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09.11.2014
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Improved tactical sales forecasting for global supply chains: how to use leading indicators?
LUMS News Improved tactical sales forecasting for global supply chains: how to use leading indicators?
Long term tactical sales forecasts are vital for effective steering of global supply chains. Conventional extrapolative forecasting methods, such as the commonly used exponential smoothing, cannot capture market dynamics and changes in the economic environment (Weller & Crone 2012).
Consider a global supply chain with a wide geographical spread and all the possible macro-economic and other indicators. Research at the Lancaster Centre for Forecasting (LCF) has shown that we can formulate advanced causal model capable of identifying useful inputs from a pool of more than 60,000 macro-economic indicators. In our case study company, we observed substantial accuracy improvements over current tactical forecasts and statistical benchmarks for both the USA and Europe markets.
Smart sales leaders know that even late stage opportunities have a ton of risk – competitors win deals, budgets don’t get allocated, champions transition into new roles, priorities change, etc. So, while there will always be a need to monitor individual deal risks, many sales leaders are using a forecast coverage tool as a more effective way to mitigate forecast risk. I recently sat in a webinar on “data-driven forecasting” and listened to about a dozen “forecast killers” which sales leaders are supposed to monitor on individual deals:  timing, slippage, no next steps, negative velocity, key players not identified, random large deals etc. Monitoring deal risk is valuable.  At Winalytics, our customers find it very helpful to monitor deal-level risk on large and key late stage deals that can help make or break goal achievement for a quarter.
By contrast, our customers find managing forecast coverage much more valuable than deal-level forecasting for making sure they will get to their goal.  By forecast coverage, we mean monitoring ideal coverage ratios as they compare to actual coverage ratios at the pipeline and stage level for the current quarter and two to three quarters out.
Managing forecast coverage is a win-win proposition.  Finance leaders get the information they need about likely revenue achievement and sales aren’t spending vital time off task. In our experience at Winalytics, there are three essential steps to building a forecast coverage model.
Synthesizing the above elements to create a coverage model empowers you to set ideal pipeline goals for reps throughout the quarter as well as for up to three quarters out.  This makes it possible to track how well your team is positioned to hit goal without having to get into detailed inspection on all the deals in your pipeline. It won't take more than a couple of minutes, but your opinion is very important to future users of the program and to the author of the program. Welcome to Forecast Pro - Software for sales forecasting, inventory planning, demand planning and collaborative planning. Do you want to learn about forecasting techniques and best practices that can improve your bottom line, but are pressed to find the time and budget to fit educational events into your schedule?
This ongoing series of 60-minute sessions provides you with in-depth knowledge on specific forecasting topics delivered right to your desk.
All of our previously presented Webinars have been recorded so that you can view these sessions at your own convenience at no charge. In this session, James Berry, Senior Consultant, shares his perspective on the practical challenges organizations face when creating and implementing forecasting processes. It can become a major distraction to your organization when actual demand varies significantly from what is planned, typically leading to time-consuming exercises to “close the gap.” In this Webinar, Timm Reiher and Todd Ferguson, Principals at Oliver Wight, explain how to implement a consistent demand control process, including incorporating statistical forecasts with inputs from multiple sources to create a demand plan.


Most forecasters realize that it is extremely unlikely that their forecasts will be 100% accurate but still depend upon them to guide the business.
Business forecasters rely heavily on time series methods which typically capture structure in the data—such as sales levels, trends, seasonal patterns, business cycles and responses to promotions—which is critical for creating the forecast.
In this one-hour Webinar Paul Goodwin, Professor Emeritus, University of Bath, will present a series of practical recommendations to ensure that you are making forecast adjustments for the right reasons and to show you how to avoid pitfalls. Do you want to build causal factors such as prices, promotions and economic indicators into your forecasts, but fear that building regression models is too complex and difficult?
In this educational, one-hour Webinar, learn from acclaimed expert Colleen "Coco" Crum, Principal at Oliver Wight, who will show you how to obtain different views of demand and integrate them to create a proposed demand plan. Although most business forecasters would welcome the chance to review each item every forecasting cycle, with limited resources this is simply not possible.
You can improve forecast accuracy by applying quantitative approaches that model events directly.
Does your organization rely heavily on automated extrapolation-based forecasting algorithms despite being dissatisfied with some of the results? Creating accurate weekly or daily forecasts is much harder than generating accurate monthly forecasts, but if this is what your business requires, there is no need to fret! Business forecasters are expected to create accurate forecasts regardless of the quality of the inputs and, unfortunately, most have no choice but to work with less-than-perfect data. In this educational one-hour Webinar, Eric Stellwagen shares his insight on tracking forecast accuracy illustrated with real-world examples which show how to avoid common pitfalls. Forecasting new products is a challenge-with little or no historical data, the trusted methods that work well for existing products often produce inaccurate forecasts for new products.
This session demystifies regression modeling, demonstrating how these models can provide insight into your data and revealing why they often yield more accurate results than alternative forecasting methods.
There are 3 worksheets in this Excel workbook : Sales Invoice, Service Invoice, Packing Slip. When considering potential lead orders and all the dynamics, the problem explodes to select from at least 800,000 variables. Furthermore, in discussions with supply chain managers it was confirmed that the identified leading indicators were meaningful and helped them to improve their insights.
Embedding such tactical forecasting models in organisations provides opportunities and challenges, particularly in terms of embedding inputs from expert knowledge and providing market intelligence and feedback to managers.
Sagaert is a visiting PhD researcher at the Lancaster Centre for Forecasting from Ghent University.
Model Success Periods:  First, and most importantly, you need to look at the historical information you have available in your CRM to identify the time periods where your reps were most successful.
Understand Rep Conversion Rates: Second, knowing your historical conversion rates by rep and by stage will help you refine the types of opportunities and total deal value you need to hit your goals. Monitor Progression Risk:  Finally, it also helps to understand how your deals move through your pipeline. If you leave your email right now we will send you tomorrow an invitation to evaluate the program. To make it easier for you to stay informed, we invite you to learn from the experts via our free educational Webinars. When you attend a live Webinar, you can participate in polls on key forecasting issues and submit questions directly to the presenter.
As illustrated through client case studies, he shows how to create a roadmap to guide your organization in designing, implementing and improving upon its forecasting process. In this educational one-hour live Webinar, Eric Stellwagen, Vice President of Business Forecast Systems reviews the ins and outs of this popular forecasting method. In this session, Eric Stellwagen explains the ins and outs of forecasting a product hierarchy, addressing issues such as whether your organization strive for “one-number” forecasts across departments and how to define the hierarchy to facilitate accurate forecasting. Understanding the uncertainty around a forecast is essential for using it in a way that improves the decision making process. Eric Stellwagen, Vice President of Business Forecast Systems, teaches you how to recognize the different components in your demand history and explains which elements you can—and cannot—forecast effectively.
Kenneth Kahn, Professor of Marketing and Director of the da Vinci Center at Virginia Commonwealth University reviews a variety of pragmatic approaches that you can use to forecast new products more accurately.
This session will demystify dynamic regression modeling, demonstrating how these models can provide insight into your data and revealing why they often yield more accurate results than alternative forecasting methods. Eric Stellwagen, Vice President of Business Forecast Systems, explains pragmatic approaches for identifying problem area quickly and systematically—both in the history and forecasts.


In this educational one-hour Webinar, you will learn; how event models work, how and when event models should be used, how to build customized event variables to meet your business needs, and best practices for applying event models. Nada Sanders of Lehigh University demonstrates how you can improve accuracy by applying different forecasting methods to different segments of your data.
In this educational session, Ellen Bonnell, CEO of Trends Savants, shares seven guiding principles for getting good forecasts from bad data.
While many companies recognize the potential benefits of S&OP, they often struggle to implement the process with any level of effectiveness. In this Webinar, we survey various pragmatic approaches for forecasting new products more accurately. However the problem with causal models lies with the identification of the appropriate drivers of sales.
It is common in practice to rely on experts to identify the appropriate indicators, but when the problem is put in perspective it is easy to see that the task is almost impossible to be solved solely by using judgment.
The size of the task obviously makes it impossible to resolve by market expertise alone or conventional regression based forecasting models.
We are currently engaged in further research that investigates different ways that experts could contribute to the specification of the statistical forecasting method.
His research focuses on including external leading economic information into business forecasting.
You can analyze those time periods to understand pipeline activity and ideal coverage ratios that contributed to success. Using real-world examples, Eric compares and contrasts different strategies for forecasting a product hierarchy, emphasizing best practices. In this educational one-hour live Webinar, James Berry, Director of Training at Business Forecast Systems, explains pragmatic approaches for dealing with forecast uncertainty. In this educational one-hour live Webinar, Eric Stellwagen addresses several issues that arise when working with weekly and daily data.
Which macro-economic variables provides the best early signal of a shift in the market and therefore allow us to revise our forecasts correctly? To make matters worse, conventional regression models are not able to provide meaningful answers, particularly given the limited sales history in comparison to the very large set of potentially useful indicators.
Using the Least Absolute Shrinkage Selection Operator (LASSO), with a setup that was developed by the group, we are able to identify meaningful leading indicators that improved forecasting accuracy. If you can break these success periods down by stage and monitor pipeline ebb and flow throughout a quarter your model will be stronger. Detailed information on opportunity progression allows your pipeline and rep coverage models to fully internalize how your pipeline actually works. Reports can be directly browsed from your website or download as CSV files (compatible with MS Excel). Mark Moon, Director of the Forecasting & Demand Management Forum at the University of Tennessee, explores ways to make S&OP implementations more effective–the key to success.
In fact this question has two aspects: one is to identify the correct indicator and the other is to identify how early this indicator carries information about the incoming changes, also known as the lead order of the indicator.
This is because the chance for picking spurious connections or building overfitted models that do not forecast well is too high. Research at LCF has already shown the strength of LASSO in other areas such as promotional modelling and this study demonstrates the usefulness of the method for business forecasting (Ma et al. Reports can be directly browsed from your website or downloaded as CSV files (compatible with MS Excel). It'll let you analyze income and expenses in the graphic chart and help you pinpoint costs and areas of excessive expenditure to control your finances more efficiently. Using "sales budget forecasting" crack, key, serial numbers, registration codes is illegal. We do not provide any download link points to Rapidshare, Depositfiles, Mediafire, Filefactory, etc.



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