached at johne at rev-a-shelf dot com.

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      A CFO’s Perspective on Demand Planning – “More Strategic Than You Think”

      The Smart Forecaster

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      Bud Schultz, CPA, Vice President of Finance for NKK Switches, presented his company’s experience with demand planning during a recent webinar. The following is a brief summary of Bud’s key points; view the complete webinar by clicking here.

      Q: Tell us about NKK’s business and demand planning challenges.

      NKK Switches, based in Scottsdale, Arizona, is a leading manufacturer and supplier of electromechanical switches. The business involves many different switch types—toggles, push-button, rotary, even some programmable switch types. We are known for our high quality, and for our ability to meet an exceptionally broad range of customer requirements on a turnkey (custom configuration) basis. NKK Switches produces customized solutions from component parts sourced exclusively from manufacturing facilities in Japan and China.

      There are literally millions of possible switch configurations, and we never know what configured solutions our customers will order. This makes our demand highly intermittent and exceptionally difficult to forecast. In fact, until fairly recently we considered our demand unforecastable. We operated on a build-to-order basis, which meant that customer orders could not be fulfilled until their component parts were produced and then fashioned into finished goods by NKK. This resulted in long lead-times, painful for our customers and a competitive challenge for our sales organization.

      Q: What did you expect to get from improved product demand forecasting?

      When we began to investigate the value of demand forecasting software (SmartForecasts from Smart Software), we tried to view the decision from a Return on Investment (ROI) point of view. We did some capital budgeting, making assumptions about potential reductions in inventory levels, reduced inventory carrying costs and other potential savings. Although the capital budgets returned positive returns on investment, we nevertheless were unable to move forward based on that information. We lacked confidence in our assumptions, and we were worried that we wouldn’t be able to justify the safety stock and inventory levels that the software would suggest.

      What we didn’t expect was a challenge from our parent company. In light of the capabilities of a newly implemented ERP system, they would consider a new approach. If we could produce demonstrably reliable demand forecasts, they would consider procuring raw materials and producing switch components on a build-to-forecast rather than build-to-order basis. This opened the door to a much more profound impact. We tracked actuals against forecasts over a twelve-month period and found that our forecasts, particularly in aggregate, were exceptionally accurate: actual demand was within 3% of forecast. Once we were able to prove the validity of our forecasts, we were able to move forward with the parent company’s plan to manufacture product based on those forecasts.

      Q: How did accurate forecasts of product lines with intermittent demand data transform NKK’s operations?

      From the many different combinations we manufacture to order, individual switch parts can show very intermittent demand (long periods with zero orders and then seemingly random spikes), but we can identify more consistent patterns across switch series. All of the part numbers in a given series have common components and raw materials, such as plastic housing, brackets and other hardware, gold, silver and LEDs.

      Providing our manufacturing facilities with reliable forecasts ended up allowing us to make dramatic changes. Our manufacturing plants could start procuring raw materials that in the aggregate would eventually be used in production of different part numbers within that series, even if the specific part numbers to be produced were unknown at the time the forecasts were made. And in many instances, despite the irregular demand history data, it was even possible for the suppliers to manufacture specific part numbers based on the forecast.

      Once the program is fully implemented, we anticipate our leads times will be reduced to half the time or even less. Shorter lead times will result in lower reorder points, resulting in higher service levels while reducing our inventory requirements.

      Bud Schultz leads all finance and accounting functions at NKK. His background as a Certified Public Accountant, attorney, engineer and pilot for the US Air Force provide unique perspective on finances for engineering and manufacturing operations.

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        Smart Software Awarded National Science Foundation Innovation Research Grant

        New research to improve service and spare parts planning for the multi-billion dollar aerospace, automotive, high tech, and utilities markets

        Belmont, Mass., November 28, 2012 – Smart Software, Inc., provider of industry-leading demand forecasting, planning, and inventory optimization solutions, today announced that it has been awarded a Phase I Small Business Innovation Research (SBIR) grant from the National Science Foundation (NSF).  Smart Software will investigate new statistical methods to forecast intermittent demand, with the ultimate objective of helping enterprises worldwide reduce inventories by tens of billions of dollars.

        The new research will build upon Smart Software’s patented solution for forecasting slow-moving or intermittent demand, developed with the support of a previous NSF grant.  The current method, commercialized as part of the company’s flagship product, SmartForecasts®, evaluates historical demand for each item and establishes the optimum level of inventory that will be required to achieve service level objectives.  The new research seeks to extend demand forecasting beyond individual products and parts, identifying and interpreting interactions across clusters of items whose demands fluctuate together.

        The new forecasting capabilities will benefit customers in several significant ways:

        • A more dynamic statistical model of parts will enable forecasts to better reflect a variety of external factors that include part usage by itself or in combination with other products, as well as the impact of macroeconomic and environmental factors.
        • Research results will provide planners with a dynamic model of item usage, enabling planners to develop functional maps of the interrelationships of large numbers of parts. Knowing which parts have demands that co-vary can be useful in at least two ways. First, item managers can be assigned to work with coherent clusters rather than arbitrary collections of miscellaneous parts, and second, parts can be co-located in warehouses for more efficient storage and retrieval.
        • Another benefit from this new approach will be improved forecasts of “aggregates” where intermittent demand is present, such as all items in a product line, or all items at a particular warehouse. Better forecasts of aggregate demand across groups of parts will also be useful for raw materials purchasing, as well as for financial planning when parts are a source of revenue.

        According to Nelson Hartunian, president of Smart Software, “Any organization that builds or supports capital equipment experiences intermittent demand for some portion of its inventory. This grant is a terrific opportunity to impact one of the biggest forecasting challenges facing these organizations – accurately forecasting parts and optimizing inventories. Ultimately, the goal is to have the right part at the right place at the right time. The research we are undertaking will make this goal more achievable.”

        The Small Business Innovation Research grant program from the National Science Foundation is extremely competitive. More than a thousand companies compete in a two-stage screening: one for intellectual merit, and the other for commercial potential. This Phase 1 grant is the third Smart Software has received.

        About Smart Software, Inc.
        Founded in 1981, Smart Software, Inc. is a leader in providing businesses with enterprise-wide demand forecasting, planning and inventory optimization solutions.  Smart Software’s flagship product, SmartForecasts, has thousands of users worldwide, including customers at mid-market enterprises and Fortune 500 companies, such as Abbott Laboratories, Otis Elevator, Mitsubishi, Siemens, Disney, Nestle, GE and The Coca-Cola Company.  SmartForecasts gives demand planners the tools to handle sales seasonality, promotions, new and aging products, multi-dimensional hierarchies, and intermittently demanded service parts and capital goods items.  It also provides inventory managers with accurate estimates of the optimal inventory and safety stock required to meet future orders and achieve desired service levels.  Smart Software is headquartered in Belmont, Massachusetts and can be found on the World Wide Web at www.smartsoftware.wpengine.com.

        SmartForecasts is a registered trademark of Smart Software, Inc.  All other trademarks are the property of their respective owners.


        For more information, please contact Smart Software, Inc., Four Hill Road, Belmont, MA 02478.
        Phone: 1-800-SMART-99 (800-762-7899); FAX: 1-617-489-2748; E-mail: info@smartsoftware.wpengine.com