Forecast Pro is one of the tools that answer the question, what is the right tool for the job. Sales forecasting and business planning is hard enough with using the wrong tool or forecasting software. Our clients find that Forecast Pro is the right tool at the right time for any sales forecasting or business planning. With Forecast Pro, businesses can create accurate forecasts quickly and easily using proven statistical forecasting methods. Forecast Pro Research has shown that no single method works best for all data, which is why Forecast Pro provides a complete range of forecasting approaches to address all types of business needs. Forecast Pro’s models accommodate seasonal demand, product hierarchies, product promotions, slow moving items, causal variables, outliers and much more.
Forecast Pro has many models to accommodate current business challenges, listed below is a list of some of the Forecast Pro forecasting models.
Forecast Pro Model 1: Expert Selection – Forecast Pro Expert Selection takes the guesswork out of forecasting. The built-in expert system analyzes your data, selects the appropriate forecasting technique, builds the model and calculates the forecasts—it even explains its reasoning in ordinary English
Forecast Pro Model 2: Exponential Smoothing – Forecast Pro includes twelve Holt-Winters exponential smoothing models to accommodate data with different forms of trends and seasonal patterns.
Forecast Pro Model 3: Box-Jenkins – For stable data sets, Forecast Pro supports a multiplicative seasonal Box-Jenkins model. Your Forecast Pro business model can be built completely automatically or interactively with the assistance of a full range of screen-oriented diagnostics.
Forecast Pro Model 3: Dynamic Regression – Forecast Pro Dynamic regression is used when there are important leading indicators or other causal variables. You can include independent variables, lagged or transformed variables and build generalized Cochrane-Orcutt models. Using Forecast Pro’s self-interpreting diagnostics, you can build and compare alternative models with a few clicks of the mouse.
Forecast Pro Model 4: Event Models – This extension of exponential smoothing provides adjustments for special events like promotions, strikes, moveable holidays or other irregular occurrences.
Forecast Pro Model 5 : Multiple-Level Models – Multiple-level models allow you to aggregate data into groups and reconcile them using top-down or bottom-up approaches to produce consistent forecasts at all levels of aggregation. Seasonal and event indexes can be extracted from the higher-level aggregates and applied to lower-level data.
Forecast pro works for many businesses and the best way to experience for your business is with a 30 Day trial. Click here to find out more about how a free 30 day trial can work for your business.
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June 2010-UK Retail rise again. Here are 2 Tips to take advantage Sales are rising in different retail sectors except food, In the UK sales forecasting software Forecast PRO is helping sales forecastors drive British economic growth whilst reducing on hand stock and finance costs.
For June the figures were UK retail sales rose 1.2% in comparison to June ‘09, when sales had picked up 1.4%. This June was slightly less hot, but sunny for most of the month. On a total basis, sales were up 3.4% against a 3.2% increase in June 2009.
As budgeting and forecast experts we are often asked what sales forecasting software to select to improve forecasting accuracy.
These business intelligence improvements reduce the cost of the budgeting and forecasting cycle. Any improvement here flows into in the Supply Chain, reducing logistics, packaging, warehousing, marketing and finance costs just for starters.
Seasonality and Trending is usually a big issue for forecasting and planning as it plays a very big role Supply Chain planning. The large ERP systems just have enough insight or flexibility to deliver the forecasting accuracy required. We constantly come across companies who are working with 40 - 60% forecasting accuracy where they need 70% plus accuracy at least.
Remember Statistics from the school days well could save your bacon now. Using Statistics on your sales histories provides valuable insights into your forecasting requirements. BIG ERP tends to give a simple average for the last 3 months Stock, Sales or Purchasing figures and projects forward on that basis. No Science, no business knowledge, no intuition, no exponential smoothing, just cut and paste. A waste of money. Now Stats just got a bit more interesting.
TIP – Get 2 years of data (SKU, Category, Product, Regional level) and find the trend in that. You can use Statistical methodologies to select the most appropriate sales forecasting methodology to forecast sales, tax revenue, passenger numbers. Track the impact of weather and season etc.
TIP – Avoid the hassle and expense of the above learning invest in sale forecasting software. Send us your data for forecasting
For example we use Forecast PRO sales forecasting software exponential smoothing to detect seasonal changes in data by ignoring the irrelevant fluctuations irrelevant. Forecast PRO exponential smoothing (unlike moving averages smoothing) older data is given progressively-less relative weight (importance) whereas newer data is given progressively-greater weight.
Also called averaging, it is employed in making short-term forecasts. Our Excel challenge works with organisation everyday to demonstrate how we can speed up forecasting, sales and operations planning, demand management. Check out the excel challenge for yourself to see how you can start moving in the right direction.
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Sales Forecasting software uses statistical forecasting methods to generate forecasting. We at MXI Software explain relevance of exponential smoothing for sales forecasting, budgeting and business modelling.
Exponential smoothing methods weight the historical data using exponentially decreasing weighting. The immeidiate prior period has the most weight and each period prior to it has relatively less weight. The decline in weight is expressed mathematically as an exponential function. The smoothing parameters determine the weights. To see the relevance of this in action we developed the mxi free forecasting offer. MXI 60 minute free forecast session:
Comparison Among Exponential Smoothing Methods
Single Exponential Smoothing: Identifies the percentage of weight given to the prior period and all other historical periods. It does not adjust for trend or for seasonal variance.
Double Exponential Smoothing: Finds trend then adjusts the forecast data to reflect this trend instead of generating a single parameter for all forecast periods.
Holt-Winters: Identifies both trend and seasonal variance, and adjusts the forecast data to reflect these factors. This method is tuned to both high and low outliers. A better choice for handling seasonality is Double Exponential Smoothing with the Data Filters parameter set to Seasonal Adjustment.
Advanced Parameters for Exponential Smoothing
These smoothing constants are used in the equations for exponential smoothing methods. Keep the default settings unless you have a strong background in time-series forecasting.
Alpha: Determines how responsive a forecast is to sudden jumps and drops. It is the percentage weight given to the prior period, and the remainder is distributed to the other historical periods. Alpha is used in all exponential smoothing methods.
The lower the value of alpha, the less responsive the forecast is to sudden change. A value of 0.5 is very responsive. A value of 1.0 gives 100% of the weight to the prior period, and gives the same results as a prior period calculation. A value of 0.0 eliminates the prior period from the analysis.
Beta: Determines how sensitive a forecast is to the trend. The smaller the value of beta, the less weight is given to the trend. The value of beta is usually small, because trend is a long-term effect. Beta is not used in Single Exponential Smoothing.
Gamma: Determines how sensitive a forecast is to seasonal factors. The smaller the value of gamma, the less weight is given to seasonal factors. Gamma is used only by the Holt-Winters method.
Trend Dampening: Determines how sensitive the forecast is to large trends in recent time periods. Dampening identifies how quickly the trend reverts to the mean. A higher value implies slower dampening while a lower value implies faster dampening. The smaller the value, the less effect the trend has on the forecast.
For each constant, you can specify a maximum value, a minimum value, and an interval. The interval is an incremental value between the maximum and minimum, which the forecasting engine uses to find the optimal value of the constant.
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In today’s environment, purchasing decisions are under increased scrutiny especially from the Finance Team. Financial Controllers and Finance Directors need solid numbers based persuasion. The investment return must obvious and the cashflows, cost, ROI, clearly demonstrated. Stands to reason.
Financial modelling and business intelligence from MXI Software can convert data into real business advantage using financial modelling software such Quantrix, Crystal Reports. Vendors need a compelling business case for CFO’s and purchasing committees. Model-based selling is the next level approach offering that opportunity, especially for high-value products and services that deliver long-lasting returns and benefits.
Model-based selling is not a new concept; however, it has never realized its potential due to technology and perception constraints. Sales executives have historically employed
spreadsheets to present figures and charts to prospective clients. However, the reliability of the model can be called into question by the closed nature of spreadsheets. Complex, linked worksheets with cryptic formulas are frequently perceived with suspicion due to the lack of transparency. Spreadsheet models can be percieved as being contrived to support the sales executives’ claims rather than present an objective business case. Sales executives will occasionally struggle explaining the inner workings of spreadsheet models to the prospect’s satisfaction. Quantrix offers a transparent, flexible tool that is ideally suited to model-based selling.
Key benefits of Quantrix include the ability to: A) Clearly present the business case using NPV, ROI, simple payback, net cash flows, tax benefits, and other calculations and metrics. B) Demonstrate the full range of configurations, customizations and other variables with “what if” scenarios. C) Present customized configurations in real time. D) Rapidly prototype solutions and respond to proposals. E) Build confidence and trust with highly transparent, understandable models. F) Create professional-looking, interactive dashboards and visualizations. G) Integrate multiple data sources to populate model with actual, timely data. H) Refine and improve model with data-driven analytics and feedback.
Vendor’s products and services are frequently evaluated according to Internal Rate of Return (IRR) or Return On Investment (ROI). If the acquisition does not meet a certain threshold or compare favorably to other investments, then the purchase does not move forward. Quantrix enables users to build models that incorporate key financial metrics including ROI, IRR, NPV, payback and others. Financial Controllers like to see where the numbers came from, it is much easier to demonstrate how the logic was constructed. The business logic and cell values used for these calculations are transparent and inscrutable, thus giving the sales executive the confidence to explain and stand behind the model.
Since Quantrix models are created with plain-language formulas, it is very easy for prospects to understand and validate the logic. Quantrix models often reduce the number of formulas by over 85%. Interested Take the Excel Challenge and let us show how Quantrix will work effectively for you. Excel Challenge
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Financial Modelling has always been central to any analysis for evaluating a business health or justify further investment in its growth.
There are several important steps to follow in developing a financial model which will serve your objectives as a corporate manager or entrepreneur, whether you’re trying to manage what you have or raise capital for what you could do. This is particularly true for newer enterprises, as the discipline associated with identifying and thinking through the key business drivers is invaluable to the early planning process.
Figure out what you’re trying to accomplish.
As an entrepreneur or executive, you have a number of competing objectives. Depending on how established you are, you may have a business to run on a day-to-day basis, and it’s hard to find the time to plan, build and manage against a set of financial models.
You may be tempted to build a simple profit and loss type spreadsheet laying out revenue assumptions and costs. But effective financial models can and should be used for so much more. Using them, you can look six to sixty months down the road to plan for organic growth, evaluate opportunities to enter new markets or take on new sources of capital, or anticipate liquidity problems. It is highly recommended to take the time to build a model which will generate a consolidated set of financial statements, cash flows, balance sheets that will provide a more comprehensive picture of your business. And the sooner you identify the range of scenarios, the easier it is to plan and build your model to accommodate them.
Fail to Plan: Plan to Fail.
Real business intelligence emerges in the planning phase of any project evaluating new opportunities, testing best worst scenario’s. Examining and determining the key business drivers and assumptions. Financial modelling software is needed to test the more complex opportunities. A good guideline is a spreadsheet with more than a 100 lines of depth and 4 connected worksheets is too much for Excel.
A great way to start forecasting correctly by taking advantage of Free Trials. And for those that need more help, MXI are willing to give you a month’s trial and build your first business model. See about MXI’s Free forecast Offer
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Forecast PRO sales forecasting software will dramatically improve your sales forecasting accuracy, track historical forecasts and produce variance reports for better business intelligence that will drive improvement in your enterprise forecasting process.
Four reasons to track forecast accuracy
1. Improving your forecasting process requires the ability to track accuracy.
Sales Forecasting should be viewed as a continuous improvement process. Your forecasting team should be constantly striving to improve the forecasting process and forecast accuracy.
For example, many organizations generate baseline forecasts using statistical approaches and then make adjustments personally based on local knowledge such as an upcoming promotion. Organisations that track the accuracy of both the statistical and adjusted forecasts learn where the adjustments improve the forecasts and where they make them worse. This knowledge allows them to focus their time and attention on the items where the adjustments are adding value.
2. Tracking accuracy provides insight into expected performance.
A forecast is more than a number. To use a forecast effectively you need an understanding of the expected accuracy.
Within-sample statistics and confidence limits provide some insight into expected accuracy; however, they almost always underestimate the actual (out-of-sample) forecasting error. This is due to the fact that the parameters of a statistical model are selected to minimize the fitted error over the historic data. The parameters are thus adapted to the historic data, and reflect any of its peculiarities. Put another way, the model is optimized for the past—not for the future.
As part of our drive to help companies improve forecasting, we are offering a free forecast: Just sent your data in to us
3. Tracking accuracy allows you to benchmark your forecasts.
If you are lucky enough to be in an industry with published statistics on forecast accuracy, comparing your accuracy to these benchmarks provides insight into your forecasting effectiveness. If industry benchmarks are not available (usually the case), periodically benchmarking your current forecast accuracy against your earlier forecast accuracy allows you to measure your improvement.
4. Monitoring forecast accuracy allows you to spot problems early.
An abrupt unexpected change in forecast accuracy is often the result of some underlying event. For example, if unbeknownst to you, a key customer decides to carry a competing product, your first indication might be an unusually large forecast error. Routinely monitoring forecast errors allows you to spot, investigate and respond to these changes early on—before they turn into bigger problems.
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It isn't easy but it can be done. Demand Management, Supply Forecasters, Sales and Operations planning collectively can achieve this. Sales Team define the sales plans, timings and volumes of the demand from customers. We explain how Sales, Marketing and Management need to combine to drive accuracy improvement.
Once a month they need to communicate anticipated purchased customers will make including volume, timing and the degree of certainty over the agreed planning horizon. Free forecast :: Send us your data and we will send you a forecast
Explain the assumptions on which plans are based. Detail the market intelligence and customer feedback outlining problems and opportunities similarly. Sales Team need to immediately advise of issues which will impact changes in demand as soon as they become known. Not a classic trait of any sales team.
Sales and Operation Planning and Demand Management is also heavily influenced by the sales forecasts provided by Marketing. Marketing need to detail plans for impacting demand. Monthly updates on the efforts of the marketing team to drive and control demand management. Promotions, Price cuts or increase, Retailer or customer incentives need to advised and communicated to Sales.
Marekting needs to update tracking, measuring and reporting competitor activity, external factors which impact sales levels on a monthly basis. In the face of the field report from Sales, Marketing needs to tweak the sales and marketing strategy.
Product and Brand Managers - Need to outline supports available, product launches, new product development, and list product to be discontinued and not be shy about sharing these details. Furnish Sales and Marketing with budgets, define the product strategies, making clear to the Demand Managers, Supply Chain Managers, Forecasters company plans and objectives.
Management need to convey the Assumptions behind product life cycles. Brand Owners, Product Managers needs to detail delays in product launches changes in product plans which impact demand.
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1. Forecast at the right level.
Sales Forecasting accuracy can be greatly improved by deciding on the right level to sales forecast at. Is a forecast at SKU level appropriate? In many environments it is not and greater detail must be made available by segmenting history and forecast by channel, sales region, or even customer. In other cases companies are currently forecasting at customer level when a more accurate forecast could be achieved with less effort by working at a higher level. Send us your sales history for real demo
2. Review forecasts at aggregate levels
When reviewing item level forecasts it is all to easy to "pad" each item's forecast just in case. It is not until the item level forecast is aggregated and reviewed at a brand or product category level that the cumulative effect of this "padding" is exposed in the form of a clearly unachievable growth in forecast over history. Aggregate level forecast review is an essential part of the forecast review process because it allows for a "sanity check" of the forecast compared to history and preferable company budgets. Any anomalies must be identified and corrected before putting the forecast into the inventory planning system.
3. Review forecast by exception
20% of your products are the important ones so why not use ABC analysis to focus on the products which are most important first while the mind is still fresh? Why not use deviation filters to identify the few products in the database which have unusually high or low trending forecasts when compared to history? Survey
4. Measure and report forecast accuracy
MXI Forecast Pro UK (Free Trial available) allows you to review and report on forecast accuracy as required. 100 Standard reports offer analysis to help you identify weak areas and opportunities for forecast accuracy improvement.
Success breeds success
A forecasting process will rarely be successful if the progress is not measured and the results reported to all participants. Take advantage of the functions available and start your forecast accuracy graph this month. Let everyone know how "good" or "bad" they are doing and what progress they have made over time. Success breeds success. Contact us now
SAVE YOUR FORECAST EVERY MONTH
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MXI explain how MXI Quantrix Modeller will help with budgeting and modelling your organisations integrated financials or a company you wish to acquire. You will need projected revenues, expenses, costs,debts and taxes driving expected profit. Next you need to make assumption examining the key business drivers on the Balance Sheet, Growth rates, Prices, Creditor - debtor days. And most importantly you need a robust model so that you can produce best and worst case scenarios.
ASSETS
Creditors
Grow with credit sales (net revenues)
Using an IF statement, model should enable users to override with creditor days sales outstanding (DSO) projection, where days sales outstanding (DSO) = (AR / Credit Sales) x days in period.
Stock
Grow with cost of goods sold (COGS) -
Override with inventory turnover (Inventory turnover = COGS / Average stock turn)
Expenses
Grow operating expenses (may include COGS if the prepaids are cycled through COGS)
Other Current Assets
Grow with revenues (presumably these are tied to operations and grow as the business grows)
If reason to believe that they are not tied to operations, straight-line projections.
Capital Assets, Plant Etc
PP&E - beginning of period (Opening)
Capital expenditures (grow historicals with sales or use mgmt or analyst guidance)
-Depreciation (function of depreciable plant & equipment opening balance by life expectancy)
- Assets sales (use historical sales as guide)
PP&E - end of period (Closing).
Intangibles
Intangibles - Opening
Purchases (when disclosed, grow historicals with sales or use mgmt or analyst guidance)
Amortisation (when disclosed, amortizable intangibles BOP divided by useful life)
Intangibles - Closing
Simple alternative is to just straight-line or grow entire balance with sales
Goodwill & Other Assets
Straight-line
LIABILITIES
Accounts payable
Grow with COGS
Override with payables payment period assumption
Accrued Expenses
Grow with operating expenses
Taxes Payable
Grow with the growth rate in tax expense on income statement
Other current liabilities
Grow with revenues
If reason to believe that they are not tied to operations, straight-line projections.
Using MXI Quantrix with a little effort in modelling the plan this can achieved in 1-2 days and create a model the entire business can use. Built with budgeting and forecasting software that will generate a real finanical model which the company can link to the ledger for rolling forecasts into the future.
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Is this familiar? You work in a large organisation with lots and lots of data. But every time IT or Finance are requested to provide some information it takes up to two weeks to get it back by which time you are onto the next challenge. There is no serious analysis and forecasting, business planning is suffering, profit opportunities are left begging to be taken yet no action, why? All because nobody could answer the question "what if we did this", "changed this", "allocated more funds to this" or "increased headcount in this department" etc. There is no shortage of data in the business, no shortage of IT power or resources. You need to clearly see the business cost/profit drivers, prepare scenarios and model them so you can test them and develop working budgets and forecasts.
We work with client's everyday providing them with this first class business intelligence which helps them and their business to identify business profit or cost drivers. Building financial models or business model that allow these organisations to plan better for a fraction of the cost and time it takes other companies or Government Departments using spreadsheets. We help customers to identify the right financial and budgeting software, business modelling software, business intelligence software to do this. Auditors, Regulators, Tax Authorities are not too keen on spreadsheets the known error levels and business risk.
Why not take the MXI Excel challenge? We will work with you to develop a trial model to demonstrate the value to your organisation in this approach, better business analysis, modelling and forecasting for your business with much greater accuracy, best and worst case scenario testing. After all does not business need every help it can in today's environment. And the best way to stay one step ahead is to stay informed, making the right decisions. Read more here: Excel Challenge
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