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Demantra Engine Tuning

Executive Summary

Many companies continue to struggle with poor forecast numbers even after implementing Demand Management (DM) or Advanced Forecasting (AFDM) modules. The promise of a more accurate forecast after the implementation of Demantra is not fulfilled because demand planners have to resort to manual instructions for a large number of records which leads to a lengthy forecast review process that causes significant delays in the overall management process. of demand.

The most common cause of bad forecast numbers produced by Demantra is that the engine does not take into account the needs of the customer’s specific data set.

Ideally, during implementation, a lot of time should be spent on analyzing the data set and configuring engine parameters while keeping the customer’s specific data model in mind. Unfortunately, it has been found that Demantra engine training is given the lowest priority and is usually left until after the live period.

Many times during the implementation of Demantra, both consultants and business users are so focused on meeting the requirements that they need with worksheets, series, workflows etc. zeal to destroy the Demantra engine.

Also, since Demantra engine tuning is a specialist skill; it requires a deep understanding of the various factors that contribute to better predictive accuracy and an understanding of the various engine parameters that can be set up for better results.

Although this is a specialized area and should be done by highly qualified and experienced consultants, users of Demantra and demand planners should also be familiar with various factors that can affect the accuracy of the forecast.

There are many factors that affect the accuracy of Demantra prediction but some of the most important ones are listed below:

• Demand Data Profiles

• Nodal tuning

• Reasons / Promotions

• Forecast Tree

• Proportion function

Demand Data Profiles

The first step towards a better Demantra forecast is to identify the different demand profiles that apply to the customer’s business.

The demand data pattern can be intermittent, regular, moderate etc. and knowing these demand patterns for different products will help Demantra use the right statistical model for forecasting.

Oracle Demantra uses statistical methods and different algorithms to predict the future. The Demantra DM model uses eight statistical methods while the Demantra AFDM uses fourteen different methods for statistical forecasting. Both the Demantra DM and AFDM modules use the Bayesian approach to generate the posterior prediction for a particular open-space dataset.

The Bayesian approach combines the results of individual models. Each model is evaluated, and each model in turn tests a number of system subsystems and user-supplied causal factors. All model combinations and subsets of causal factors are weighted to indicate their significance. Each combination contributes to the final prediction according to its weight.

Therefore, understanding the demand patterns of your products can help you implement the right forecasting method on the item-location combination in Demantra which will significantly improve the forecasting accuracy.

For example, if you already know that there is a product line that only shows intermittent demand patterns, then cutting out other forecasting models for this combination can significantly improve forecast accuracy because other forecasting methods will not contribute to the final forecast number.

The following forecasting models are used by Demantra:

• Regression

• Regression

• Login (login conversion before backup)

• CMReg (Markov chain selection of a subset of causal factors)

• Elog (uses Markov chain after log transformation)

• Significant illness

• Holt

• Bwint

• Intermediate Models

• CMReg for Intermittent

• Regression to Interval

• Croston

• Time Series Models

• ARX and ARIX

• Logistics and AR Logistics

• Other models

• BWint (a combination of regression and extreme flexibility)

Nodal Tuning

One of the reasons for poor forecast accuracy for customers using Demantra’s Demand Management (DM) module is that statistical methods and algorithms are either applied to all aggregates or not applied at all. The selection of particular statistical models does not differentiate a particular collection from the rest of the population, even if the demand pattern exhibited by that item-location combination is different from the rest of the collection. This is proving to be a major hurdle for Demantra DM module customers during forecasting.

This constraint is overcome in the Demantra AFDM module which provides advanced analytical capabilities with the Nodal tuning feature.

Nodal Tuning is a powerful functionality available in the Advanced Demand Management (AFDM) module.

Nodal Tuning allows demand planners to pick and choose which statistical models the engine should apply to a specific location-combination to generate a system forecast and also allows setting engine parameters for that combination.

The nodal arrangement also allows to adjust the parameters of the Demantra engine which are specific to the comparison.

This feature puts a tool in the hands of Demantra experts to tune the engine for better predictive accuracy. This feature along with the knowledge of demand patterns​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​ should be able to enable users to only activate forecasting models that match the demand pattern. This greatly increases the accuracy of the prediction.

Causal Factors / Promotions

One has to be very careful when modeling causal factors in Demantra. If the Data Model has many causal factors and enhancements, they tend to reduce the baseline prediction and arrive at a very skewed prediction.

A good practice of introducing causal factors into the model is to first start without causal factors and development data to generate a basic prediction from Demantra. Once the basic prediction is fixed, other causal factors should be introduced one by one and the effect of introducing each causal factor on the basic prediction should be taken into account.

In this way, the effect of causal factors on the underlying prediction can be easily monitored and analyzed, and should be excluded whenever causal attribution does not seem to have the desired effect.

Forecast Tree

The prediction tree determines at which object/location combination the engine will predict. The engine examines each level in the forecast tree and verifies whether there is enough sales history data for the forecast or if the forecast generated has sufficient accuracy at that level. If validation fails, the engine moves to the next level and continues the validation phase until it finds a level that can make a prediction.

If the engine terminates the prediction at a higher level of aggregation in the prediction tree, the prediction is split into lower levels.

A prediction tree is a system configuration that has a direct bearing on prediction accuracy.

This is one of the first settings that should be made after a careful analysis of sales history and after discussion with users. The level of prediction should be meaningful to business users and it is recommended to have between 3 and 6 levels that the engine can monitor and predict.

It is useful for the prediction tree to measure the level accurately, if possible.

Ratio

Ratios are very important and are used while summarizing the forecast from the lowest level to higher levels and extrapolating the forecast made at the higher level to the lower levels.

The final output of the built-in Demantra forecast can vary greatly depending on the rates.

Rates are calculated and stored when loading sales history data. Several parameters control the calculations of the rates.

One of the parameters that affects rates is the amount of sales history data the system uses to calculate rates. Rates calculated based on 12 months of sales data will be different than those calculated based on 6 months of historical data. Therefore, the correct setting of this parameter is important for rate calculations which in turn affects the final prediction.

Results

Tuning a Demantra engine is a complex task and there is no one-size-fits-all solution for it.

A major engine overhaul should be done every two years and whenever there is a change in the product demand pattern. The tuning should be tailored to the client’s specific Demantra implementation, but their awareness of the factors that affect forecast accuracy will go a long way in improving forecast accuracy.

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