In the following, we demonstrate how to get the most out of Datup forecasts to increase accuracy levels and decrease lead times in planning. To do this, the basic, intermediate and advanced journey and usage scenarios will be described.

These uses will allow the analyst to take into consideration various fields of the results cubes, which will better guide decision making with respect to commodity price behaviour.

Basic Use: Suggested Forecasts

This use guides better decision making for:

  • Know the values of raw materials required by customers for each item for one or more future months.

  • Propose in S&OP sessions values according to optimistic, conservative or pessimistic sales conditions.

  • Assess the degree of certainty or uncertainty of commodity forecasts.

  • Prioritise the portfolio forecast by the items with the highest sales, turnover and variation.

This scenario focuses on suggested forecasts, automatically generated and selected by the platform. First, you must locate the date and the item of interest, by means of the columns Date e Itemrespectively. The column SuggestedForecast shows the suggested forecast for the date and item under analysis, based on the historical behaviour of raw material prices (Target); considering seasonality, levels, trends and even relationships with other items in the portfolio.

In addition, the column NextSuggestedForecastwhich presents an alternative scenario to the suggested forecast when there are indications or a substantial rise in raw material prices is anticipated for the specific period. The NextSuggestedForecast is often very useful for S&OP sessions, where the trading counterpart often arranges higher values for forecasts of certain items, subject to marketing campaigns or higher consumption strategies. Instead of opening the space for speculation for such a possible higher level of demand, the platform offers an alternative value that contemplates such an over-demand scenario. On the other hand, the solution also presents an alternative value for the under-demand scenario, where the trading partner foresees a discouragement in the consumption of the item for future periods. The field BackSuggestedForecast contains the values to be considered.

The countryside WMAPEThe error expected for the forecast period, based on the errors obtained by testing the backtestsThe Datup system simulates the forecast of commodity prices for previous periods, where the actual behaviour is known, hence it is possible to measure performance. By default, Datup performs 5 of these simulations, or backtests in each iteration of the models.

Finally, it is suggested to take into account the column RankingThis allows to determine the items with the highest, consumption, turnover and price stability. In other words, it identifies which items are the most and least valuable for the business.


Intermediate Use: Suggested Intervals

This use guides better decision making for:

  • All the advantages of the basic journey.

  • Know which items are recurrently over-priced with respect to their historical prices to prevent excessive inventory losses.

  • Know which items are recurrently under-predicted with respect to their forecast history to prevent out-of-stock losses or stock-outs.

The second scenario accompanies the suggested forecasts with suggested ranges, allowing the analyst to know the items with consistent under- or over-forecasts in the past. The column SuggestedInterval indicates the forecast interval associated with the suggested forecast in column SuggestedForecast. Accordingly, the suggested upside forecasts (NextSuggestedForecast) and downward (BackSuggestedForecast) are also accompanied by their suggested intervals NextSuggestedIntervaly BackSuggestedIntervalrespectively.

The forecast intervals SuggestedInterval, NextSuggestedInterval y BackSuggestedIntervalare not only calculated for the periods to be forecast, but also for each forecast period. backtest or simulation. It is precisely in this way that it is possible to determine what is the forecast interval, and hence the most likely suggested forecast, based on its repetition in the observed price history.

As a practical matter, the demand analyst or planner can determine whether an item is susceptible to over-demand if the suggested range SuggestedInterval has one of the following values: Lo95, Lo80 o Lo60. On the other hand, the item is susceptible to under-demand if the SuggestedInterval presents either Up60, Up80 o Up95. In both cases, the under- or over-demand is established with respect to the point forecast. ForecastPoint which lies in the middle of all forecast intervals.

Using forecast intervals allows the planning process to anticipate and avoid overproduction or overstocking events, as well as stock-outs or stock-outs.

Advanced Use: Naive Forecasting and Forecast Intervals

This use guides better decision making for:

  • All the advantages of the intermediate journey.

  • Examine naïve forecasts, i.e. averages or last observations, to plan demand on items with high variation or intermittency and increase their accuracy.

  • Examine the suggested forecasts for each item and period to see how high or low prices may behave in different market circumstances.

  • Evaluate the MASE error to determine in which cases it is advisable to use naïve forecasts to increase planning accuracy.

The third and final scenario involves the naïve forecast and all the associated forecast intervals estimated by Datup. This collection of values allows the analyst to know all possible scenarios of high and low forecast prices.

The forecasts point ForecastPointlower 95 ForecastLo95, lower 80 ForecastLo80, lower 60ForecastLo60, higher 60 ForecastUp60, higher than 80 ForecastUp80 and above 95 ForecastUp95 The total number of points Datup estimates for each date and item to consider the different scenarios of high, medium and low. The algorithm automatically selects the scenario, and in turn the most likely value. However, in the results cube, the results are presented in an open way to the analyst to enable possible additional exercises.

For its part, the naïve prognosis in the columnForecastNaive calculates the forecast value by means of a rolling window average. The size of the window coincides with the number of periods to forecast. For example if you want to forecast 4 weeks, the rolling window calculates the naive forecast by taking the average of 4 weeks at a time from history. Datup compares the performance of its model against the naïve forecast, as there are items whose stability in price history suffers from high intermittency and/or random behaviour that prevent their forecasting through traditional or advanced statistical methods. The value of the MASE shows the result of this comparison. Values close to 1 (above or below) confirm the superior performance of the Datup model, while values well above 1 favour the use of the naive forecast. ForecastNaive. If this is the scenario, in the bucket, the naïve forecast will be the value suggested as the forecast for the period evaluated.

Advanced Journey: Impact, Correlation and Causality

This journey guides decision making to:

  • Identify the variables or indicators that have the greatest impact on the forecast for one or more items of interest. Upward or downward changes in the associated variables are most likely to produce significant variations in the planning forecasts.

  • Determine the variables or indicators correlated with the items of interest. That is, the variables whose upward or downward changes produce changes in the Items of Interest, in the same proportion.

  • Determine the causal variables or indicators for the items of interest. That is, the variables whose upward or downward changes are a direct cause for the future behaviour of the items of interest.

Journey: Impact

In conjunction with demand planning, it is possible to know the items, variables or indicators that have the greatest impact on forecasts, accompanied by a percentage of importance; the higher the percentage, the greater the importance of the variable. Datup automatically identifies and orders the determining variables in the planning. In this way, analysts can focus their efforts on tracking key indicators whose changes have a direct and considerable influence on forecasts. In the example in the graph, US Existing Home Sales is the most important indicator for the generated forecasts with a 15% share, followed by the Acetic Acid Index (7.4%), Acetic Acid RMB (6.4%) and US Ethanol Price (6.2%). Thus, analysts can identify among hundreds of variables, which are the top-n that really influence purchase prices, sales quantities or demand units for future periods, in order to put in place better targeted trading, purchasing, production or distribution strategies in advance.

Journey: Correlation

Correlation provides a second criterion in the selection of variables or indicators that deserve greater attention in the decision-making processes for purchasing, negotiation, production or distribution, as it determines which variables whose upward or downward trends generate a direct or inverse behaviour in an item of interest. In general, variables with a direct correlation between 0.7 and 1 or an inverse correlation between -1 and -0.7, deserve to be taken into account. In this case, the item or indicator of interest is the raw material purchase price. Vynyl Acetate Monomer (VAM). The directly correlated indicators are: Price VAM ADC (0.9), VAM Index (0.86), Acetic Acid Index (0.85) y Acetic Acid RMB (0.85). At this point, special attention should be paid to those variables that appear with high percentages of importance and high correlations, i.e. Acetic Acid Index y Acetic Acid RMBas their upward variations are even more likely to produce upward variations also in the price of VAM. It is important to check if there are variables with high negative correlations (-1 to -0.7), as they may indicate an inverse behaviour, a rise in the variable will indicate a downward trend in the Price VAM. In this example there are no inversely correlated variables.

Journey: Causality

Finally, causality is the last criterion in the analysis and selection of important variables in price, sales or demand forecasts. Causality, unlike correlation, allows establishing a cause-effect relationship between a group of variables and indicators and the items of interest. Based on the forecasts, Datup determines which are the variables whose behaviours are the cause of upward or downward changes in the target items. For example, it is observed that VAM price forecasts are mostly caused by the Methanol China Index (0.5), New Orleans Temperature (0.39), Acetic Acid Index (0.35), Qingdao Temperature (0.31), US Price Ethanol (0.31) . Once again, it is important to pay attention to those variables common to impact, correlation and causality analysis, as they form the set of key indicators to be taken into account by analysts for their operational and strategic decisions. Here, Price Ethanol is identified in both impact and causality analysis. Even more relevant, the Acetic Acid Index is the common denominator in all 3 significance analyses.

Thus, the key indicators in order of importance to guide Price Vynil Acetate Monomer (VAM) purchasing strategies, following the criteria of impact, causality and correlation are:

  1. Acetic Acid Index

  2. Acetic Acid RMB

  3. US Ethanol Price

  4. US Existing Home Sales

  5. Methanol China Index

  6. New Orleans Temperature

  7. Qingdao Temperature

Finally, it should be noted that each new daily, weekly or monthly forecast involves the estimation of high-impact variables and indicators, causalities and correlations, based on changes observed in the most recently collected data. Datup automatically and continuously identifies and presents to analysts the top-n of key indicators to be taken into account for the best decision making in trading, purchasing, production or distribution.


Journey Advanced: Scenario Simulation

This journey guides decision making to:

    • Simulate the behaviour of items of interest, based on the increase, maintenance or decrease in the values of specific variables and indicators.

Taking the identified high impact, causal and/or correlated indicators identified by Datup's importance analysis as a starting point, it is possible to forecast the quantity or price of an item of interest in future periods. Unlike the forecasts of the first part, the simulation not only considers historical data, but also builds various scenarios from the key indicators to generate the forecasts of the items of interest; covering increases of 10% and 30%, drops of -10% and -30% or the permanence of the current value. In addition to the indicators postulated by the impotance analysis, analysts can include additional variables to simulate.

In the example use case, Price Vynil Acetate Monomer (VAM) is the item of interest, the key indicators to simulate are: Acetic Acid RMB, US Ethanol Price, US Existing Home Sales. In addition, the supporting indicators Dollar TRM and WTI Crude USD are included in the simulation.

The results show the simulation of 5 scenarios that can be presented by the Acetic Acid RMB in the short-term forecasts, which correspond to increases of 10% ($9,995) and 30% ($11,765), falls of -10% ($8,145) and -30% ($6,335) and the current price ($9,050) remaining unchanged. For each scenario, Datup forecasts the Price VAM most likely in the column Pronóstico VAMaccompanied by the ceiling price Pronóstico VAM Sup and floor Pronóstico VAM Inf where it can be located, depending on the uncertainties observed in its history. For example, if the Acetic Acid RMB increases its current price by 10% to reach $9,995, the Price VAM will stand at $2,309, with a ceiling at $2,337 and a floor at $2,282.

Now, taking the TRM dollar as one of the variables to be simulated, we consider the scenario where the exchange rate falls by 10% to $3,460. In this way, the Price VAM will be $2,246, with a ceiling at $2,284 and a floor at $2,213.

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