An Intelligent Approach to Demand Forecasting

Adhikari, Nimai Chand Das; Domakonda, Nishanth; Chandan, Chinmaya; Gupta, Gaurav; Garg, Rajat; Teja, Surya; Das, Lalit; Misra, Ashutosh

doi:10.1007/978-981-10-8681-6_17

Cited by 10 publications

(4 citation statements)

References 20 publications

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“…It is an important task for retailers as it is required for various operational decisions [11]. It goes without saying that if an organization does not get its forecasting accurate to a reasonable level, the whole supply chain gets affected [12]. Based on the paper's objective, we focus on demand prediction techniques, specifically machine learning regression algorithms and hybrid models to forecast demand.…”

Section: Research Background and Literature Reviewmentioning

confidence: 99%

Improved Demand Forecasting of a Retail Store Using a Hybrid Machine Learning Model

Taparia,

Mishra,

Gupta

et al. 2023

JGEU

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Accurate demand forecasting is a competitive advantage for all supply chain components, including retailers. Approaches like naïve, moving average, weighted average, and exponential smoothing are commonly used to forecast demand. However, these simple approaches may lead to higher inventory and lost sales costs when the trend in demand is non-linear. Additionally, price strongly influences demand, and we can’t neglect the impact of price on demand. Similarly, the demand for a stock keeping unit (SKU) depends on the price of the competitor for the same SKU and the price of the competitive SKU. We thus propose a demand prediction model that considers historical demand data and the SKU price to forecast the demand. Our approach uses different machine-learning regressor algorithms and identifies the best machine-learning algorithm for the SKU with the lowest forecasting error. We further extend the forecasting model by training a hybrid model from the best two regression algorithms individually for each SKU. Forecasting error minimisation is the driving criterion for our literature. We evaluated the approach on 1000 SKUs, and the result showed that the Random Forest is the best-performing regressor algorithm with the lowest mean absolute percentage error (MAPE) of 8%. Furthermore, the hybrid model resulted in a lower inventory and lost sales cost with a MAPE of 7.74%. Overall, our proposed hybrid demand forecasting model can help retailers make informed decisions about inventory management, leading to improved operational efficiency and profitability.

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Section: Research Background and Literature Reviewmentioning

confidence: 99%

Improved Demand Forecasting of a Retail Store Using a Hybrid Machine Learning Model

Taparia,

Mishra,

Gupta

et al. 2023

JGEU

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“…Croston Model -The model suitable for the products with intermittent demand is Croston [1]. It is a modification of exponential smoothing for sporadic demand product time series.…”

Section: Triple Exponential Smoothing (Tes) -mentioning

confidence: 99%

A Comprehensive Study on Demand Forecasting Methods and Algorithms for Retail Industries

Vikas¹,

Sunil²,

Hallikar³

et al. 2021

JUSST

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Without a doubt, demand forecasting is an essential part of a company’s supply chain. It predicts future demand and specifies the level of supply-side readiness needed to satisfy the demand. It is imperative that if a company’s forecasting isn’t reasonably reliable, the entire supply chain suffers. Over or under forecasted demand would have a debilitating impact on the operation of the supply chain, along with planning and logistics. Having acknowledged the importance of demand forecasting, one must look into the techniques and algorithms commonly employed to predict demand. Data mining, statistical modeling, and machine learning approaches are used to extract insights from existing datasets and are used to anticipate unobserved or unknown occurrences in statistical forecasting. In this paper, the performance comparison of various forecasting techniques, time series, regression, and machine learning approaches are discussed, and the suitability of algorithms for different data patterns is examined.

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“…The better bank can forecast strategic objective (as ROE), the more informed management can make timely decisions and enables management to communicate to shareholders or the public. Short-term forecasting is essential to avoid overoptimistic or pessimistic predictions, which may result in not optimal strategic objective, significant deviations in internal resource management and wrong expectations of shareholders and public (Adhikari et al, 2019).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Forecasting Banks Return on Equity Using Leading Economic Indicators

Jurevičienė

Rauličkis

2020

Business: Theory and Practice

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The research examines an approach to forecast return on equity using leading economic indicators for short periods in banks. ROE is one of the most important ratios for performance measurement. Its adequacy is necessary for competitiveness, attract funding in financial markets, accumulate reserve for future turbulences, secure compliance with supervisory requirements and maintain positive signals for the market. There is still a debate in the literature on factors of commercial banks’ profitability forecasting, techniques, and most appropriate models to improve the correctness of predicting and acquiring more accurate signals for communication on targets. The problems are still relevant from both a theoretical perspective and practical implementation. This research aims to prove the necessity to include leading economic indicators for short term ROE forecasting. It conducts investigations for the relevant studies, using regression analysis, necessary tests, ascertains opportunities and limitations of using these indicators and develops a conceptual model and its assessment major Baltic banks. The results show verification of approach to forecast ROE using leading economic indicators for short periods. Such study complements signalling theory with a new approach, how to predict and acquire signal not only using economic indicators as a general group but sub-group them into coinciding, lagging and leading.

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An Intelligent Approach to Demand Forecasting

Cited by 10 publications

References 20 publications

Improved Demand Forecasting of a Retail Store Using a Hybrid Machine Learning Model

Improved Demand Forecasting of a Retail Store Using a Hybrid Machine Learning Model

A Comprehensive Study on Demand Forecasting Methods and Algorithms for Retail Industries

Forecasting Banks Return on Equity Using Leading Economic Indicators

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