Testing times for everybody mean we are trying more and more ways to resolve problems. In our business we are constantly being asked by pressurised business owners and managers, how can I handle such erratic demand?, yet satisfy demand. I must reduce Stock, I can’t afford to hold it yet I must work with powerful suppliers otherwise they won’t give me stock when needed. Big problem and the answer is. DO the maths!
What do we mean? well maths includes statistics and elementary statists could be the answer to your problem. Sales forecasting software from MXI UK such as Forecast PRO examines trends in your sales, stock, purchasing histories. Statistics forecasting methodologies such exponential smoothing helps business everyday carry less stock meet weaker demand as it arises. The technical explanation is that statistical smoothing techniques common to sales forecasting software such as Forecast PRO weights recent sales, stock time series history (i.e. the financial crises & recession). In other words, recent observations are given slightly more weight in the forecast than the older observations. This is a very popular scheme to produce a smoothed Time Series.
Thats why we developed the very popular MXI free forecast review session. Send us your data and we will forecast it free online free. (UK/IRL) only:
We help companies and organisation to use Forecast PRO sales forecasting software exponential smoothing to detect seasonal changes in data by ignoring the irrelevant fluctuations irrelevant. Further we can select other methods such Holt Winters, simple trend-line or same as last year plus or minus a delta (if required), incremental growth.
Of course we encourage users to intervene directly themselves using the human experience and interaction to finesse the forecast. Sales forecasting software can’t be aware of increased advertising, reduced marketing, promotions, end of line, new product launches, etc. Sales forecasting software builds greater supply chain accuracy into the forecast. Results always need intepretation, human intervention and finessing.
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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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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Contact MXI Software
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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New research shows 38% of organizations surveyed are practicingadvanced analytics today to drive sales forecasting using new sales forecasting software and business intelligence. Within 3 years 85% say they intend to. The current recession is teaching everyone to understand business, customer better.
Why such a dramatic change? The use of advanced analytics such as exponential smoothing, trending analysis, business intelligence is driving up organisations need to intepret constantly changing business environments (as seen in the ongoing recession and the resultant market turmoil), as well as to discover opportunities for cost reductions and new sales targets (which are key to surviving and thriving in a down economy). Its the logical extension of the lean manufacturing ethos. Read Fill the Gap between Excel & Business intelligence.
Organisations driving these using advanced analytics: query-based analytics (which relies on complex SQL statements to define recent business events) and predictive analytics using sales forecasting software (which uses data mining and statistical methods to anticipate future events). Much of this activity is currently being done in spreadsheets.
There are many applications of advanced analytics, but most of
them involve discovering relationships, reading trends, anticipating the future, and adapting to change. Working with the right data in the right condition is key to achieving these goals.
Discover relationships. Whether advanced analytics is data mining, statistics, artificial intelligence, or complex queries, you will discover and quantify important relationships that you may have been unaware of. These relationships can reveal fraud, define customer segments, group products of affinity, and link field conditions that lead to product failures. The newly
discovered relationships target marketing campaigns more accurately, develop effective merchandizing strategies, and improve product quality.
Sales forecasting software for example will anticipate the future. For example, predictive business models quantify a customer’s proclivity to churn, thereby giving you an opportunity to retain the customer. Predictive financial models can assist with various types of forecasting. Likewise, predictive analytics can quantify future risk for pragmatic applications in actuarial tables or loan approvals.
Understand and adapt to change. On the one hand, advanced analytics can help you understand change in the form of rising costs or new customer behaviours. On the other hand, the discoveries made through analytics can lead to positive changes that help your business adapt to an evolving world.
From a business standpoint, benefits are obvious however it needs specialised analytic tools and analytic databases from a technology standpoint. Organisations new to advanced analytics will need to reach beyong current IT landscape, reporting and data capabilities.
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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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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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MXI Software provider of Budgeting and forecasting software in the UK Ireland providing companies with the data analysis tools and busines intelligence which enables organisation to strategically plan find and work the true profit drivers in their business using proper financial modelling software. MXI suggest Quantrix business intelligence tools. WATCH HERE
We want to be the partner of choice in UK Ireland for organisations wishing to implement leading edge demand management, sales forecasting, business modelling and financial modelling. Take the Excel challenge.
We believe that spreadsheets are inaccurate, error prone and too difficult to correct when faulty. Its now well documented that most large consulting practices, Bearing Point, PWC, Deloitte etc state clearly that any spreadsheet with more than 100 rows of data is likely to contain errors. Just Google it for yourself.
An example of the crucial role played by spreadsheets is the valuation of most Pension and investment portfolios around the world. The US albeit in a slightly different form, Iceland, Ireland and Italy all have state agencies who are responsible for relieving Banks of most of their toxic property portfolios. indeed many of these agencies will become the largest property companies in the world.
These agencies are using spreadsheets to value these portfolio's or funds. This can't be right in this day and age. In Iceland and Ireland this will amount to the largest investment ever made by the state and the use of a proper financial modelling intelligence to get at the facts.
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Forecast Pro Trac is the latest version of the Forecast Pro family and it's ideal for demand forecasting, demand planning, demand management, sales forecasting, item level planning, sku forecasting. Easy to use, light years more advanced than spreadsheets and enjoys award winning status for its statistical forecasting accuracy and is much less expensive than many of the alternatives.
MXI Forecast PRO UK combines forecasting, management reporting, graphing, reporting and adjustment capabilities as found in Forecast PRO unlimited. However Forecast PRO TRAC also provides easy to understand data graphics business intelligence that concentrate attention on the MAPE to drive forecasting accuracy improvement.
Forecast PRO uses will be able to select non performing forecasts and specifically concentrate attention on forecast accuracy. For example a specific exception report in the business intelligence selector allows users to select forecasts which are greater than 50% out against actuals and concentrate on them.
Forecast Pro TRAC allows you to view your data and forecasts in as many units of measure as you need. A drop down menu lets you select for example.
Rearrange your hierarchies.
With Forecast Pro TRAC's powerful "shuffling" capability, you can rearrange hierarchies with just a couple of clicks. Generate reports showing product-level forecasts broken out by customer and the
Forecast Accuracy, Identify SKU, pruduct groups which need attention
With Forecast Pro TRAC's flexible exception reporting, you can automatically flag exceptions, saving you from having to manually review a long list of items
Statistical forecasting accuracy improvement
Forecast Pro TRAC maintains an archive of your previous forecasts (both statistically-generated and adjusted forecasts) so that you can compare and contrast previous forecasts to what actually happened.
Consolidate your team's forecasts.
Forecast Pro TRAC breaks large forecasting projects into smaller pieces that can be worked on separately and then reassembled into consolidated forecasts. For example, if three demand planners are responsible for forecasting three different geographical territories, they can work on their forecasts separately; after they're done, their consolidated forecast can be further manipulated.
If you are still using Excel, download our free whitepaper on the true cost of Excel: True Cost of Excel
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We are regularly asked how much historical data I need to improve my forecasting accuracy using sales forecasting software?
Here is a general guide, to reliably fit and test statistical models, you need a time series of 48-60 months. To pick up seasonal patterns correctly you will need at least 3 seasonal cycles to improve forecasting accuracy for a forecasting sales or purchasing, stock seasonality model.
With 48 months, for example, the first 36 can be used to fit the model and the last 12 to test the model's forecasting accuracy. For short-term forecasting, going back more than 60 months is unlikely to be helpful, since most statistical methods apply more weight to the more recent than to the distant past.
And when there is no data available and a forecast is none the less required, it is still possible to develop a forecast. One easy way is to enforce the history from another item and literally cut and paste it into the forecast making manual adjustments. Top-Down Planning is another option when using Forecast Pro. This uses the pattern from the product group to drive the forecast. This has the benefit of being automated and derives its sales and seasonality from the other like products at the group level. The results can then be reviewed and adjusted at the granular level using modifiers by the planners.
A little user experience in Forecast PRO, natural curiosity and training will greatly improve forecasting accuracy, speed and response times.
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QUANTRIX financial modeller 3.7 from UK Vendors MXI has a great new data add on business intelligence component which significantly augments the renowned financial and business modelling software Quantrix. Users are always looking for better ways to access and use the huge amounts of data that reside on their computers and within their business intelligence software. Naturally using this data correctly can really help drive a business.
We (MXI) thought we would provide usage examples to give some overview of potential use of Quantrix. Remember the key here is to avoid the huge cost normally associated with Business Intelligence implementations. In the California Energy market Quantrix DataNAV provides real-time transactional data online allowing market makers to query and instantly access market information. This is provided as a SAAS (software as a service) offering.
PMC President Henry Shin, reports his client navigate extensive information using Quantrix DataNAV without having to download huge amounts of data and without having to wait 2 weeks for the IT department to build custom reports. Users simply use DataNAV to provide the ease of use, integrity and obviously management level reporting online.
In the UK we are working with Manufacturing customers to build improved costing and pricing methodologies using Quantrix DataNAV. Pulling information from a range of IT platforms containing more than 30 years of data. Clients will price, source, design and build new products in less than half the time, meeting all of the regulatory requirements.
Quantrix Modellers multidimensional data and business modelling and analytics software enables financial professionals to move on from spreadsheet technology and consequently develop finance and business models of the highest integrity, visibility and flexibility. This is really important due to the heavy reliance on spreadsheets within large organisations in order to leverage their CRM, ERP or BI products.
Register now to watch our free Quantrix Demo.
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