The Experts below are selected from a list of 134070 Experts worldwide ranked by ideXlab platform
Damiano Milanato - One of the best experts on this subject based on the ideXlab platform.
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Processi di Demand Planning
UNITEXT, 2008Co-Authors: Damiano MilanatoAbstract:La disciplina gestionale del Demand Planning presenta diversi aspetti critici, legati al contesto di business, che la rendono un macroprocesso chiave per la competitivita delle aziende consorziate in sistemi integrati di Supply Chain Network.
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Sistemi organizzativi di Demand Planning
UNITEXT, 2008Co-Authors: Damiano MilanatoAbstract:I processi gestionali di Demand Planning si collocano all’interno delle attivita di pianificazione della domanda, della produzione e della distribuzione, nell’ambito dei cicli di programmazione e controllo del Sales & Operations Planning.
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Sistemi informativi di Demand Planning
UNITEXT, 2008Co-Authors: Damiano MilanatoAbstract:Le tre tipologie base di attivita di Demand Planning (operative, gestionali ed analitiche) sono abilitate dall’utilizzo dei moderni strumenti di Information & Communication Technology (ICT), aventi finalita di: automazione delle attivita ripetitive e standard tipiche dei cicli esecutivi SCE; archiviazione ed organizzazione strutturata dei dati target e consuntivi rilevati nel corso dei processi SCP e SCE; supporto decisionale nei processi di pianificazione strategica, tattica, operativa.
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Demand Planning e Supply Chain Management
UNITEXT, 2008Co-Authors: Damiano MilanatoAbstract:Le moderne aziende industriali progettano, producono e distribuiscono prodotti finiti e servizi da immettere nei mercati finali di vendita, dove i clienti manifestano la volonta di acquisto per tali prodotti e servizi, recandosi presso le strutture preposte alla vendita diretta, nel caso dei consumatori finali, o stipulando accordi e contratti di acquisto con i fornitori di beni intermedi, nel caso in cui i clienti siano altre aziende industriali.
Jakey Blue - One of the best experts on this subject based on the ideXlab platform.
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Performance Analysis of Demand Planning Approaches for Aggregating, Forecasting and Disaggregating Interrelated Demands
International Journal of Production Economics, 2010Co-Authors: Argon Chen, Jakey BlueAbstract:A synchronized and responsive flow of materials, information, funds, processes and services is the goal of supply chain Planning. Demand Planning, which is the very first step of supply chain Planning, determines the effectiveness of manufacturing and logistic operations in the chain. Propagation and magnification of the uncertainty of Demand signals through the supply chain, referred to as the bullwhip effect, is the major cause of ineffective operation plans. Therefore, a flexible and robust supply chain forecasting system is necessary for industrial planners to quickly respond to the volatile Demand. Appropriate Demand aggregation and statistical forecasting approaches are known to be effective in managing the Demand variability. This paper uses the bivariate VAR(1) time series model as a study vehicle to investigate the effects of aggregating, forecasting and disaggregating two interrelated Demands. Through theoretical development and systematic analysis, guidelines are provided to select proper Demand Planning approaches. A very important finding of this research is that disaggregation of a forecasted aggregated Demand should be employed when the aggregated Demand is very predictable through its positive autocorrelation. Moreover, the large positive correlation between Demands can enhance the predictability and thus result in more accurate forecasts when statistical forecasting methods are used.
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Demand Planning approaches to aggregating and forecasting interrelated Demands for safety stock and backup capacity Planning
International Journal of Production Research, 2007Co-Authors: Argon Chen, Jakey BlueAbstract:Results of Demand Planning serve as the basis of every Planning activity in a Demand–supply network and ultimately determine the effectiveness of manufacturing and logistic Planning, such as capacity and safety stock Planning, in the network. The uncertainty of Demand signals that are propagated and magnified over the network becomes the crucial cause of ineffective operation plans. With the globalization of Demand–supply networks and the desire for a more integrated operation plan, Demand Planning is now one of the greatest challenges facing manufacturers. To manage the Demand variability, appropriate Demand aggregation and statistical forecasting approaches are known to be effective. This paper will use the bivariate VAR(1) time-series model as a study vehicle to investigate the effects of aggregating two interrelated Demands. It is shown that the aggregated time series of two VAR(1) times series is equivalent to the sum of two AR(1) time series. Through theoretical development, the paper further explor...
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Demand Planning Approaches to Aggregating and Forecasting Interrelated Demands for Safety Stock and Backup Capacity Planning
International Journal of Production Research, 2007Co-Authors: Argon Chen, Jakey BlueAbstract:Results of Demand Planning serve as the basis of every Planning activity in a Demand-supply network and ultimately determine the effectiveness of manufacturing and logistic Planning, such as capacity and safety stock Planning, in the network. The uncertainty of Demand signals, that are propagated and magnified over the network, becomes the crucial cause of ineffective operation plans. With the globalization of Demand-supply networks and the desire for a more integrated operation plan, Demand Planning is now one of greatest challenges facing manufacturers. To manage the Demand variability, appropriate Demand aggregation and statistical forecasting approaches are known to be effective. This paper will use the bivariate VAR(1) time series model as a study vehicle to investigate the effects of aggregating two interrelated Demands. We show that the aggregated time series of two VAR(1) times series is equivalent to the sum of two AR(1) time series. Through theoretical development, we further explore the properties of the aggregated time series and provide guidelines for practitioners to determine proper aggregation and forecasting approaches. A very important finding of our research is that Demand aggregation is far more effective than statistical forecasting in operations Planning for any two Demands with low positive correlation or negative correlation.
Artur Swierczek - One of the best experts on this subject based on the ideXlab platform.
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Investigating the role of Demand Planning as a higher-order construct in mitigating disruptions in the European supply chains
The International Journal of Logistics Management, 2020Co-Authors: Artur SwierczekAbstract: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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Demand Planning as a tamer and trigger of operational risk disruptions: evidence from the European supply chains
Supply Chain Management, 2019Co-Authors: Artur Swierczek, Natalia SzozdaAbstract:The purpose of this paper is to explore the effects of Demand Planning practices on the disruptions induced by operational risk. The study reveals whether the negative consequences of operational risk factors (covering Demand, supply, control and process risks) can be absorbed or amplified through the application of specific Demand Planning practices in supply chains.,The study involves the partial least squares path model procedure. Likewise, the items of the constructs in the outer model were subjected to a purification process by principal component analysis with the orthogonal (varimax) and oblique (Promax) methods of rotation.,The findings suggest that although one may not observe uniformity and standardization in the role of Demand Planning in alleviating the negative effects of operational risks, still some regularities can be obtained. Having said that some Demand Planning practices tend to mitigate or reinforce disruptions driven by operational risk, whereas the other practices simultaneously absorb and amplify disruptions driven by operational risk.,The study shows that different managerial instruments, which are not inherently dedicated to risk management, when appropriately applied, may have an indirect impact on the mitigation of supply chain risk. In particular, the concept of Demand Planning might be very helpful for managers when dealing with Demand and control risks.,The study simultaneously examines a more detailed bundle of practices forming the Demand Planning process. The research attempts to investigate the link between the Demand Planning process and operational risk consequences, derived from all sources (supply, Demand, process and control). The paper shows that risk management is not a sole tool to mitigate disruptions. Among the concepts, which contribute to decrease risks is the Demand Planning process. The study demonstrates that the Demand Planning process when applied as a component of supply chain management, may contribute to mitigate certain operational risks.
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Demand Planning as a tamer and trigger of operational risk disruptions: evidence from the European supply chains
Supply Chain Management: An International Journal, 2019Co-Authors: Artur Swierczek, Natalia SzozdaAbstract:Purpose The purpose of this paper is to explore the effects of Demand Planning practices on the disruptions induced by operational risk. The study reveals whether the negative consequences of operational risk factors (covering Demand, supply, control and process risks) can be absorbed or amplified through the application of specific Demand Planning practices in supply chains. Design/methodology/approach The study involves the partial least squares path model procedure. Likewise, the items of the constructs in the outer model were subjected to a purification process by principal component analysis with the orthogonal (varimax) and oblique (Promax) methods of rotation. Findings The findings suggest that although one may not observe uniformity and standardization in the role of Demand Planning in alleviating the negative effects of operational risks, still some regularities can be obtained. Having said that some Demand Planning practices tend to mitigate or reinforce disruptions driven by operational risk, whereas the other practices simultaneously absorb and amplify disruptions driven by operational risk. Practical implications The study shows that different managerial instruments, which are not inherently dedicated to risk management, when appropriately applied, may have an indirect impact on the mitigation of supply chain risk. In particular, the concept of Demand Planning might be very helpful for managers when dealing with Demand and control risks. Originality/value The study simultaneously examines a more detailed bundle of practices forming the Demand Planning process. The research attempts to investigate the link between the Demand Planning process and operational risk consequences, derived from all sources (supply, Demand, process and control). The paper shows that risk management is not a sole tool to mitigate disruptions. Among the concepts, which contribute to decrease risks is the Demand Planning process. The study demonstrates that the Demand Planning process when applied as a component of supply chain management, may contribute to mitigate certain operational risks.
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The effect of supply chain integration on Demand Planning process. An empirical evaluation
2013 10th International Conference on Service Systems and Service Management, 2013Co-Authors: Natalia Szozda, Artur SwierczekAbstract:One of supply chain management concepts, strongly conditioned upon inter-organizational integration, is Demand Planning, encompassing a sequence of activities concerning the coordinated flow of Demand through companies, effectively supported by specific methods and technical instruments. The paper seeks to explore the contribution of multidimensional aspects of supply chain integration in the methods and instruments supporting Demand Planning process. In order to achieve this goal, the research methodology was employed. Having performed a Principal Component Analysis (PCA) with varimax rotation in a space of the variables manifesting supply chain integration, the constructs were extracted and employed as classification criteria in a cluster analysis. The results of the study show that the examined supply chains may be grouped into three classes having distinct characteristics. The findings of the paper demonstrate the types and intensity of the use of methods and instruments supporting Demand Planning process regarding the level of supply chain integration.
Natalia Szozda - One of the best experts on this subject based on the ideXlab platform.
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Demand Planning as a tamer and trigger of operational risk disruptions: evidence from the European supply chains
Supply Chain Management, 2019Co-Authors: Artur Swierczek, Natalia SzozdaAbstract:The purpose of this paper is to explore the effects of Demand Planning practices on the disruptions induced by operational risk. The study reveals whether the negative consequences of operational risk factors (covering Demand, supply, control and process risks) can be absorbed or amplified through the application of specific Demand Planning practices in supply chains.,The study involves the partial least squares path model procedure. Likewise, the items of the constructs in the outer model were subjected to a purification process by principal component analysis with the orthogonal (varimax) and oblique (Promax) methods of rotation.,The findings suggest that although one may not observe uniformity and standardization in the role of Demand Planning in alleviating the negative effects of operational risks, still some regularities can be obtained. Having said that some Demand Planning practices tend to mitigate or reinforce disruptions driven by operational risk, whereas the other practices simultaneously absorb and amplify disruptions driven by operational risk.,The study shows that different managerial instruments, which are not inherently dedicated to risk management, when appropriately applied, may have an indirect impact on the mitigation of supply chain risk. In particular, the concept of Demand Planning might be very helpful for managers when dealing with Demand and control risks.,The study simultaneously examines a more detailed bundle of practices forming the Demand Planning process. The research attempts to investigate the link between the Demand Planning process and operational risk consequences, derived from all sources (supply, Demand, process and control). The paper shows that risk management is not a sole tool to mitigate disruptions. Among the concepts, which contribute to decrease risks is the Demand Planning process. The study demonstrates that the Demand Planning process when applied as a component of supply chain management, may contribute to mitigate certain operational risks.
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Demand Planning as a tamer and trigger of operational risk disruptions: evidence from the European supply chains
Supply Chain Management: An International Journal, 2019Co-Authors: Artur Swierczek, Natalia SzozdaAbstract:Purpose The purpose of this paper is to explore the effects of Demand Planning practices on the disruptions induced by operational risk. The study reveals whether the negative consequences of operational risk factors (covering Demand, supply, control and process risks) can be absorbed or amplified through the application of specific Demand Planning practices in supply chains. Design/methodology/approach The study involves the partial least squares path model procedure. Likewise, the items of the constructs in the outer model were subjected to a purification process by principal component analysis with the orthogonal (varimax) and oblique (Promax) methods of rotation. Findings The findings suggest that although one may not observe uniformity and standardization in the role of Demand Planning in alleviating the negative effects of operational risks, still some regularities can be obtained. Having said that some Demand Planning practices tend to mitigate or reinforce disruptions driven by operational risk, whereas the other practices simultaneously absorb and amplify disruptions driven by operational risk. Practical implications The study shows that different managerial instruments, which are not inherently dedicated to risk management, when appropriately applied, may have an indirect impact on the mitigation of supply chain risk. In particular, the concept of Demand Planning might be very helpful for managers when dealing with Demand and control risks. Originality/value The study simultaneously examines a more detailed bundle of practices forming the Demand Planning process. The research attempts to investigate the link between the Demand Planning process and operational risk consequences, derived from all sources (supply, Demand, process and control). The paper shows that risk management is not a sole tool to mitigate disruptions. Among the concepts, which contribute to decrease risks is the Demand Planning process. The study demonstrates that the Demand Planning process when applied as a component of supply chain management, may contribute to mitigate certain operational risks.
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The effect of supply chain integration on Demand Planning process. An empirical evaluation
2013 10th International Conference on Service Systems and Service Management, 2013Co-Authors: Natalia Szozda, Artur SwierczekAbstract:One of supply chain management concepts, strongly conditioned upon inter-organizational integration, is Demand Planning, encompassing a sequence of activities concerning the coordinated flow of Demand through companies, effectively supported by specific methods and technical instruments. The paper seeks to explore the contribution of multidimensional aspects of supply chain integration in the methods and instruments supporting Demand Planning process. In order to achieve this goal, the research methodology was employed. Having performed a Principal Component Analysis (PCA) with varimax rotation in a space of the variables manifesting supply chain integration, the constructs were extracted and employed as classification criteria in a cluster analysis. The results of the study show that the examined supply chains may be grouped into three classes having distinct characteristics. The findings of the paper demonstrate the types and intensity of the use of methods and instruments supporting Demand Planning process regarding the level of supply chain integration.
Argon Chen - One of the best experts on this subject based on the ideXlab platform.
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Performance Analysis of Demand Planning Approaches for Aggregating, Forecasting and Disaggregating Interrelated Demands
International Journal of Production Economics, 2010Co-Authors: Argon Chen, Jakey BlueAbstract:A synchronized and responsive flow of materials, information, funds, processes and services is the goal of supply chain Planning. Demand Planning, which is the very first step of supply chain Planning, determines the effectiveness of manufacturing and logistic operations in the chain. Propagation and magnification of the uncertainty of Demand signals through the supply chain, referred to as the bullwhip effect, is the major cause of ineffective operation plans. Therefore, a flexible and robust supply chain forecasting system is necessary for industrial planners to quickly respond to the volatile Demand. Appropriate Demand aggregation and statistical forecasting approaches are known to be effective in managing the Demand variability. This paper uses the bivariate VAR(1) time series model as a study vehicle to investigate the effects of aggregating, forecasting and disaggregating two interrelated Demands. Through theoretical development and systematic analysis, guidelines are provided to select proper Demand Planning approaches. A very important finding of this research is that disaggregation of a forecasted aggregated Demand should be employed when the aggregated Demand is very predictable through its positive autocorrelation. Moreover, the large positive correlation between Demands can enhance the predictability and thus result in more accurate forecasts when statistical forecasting methods are used.
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Demand Planning approaches to aggregating and forecasting interrelated Demands for safety stock and backup capacity Planning
International Journal of Production Research, 2007Co-Authors: Argon Chen, Jakey BlueAbstract:Results of Demand Planning serve as the basis of every Planning activity in a Demand–supply network and ultimately determine the effectiveness of manufacturing and logistic Planning, such as capacity and safety stock Planning, in the network. The uncertainty of Demand signals that are propagated and magnified over the network becomes the crucial cause of ineffective operation plans. With the globalization of Demand–supply networks and the desire for a more integrated operation plan, Demand Planning is now one of the greatest challenges facing manufacturers. To manage the Demand variability, appropriate Demand aggregation and statistical forecasting approaches are known to be effective. This paper will use the bivariate VAR(1) time-series model as a study vehicle to investigate the effects of aggregating two interrelated Demands. It is shown that the aggregated time series of two VAR(1) times series is equivalent to the sum of two AR(1) time series. Through theoretical development, the paper further explor...
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Demand Planning Approaches to Aggregating and Forecasting Interrelated Demands for Safety Stock and Backup Capacity Planning
International Journal of Production Research, 2007Co-Authors: Argon Chen, Jakey BlueAbstract:Results of Demand Planning serve as the basis of every Planning activity in a Demand-supply network and ultimately determine the effectiveness of manufacturing and logistic Planning, such as capacity and safety stock Planning, in the network. The uncertainty of Demand signals, that are propagated and magnified over the network, becomes the crucial cause of ineffective operation plans. With the globalization of Demand-supply networks and the desire for a more integrated operation plan, Demand Planning is now one of greatest challenges facing manufacturers. To manage the Demand variability, appropriate Demand aggregation and statistical forecasting approaches are known to be effective. This paper will use the bivariate VAR(1) time series model as a study vehicle to investigate the effects of aggregating two interrelated Demands. We show that the aggregated time series of two VAR(1) times series is equivalent to the sum of two AR(1) time series. Through theoretical development, we further explore the properties of the aggregated time series and provide guidelines for practitioners to determine proper aggregation and forecasting approaches. A very important finding of our research is that Demand aggregation is far more effective than statistical forecasting in operations Planning for any two Demands with low positive correlation or negative correlation.