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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Contact MXI Software
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.
Contact MXI Software
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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