How ensemble approaches help supply chain managers

Gauging forecast is one of the important aspects of businesses in the logistics industry and supply chain market. This step helps in future forecasting of sales as well as effective inventory management and financial planning.

An inaccurate or wrong forecast can render a negative impact on the company. Accurate forecasting, on the other hand, can pave the way for accuracy in operations and profit generation.

Demand forecasting: A vital element of the supply chain industry

Demand forecasting occupies a prominent position in the supply chain industry. It is a part of prediction that not only helps in garnering profits but also in maintaining the number of products. It is one of the major factors driving the planning and decision-making processes of supply chain managers.

Any inaccuracy or inefficiency could significantly impact organizations. The more accurate our forecasting is; the less deviation will be from the sales and vice versa!

Organisations in the supply chain domain are increasingly treading on the transformation path to adopt innovations so as to survive and thrive in the market as well as to enjoy a competitive edge in the market. They are well aware that the process of forecasting is majorly dependent on the machinery used.

This pushes the supply chain managers to bid goodbye to the traditional models and adopt new-age ensemble approaches for effective and accurate forecasting.

The emergence of ensemble approaches as an innovative solution

Ever since digital disruption picked up pace due to the pandemic, tech deployment has been prompt in the sector. Solutions such as AI (Artificial Intelligence) and ML (Machine Learning) have forayed into the segment and are being used in various processes.

Machine Learning specifically has occupied a prominent position in the supply chain market in the form of ensemble approaches helping in the maintenance of products and forecasting.

ALSO READ

Supply chain disruption remains for retailers in 2022, but contracts can help

The averaging method

The averaging method has gained prominence over the period of time. It prioritises the algorithms which drive accuracy and help decrease deviation from sales.

It helps in better forecasting of products as compared to statistical and time-series algorithms including ARIMA, Moving Average, Exponential Smoothing, etc. when used individually.

First, the data is processed using the MAD concept (Median Absolute Deviation) followed by the process of dataset creation. The over-forecasting and under-forecasting are nullified thereby ensuring the predictions are close to actuality.

In this approach, the Time series algorithm and Regression-based algorithm combine to produce one optimal predictive model. The two methods blend with a specific weightage to both the forecasts method and provide the outcomes that are accurate and precise.

The Time series model is developed in R-studio and the Regression-based model using data mining algorithms is developed in Python. Once the results are generated, an ensemble of the results is cross-checked and further calculated using Microsoft Excel.

The BAGGING approach

Another popular ensemble approach is BAGGING which combines Boostrapping and AGGregating. Multiple bootstrapped data samples possessing the same set of features are culled out from the dataset.

The predictive value of each of the bootstrapped sub-samples is then calculated. An algorithm is used to aggregate over these and come up with an efficient predictor at the final stage.

The concept of Random Forest

The Random Forest model is also a new-age ensemble method where the dataset is split on a random selection of features. Hence, in this scenario, every sub-sample will be distinct. This approach applies the differentiation method and this differentiation level provides a greater ensemble to aggregate over thereby producing an accurate predictor.

Summing up

In addition to the above, data scientists also combine moving average models with a Bayesian belief network to produce an ensemble method that possesses the potential to enhance forecasting accuracy.

Be it any approach, Machine Learning as a tech solution aims at finding a model that will provide the best outcome and accurate prediction for the firms.

Rather than creating one such model, the ensemble approach is now preferred that takes into account various other models and combines them seamlessly to produce one final model that could suffice our purpose.

Accurate demand forecasting helps in optimal supply availability, inventory planning as well as management of products. Ensemble approaches help make better predictions and achieve the goals of better performance. It is thus gaining popularity as a new-age tool for prediction and demand forecasting techniques in the supply chain segment!