Investigating the Effectiveness of Variance Reduction Techniques in Manufacturing, Call Center and Cross-Docking Discrete Event Simulation Models

Adewunmi, Adrian; Aickelin, Uwe

doi:10.1007/978-3-642-28777-0_1

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…variation in emission estimates across visits to the same farm. Although increasing the number of visits per farm could be a solution to reduce the within farm variance, the required number of replications (visits) to achieve a desired precision is unknown in advance (Adewunmi and Aickelin 2012). The within farm variance of the estimate for the population mean reduces not only with an increase in visits per farm, but also with an increase in the number of farms visited.…”

Section: Longitudinal Observation For Lcamentioning

confidence: 99%

Understanding variability in greenhouse gas emission estimates of smallholder dairy farms in Indonesia

Apdini

Zahra

Oosting

et al. 2021

Int J Life Cycle Assess

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Purpose Life cycle assessment studies on smallholder farms in tropical regions generally use data that is collected at one moment in time, which could hamper assessment of the exact situation. We assessed seasonal differences in greenhouse gas emissions (GHGEs) from Indonesian dairy farms by means of longitudinal observations and evaluated the implications of number of farm visits on the variance of the estimated GHGE per kg milk (GHGEI) for a single farm, and the population mean. Methods An LCA study was done on 32 smallholder dairy farms in the Lembang district area, West Java, Indonesia. Farm visits (FVs) were performed every 2 months throughout 1 year: FV1–FV3 (rainy season) and FV4–FV6 (dry season). GHGEs were assessed for all processes up to the farm-gate, including upstream processes (production and transportation of feed, fertiliser, fuel and electricity) and on-farm processes (keeping animals, manure management and forage cultivation). We compared means of GHGE per unit of fat-and-protein-corrected milk (FPCM) produced in the rainy and the dry season. We evaluated the implication of number of farm visits on the variance of the estimated GHGEI, and on the variance of GHGE from different processes. Results and discussion GHGEI was higher in the rainy (1.32 kg CO2-eq kg−1 FPCM) than in the dry (0.91 kg CO2-eq kg−1 FPCM) season (P < 0.05). The between farm variance was 0.025 kg CO2-eq kg−1 FPCM in both seasons. The within farm variance in the estimate for the single farm mean decreased from 0.69 (1 visit) to 0.027 (26 visits) kg CO2-eq kg−1 FPCM (rainy season), and from 0.32 to 0.012 kg CO2-eq kg−1 FPCM (dry season). The within farm variance in the estimate for the population mean was 0.02 (rainy) and 0.01 (dry) kg CO2-eq kg−1 FPCM (1 visit), and decreased with an increase in farm visits. Forage cultivation was the main source of between farm variance, enteric fermentation the main source of within farm variance. Conclusions The estimated GHGEI was significantly higher in the rainy than in the dry season. The main contribution to variability in GHGEI is due to variation between observations from visits to the same farm. This source of variability can be reduced by increasing the number of visits per farm. Estimates for variation within and between farms enable a more informed decision about the data collection procedure.

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Section: Longitudinal Observation For Lcamentioning

confidence: 99%

Understanding variability in greenhouse gas emission estimates of smallholder dairy farms in Indonesia

Apdini

Zahra

Oosting

et al. 2021

Int J Life Cycle Assess

View full text Add to dashboard Cite

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“…This indicates that the farms in this study are rather homogeneous in terms of their GHGEI per kg milk, and that the main source of variation in GHGEI relates to the within farm variance, i.e., variation in emission estimates across visits to the same farm. Although increasing the number of visits per farm could be a solution to reduce the within farm variance, the required number of replications (visits) to achieve a desired precision is unknown in advance (Adewunmi and Aickelin 2012). The within farm variance of the estimate for the population mean reduces not only with an increase in visits per farm, but also with an increase in the number of farms visited.…”

Section: Longitudinal Observation For Lcamentioning

confidence: 99%

Improving manure management at smallholder dairy farms in Indonesia: a multi-level analysis

Zahra¹

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the N-P excretion of dairy cattle is first needed. Quantification of N-P is crucial because both managed and unmanaged N-P lead to environmental emissions. Mathematical models are often used to predict N-P excretion of dairy cows. Most N-P excretion models, however, are developed for dairy farming systems in developed countries, and not for smallholder systems in Indonesia. A generic model to predict N-P excretion of dairy cows on smallholder dairy farms is not available.Information on N-P excretion can subsequently be used to estimate the flows and losses of nutrients on smallholder dairy farms. Flows and losses in dairy farming systems, however, may also depend on manure management system (MMS). Different MMSs exist in smallholder dairy farms in Indonesia (de Vries et al., 2017). So far, it is unknown how nutrient flows and losses differ across systems.Furthermore, it is unknown to what extent the dairy sector contributes to the pollution of the Citarum river, being the longest river in the province of West Java (Garg et al., 2018;Yoshida et al., 2017; Kerstens et al., 2013). Though the dairy sector is presumed to contribute to the pollution of this river, its exact contribution is so far unknown.Whereas estimating N-P losses is relevant with regard to local environmental impacts such as eutrophication and contamination of drinking water, climate change happens at a global scale, and quantification of GHGE therefore requires a value chain level approach. So far, most studies that assessed GHGE on smallholder farms in tropical regions used data collected at one particular moment in time (i.e., cross-sectional observation) or used data based on farmers' recall. The climate of Indonesia, however, is characterized by a dry and a rainy season, and dairy farmers adapt their practices to these seasons, mainly with regard to feeding and manure management. Such seasonal differences can be an important source of variability in estimates of the GHGE-intensity (GHGEI)i.e. the GHGE per kg milk. Longitudinal studies could provide insight into the impact of seasonal differences on GHGEI estimates, and into the implications of the number of farm visits on the accuracy of the estimate but are currently not available.In order to reduce GHGE from dairy farms, insight into the impact of mitigation strategies is needed.An important mitigation strategy proposed is increasing milk yield per cow. This strategy is promising General introduction | 13 3 1 because there is a non-linear negative association between milk yield and GHGE per unit of milk output (Gerber et al., 2011). However, this strategy often appears difficult to adopt at smallholder dairy farms because it requires a combination of improving feed supply and quality, improving animal health and cow fertility, and improving genetic potential. Therefore, alternative strategies for mitigating GHGE are required. So far, however, strategies to reduce GHGE from smallholder dairy farms in Indonesia beyond milk yield increases are unexplored.Proper manure management is presently no...

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Impact of task priority on software supply chain: a simulation approach

Kumar

Vaidya

Srivastava

2020

SAJBS

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PurposeThe purpose of this paper is to improve the understanding of the role of the bottlenecks in the dynamic software development supply chains. The paper examines the effects of the task priorities in the software development and investigates the possible strategies to manage them effectively.Design/methodology/approachIn this paper, a software development supply chain has been simulated. This includes modeling of the various sizes of software requirement, different priorities, variations in development times, quality defects, etc. The model assumes a fixed set of resources of various skills. The model is studied for the bottlenecks, throughput, work in progress (WIP), etc. under various work preemption scenarios.FindingsThe results indicate that job priorities impact the bottleneck formulation, throughput and WIP of the software development. The work interruption policies to accommodate priority jobs adversely impact the throughput. Selective introduction of interruptions by leaving the bottlenecks from interruptions helps balancing the throughput and priorities.Research limitations/implicationsThe impact of the learning curve and knowledge acquisition time needed by the resources to restart the interrupted work has not been considered in this paper, which can be a future area of research.Practical implicationsThe paper helps the practicing managers evaluate the dynamics of the bottlenecks with various task management approaches and comprehend the possible tradeoffs between priority and throughout.Originality/valueThe paper looks at software development from a perspective of workflow dynamics. This is a pioneer effort, as it utilizes simulation and modeling approach in understanding the software supply chains better.

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Investigating the Effectiveness of Variance Reduction Techniques in Manufacturing, Call Center and Cross-Docking Discrete Event Simulation Models

Cited by 4 publications

References 32 publications

Understanding variability in greenhouse gas emission estimates of smallholder dairy farms in Indonesia

Understanding variability in greenhouse gas emission estimates of smallholder dairy farms in Indonesia

Improving manure management at smallholder dairy farms in Indonesia: a multi-level analysis

Impact of task priority on software supply chain: a simulation approach

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