A new stochastic multi source approach to improve the accuracy of the sales forecasts

Bendato, Ilaria; Mosca, Marco; Mosca, Roberto

doi:10.1108/fs-07-2016-0036

Cited by 8 publications

(8 citation statements)

References 40 publications

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“…The importance of demand forecasting was underscored in several prior studies. For instance, Cassettari et al (2017) defined demand planning as the process encompassing several steps in developing reliable forecasts. As evidenced in our study, the second important demand planning practice is communicating the demand predictions and synchronizing supply with demand.…”

Section: Findings and Discussionmentioning

confidence: 99%

“…In this study, we consider demand planning as a dynamic routine involving a structured and measured set of practices, i.e. goal setting, data gathering, demand forecasting, communicating the demand predictions and synchronizing supply with demand (Croxton et al , 2008; Croxton et al , 2001; Bindra, 2014; Cassettari et al , 2017), designed to yield a specific output for a particular customer and market (Davenport, 1993). The overarching aim of demand planning is to keep the balance between supply and demand in a supply chain (Metcalfe, 2012; Raza and Kilbourn, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

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Investigating the role of demand planning as a higher-order construct in mitigating disruptions in the European supply chains

Świerczek

2020

IJLM

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PurposeThe goal of the paper is twofold. First, it aims to empirically conceptualize whether a wide array of fragmented demand planning activities, performed in supply chains, can be logically categorized into actionable sets of practices, which then form a broader conceptualization of the demand planning process. Second, regarding certain contextual factors, our research seeks to investigate the contribution of demand planning, as a higher-order construct, to mitigating disruptions induced by operational risks in supply chains.Design/methodology/approachIn this study, PLS-SEM was used to estimate the reflective-formative nature of the model. The results of PLS-SEM were additionally complemented by the assessment of the predictive power of our model. Finally, to reveal possible contingency effects, the multigroup analysis (MGA) was conducted.FindingsThe study suggests that demand planning process (DPP) is a second-order construct that is composed of four sets of practices, including goal setting, data gathering, demand forecasting, communicating the demand predictions and synchronizing supply with demand. The study also reveals that the demand planning practices, only when considered together, as a higher-order factor, significantly contribute to mitigating disruptions driven by operational risks. Finally, the research shows that the strength of the impact of demand planning on disruptions is contextually dependent.Research limitations/implicationsWhile the study makes some important contributions, the obtained findings ought to be considered within the context of limitations. First, the study only investigates disruptions driven by operational risks, ignoring the negative consequences of environmental risks (terrorist attacks, natural disasters, etc.), which may have a far more negative impact on supply chains. Second, the sample is mostly composed of medium and large companies, not necessarily representative of demand planning performed by the entire spectrum of companies operating in the market.Practical implicationsThe study shows that to effectively mitigate disruptions induced by operational risks, the demand planning practices should be integrated into a higher-order construct. Likewise, our research demonstrates that the intensity of demand planning process is contingent upon a number of contextual factors, including firm size, demand variability and demand volume.Social implicationsThe study indicates that to mitigate disruptions of operational risk, demand planning as a higher-order dynamic capability can be referred to the concept of organizational learning, which contributes to forming a critical common ground, ensuring the balance between formal and informal dynamic routines.Originality/valueThe paper depicts that to fully deal with disruptions, the demand planning practices need to be integrated and categorized into the dedicated higher-order. This may lead to forming demand planning as a higher-order dynamic capability that provides a more rapid and efficient rebuttal to any disruptions triggered by operational risks.

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Section: Findings and Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Investigating the role of demand planning as a higher-order construct in mitigating disruptions in the European supply chains

Świerczek

2020

IJLM

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“…3 ESTGA, CIDMA, University of Aveiro, Aveiro, Portugal. 4 DEGEIT, CIDMA, University of Aveiro, Aveiro, Portugal.…”

Section: Discussionmentioning

confidence: 99%

“…In order to improve the forecast, the probability density functions of the forecasts obtained by each of these individual methodologies were combined in a ensemble approach. In this approach, similar to the one proposed in [4] by Cassettari et al, a weighted linear combination of the density functions of the forecasts was used for obtaining a better final forecast. This methodology leads to more robust forecasts and allows to deal with non-linearity and seasonality.…”

Section: Model S-stochastic Inventory Modelmentioning

confidence: 99%

Inventory models with reverse logistics for assets acquisition in a liquefied petroleum gas company

et al. 2020

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This paper addresses a case study regarding inventory models for acquiring liquefied petroleum gas (LPG) cylinders. This is an industrial challenge that was proposed at an European Study Group with Industry, by a Portuguese energy company, for which the LPG cylinder is the main asset of its LPG business. Due to the importance of this asset, an acquisition plan must be defined in order to determine the amount of LPG cylinders to acquire, and when to acquire them, in order to optimize the investment. As cylinders are returned and refilled, the reverse logistic flows of these assets must be considered. As the classical inventory models are not suitable for this case study, three new inventory models, which account for the return of LPG cylinders, are proposed in this work. The first proposed model considers deterministic constant demand and continuous returns of LPG cylinders, with discrete replenishment from the supplier. The second model is similar, but for the case when the returned cylinders cover for the demand. A third model is also proposed considering that both the demand and the returns are stochastic in nature and the replenishment from the supplier is discrete. The three models address different scenarios that the company is either currently facing or is expecting to occur in the near future.

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“…Se o planejamento da demanda não for bem elaborado pode haver uma propagação de erros em toda a cadeia de suprimentos, ocasionando o efeito chicote, na qual prejudica a previsão de demanda e gera ineficiências em toda a cadeia de suprimentos ( VAN BELLE, GUNS e VERBEKE, 2021). O processo de planejamento de demanda deve conter, segundo Cassettari, et al (2017):…”

Section: Revisão Sistemática Da Literaturaunclassified

Impacto do uso de técnicas de aprendizado de máquina, big data e variáveis causais na gestão de demanda

Silva¹

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Este trabalho de dissertação tem como principal objetivo avaliar o efeito da utilização de algoritmos de aprendizado de máquina e Big Data na acuracidade de previsão de demanda, além de analisar a influência das variáveis causais internas e externas. Para alcançar esse objetivo, realizou-se uma revisão sistemática da literatura com os principais conceitos de gestão de demanda para avaliar os principais métodos, abordagens e fatores relevantes para construção do modelo de planejamento de demanda. Além disso, estudou-se também os principais algoritmos de aprendizado de máquina e Big Data, que pudessem melhorar no desempenho do modelo. Após, elaborou-se um procedimento metodológico de estudo de caso em uma empresa do segmento de agronegócio, com coleta e análise de dados da demanda planejada e realizada, dos últimos cinco anos e, na sequência, realizou-se análises quantitativas para validar as hipóteses e análises qualitativas através de entrevistas com os especialistas da área. Ambas análises foram importantes para elaboração final do modelo. Como resultado, obteve-se que a utilização das técnicas de aprendizado de máquina e Big Data, melhorou em 37% a acuracidade de previsão de demanda em relação ao método qualitativo de ajuste de julgamento utilizado pela empresa. Além disso, o modelo proposto em 100% dos casos, utilizou de métodos mais robustos e sofisticados de aprendizado de máquina, do que utilizar métodos tradicionais de séries temporais simples. Outro fator importante é que as variáveis causais internas contribuíram em 100% dos casos da família de fungicidas para melhoria do planejamento de demanda e, em contrapartida, as variáveis causas externas apoiou em 50% dos casos, para melhoria desse modelo. Conclui-se, então, que as principais contribuições deste trabalho de pesquisa foram: avaliar o desempenho da acuracidade de previsão de demanda comparando métodos clássicos de séries temporais, com métodos de aprendizado de máquina; verificar as influências das variáveis causais internas e externas nos modelos de previsão de demanda; utilizar software de planejamento de demanda com modelos mais robustos, de fácil utilização e com um rápido tempo de processamento; e por fim, estruturar uma revisão sistemática da literatura, com as pesquisas mais recentes dos estudiosos da área de gestão de demanda e Supply Chain 4.0, com intuito de utilizar as melhores pesquisas praticadas no mercado.Palavras-chave: aprendizado de máquina; acuracidade de previsão de demanda; cadeia de suprimentos digital; variáveis causais; Big Data.

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A new stochastic multi source approach to improve the accuracy of the sales forecasts

Cited by 8 publications

References 40 publications

Investigating the role of demand planning as a higher-order construct in mitigating disruptions in the European supply chains

Investigating the role of demand planning as a higher-order construct in mitigating disruptions in the European supply chains

Inventory models with reverse logistics for assets acquisition in a liquefied petroleum gas company

Impacto do uso de técnicas de aprendizado de máquina, big data e variáveis causais na gestão de demanda

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