Spatially distributed production data for supply chain models - Forecasting with hazardous waste

Pavlas, M.; Šomplák, Radovan; Šmejkalová, Veronika; Nevrlý, Vlastimír; Zavíralová, Lenka; Kůdela, Jakub; Popela, Pavel

doi:10.1016/j.jclepro.2017.06.107

Cited by 25 publications

(9 citation statements)

References 25 publications

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“…Second, usually, there is no clear idea about the shape of the dependency between given waste and predictor. Forecasting in the waste management area was performed in [7], where spatially distributed hazardous production data were analysed. In this study, 41 socio-economical predictors were considered.…”

Section: Waste Quantity Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Heuristic Approach to Multivariate Inverse Prediction Problem using Data Reconciliation

Rosecký¹,

Šomplák²,

Janošťák³

et al. 2018

mendel

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Some engineering waste management tasks require a complete data sets of its production. However, these sets are not available in most cases. Whether they are not archiving at all or are unavailable for their sensitivity. This article deals with the issue of incomplete datasets at the microregional level. For estimates, the data from higher territorial units and additional information from the micro-region are used. The techniques used in this estimation are illustrated by an example in the field of waste management. In particular, it is an estimate of the amount of waste in individual municipalities. It is based on recorded waste production at district level and total waste management costs, which is available at a municipal level. To estimate the waste production, combinations of linear regression models with random forest models were used, followed by correction by quadratic and nonlinear optimization models. Such task could be seen as a multivariate version of inverse prediction (or calibration) problem, which is not solvable analytically. To test this approach, data for 2010 - 2015 measured in the Czech Republic were used.

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Section: Waste Quantity Modelsmentioning

confidence: 99%

“…all together with the equations (6), (7),(8),(9) from fixed quantities model. Both mentioned models are the tasks of the quadratic programming.…”

Section: Fixed Coefficients Modelmentioning

confidence: 99%

Heuristic Approach to Multivariate Inverse Prediction Problem using Data Reconciliation

Rosecký¹,

Šomplák²,

Janošťák³

et al. 2018

mendel

View full text Add to dashboard Cite

show abstract

“…Intermittent DPUT a common occurrence for service and commercial companies that supply parts to industry sectors such as aerospace (Ayu Nariswari, Bamford & Dehe, 2019), automotive (Zhang & Xiaofeng, 2017), information technology (Antosz & Ratnayake, 2019), and military (Babai, Syntetos & Teunter, 2014). This particular frequency in demand could impact different company strategies (Pavlas et al, 2017;Devika et al, 2016), such as the use of inventory models (Jonkman, Barbosa-Póvoa & Bloemhof, 2019). Inventory models reduce costs and establish optimal stock levels in order to meet the demand for components and final products for customers (Rojas et al, 2019).…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Managing slow-moving item: a zero-inflated truncated normal approach for modeling demand

Rojas

Coluccio

Vega-Vargas

et al. 2020

PeerJ Computer Science

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This paper proposes a slow-moving management method for a system using of intermittent demand per unit time and lead time demand of items in service enterprise inventory models. Our method uses zero-inflated truncated normal statistical distribution, which makes it possible to model intermittent demand per unit time using mixed statistical distribution. We conducted numerical experiments based on an algorithm used to forecast intermittent demand over fixed lead time to show that our proposed distributions improved the performance of the continuous review inventory model with shortages. We evaluated multi-criteria elements (total cost, fill-rate, shortage of quantity per cycle, and the adequacy of the statistical distribution of the lead time demand) for decision analysis using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We confirmed that our method improved the performance of the inventory model in comparison to other commonly used approaches such as simple exponential smoothing and Croston’s method. We found an interesting association between the intermittency of demand per unit of time, the square root of this same parameter and reorder point decisions, that could be explained using classical multiple linear regression model. We confirmed that the parameter of variability of the zero-inflated truncated normal statistical distribution used to model intermittent demand was positively related to the decision of reorder points. Our study examined a decision analysis using illustrative example. Our suggested approach is original, valuable, and, in the case of slow-moving item management for service companies, allows for the verification of decision-making using multiple criteria.

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“…Intermittent DPUT a common occurrence for service and commercial companies that supply parts to industry sectors such as aerospace (Ayu Nariswari et al, 2019), automotive (ZHANG and Xiaofeng, 2017), information technology (Antosz and Ratnayake, 2019), and military (Babai et al, 2014). This particular frequency in demand could impact different company strategies (Pavlas et al, 2017;Devika et al, 2016), such as the use of inventory models (Jonkman et al, 2019). Inventory models reduce costs and establish optimal stock levels in order to meet the demand for components and final products for customers (Rojas et al, 2019).…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Peer Review #3 of "Managing slow-moving item: a zero-inflated truncated normal approach for modeling demand (v0.1)"

2020

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This paper proposes a slow-moving management method for a system using of intermittent demand per unit time and lead time demand of items in service enterprise inventory models. Our method uses zeroinflated truncated normal statistical distribution, which makes it possible to model intermittent demand per unit time using mixed statistical distribution. We conducted numerical experiments based on an algorithm used to forecast intermittent demand over fixed lead time to show that our proposed distributions improved the performance of the continuous review inventory model with shortages. We evaluated multi-criteria elements (total cost, fill-rate, shortage of quantity per cycle, and the adequacy of the statistical distribution of the lead time demand) for decision analysis using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We confirmed that our method improved the performance of the inventory model in comparison to other commonly used approaches such as simple exponential smoothing and Croston's method. We found an interesting association between the intermittency of demand per unit of time, the square root of this same parameter and reorder point decisions, that could be explained using classical multiple linear regression model. We confirmed that the parameter of variability of the zero-inflated truncated normal statistical distribution used to model intermittent demand was positively related to the decision of reorder points. Our study examined a decision analysis using illustrative example. Our suggested approach is original, valuable, and, in the case of slow-moving item management for service companies, allows for the verification of decision-making using multiple criteria.

show abstract

Spatially distributed production data for supply chain models - Forecasting with hazardous waste

Cited by 25 publications

References 25 publications

Heuristic Approach to Multivariate Inverse Prediction Problem using Data Reconciliation

Heuristic Approach to Multivariate Inverse Prediction Problem using Data Reconciliation

Managing slow-moving item: a zero-inflated truncated normal approach for modeling demand

Peer Review #3 of "Managing slow-moving item: a zero-inflated truncated normal approach for modeling demand (v0.1)"

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