Model-Based Approach for Assessing Planning Quality in Production Logistics

Lucht, Torben; Mütze, Alexander; Kämpfer, Tim; Nyhuis, Peter

doi:10.1109/access.2021.3104717

Cited by 9 publications

(5 citation statements)

References 26 publications

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“…Manufacturing area in recent years is still amongst the most impacted fields by advances of DA and digital transformation, which resulted in huge expansion of data availability (Tamás & Koltai, 2020). Production planning and control are the dominant topics for applying DA in this context (Lucht et al, 2021). The results of the current literature review are in line with the previous one and we also confirmed that most papers in the manufacturing area address production planning and control issues.…”

Section: In What Areas Of Scm Is Da Being Applied?supporting

confidence: 86%

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Darbanian,

Brandtner,

Falatouri

et al. 2024

OSCM: An Int. Journal

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In recent years, there has been a growing surge of interest in the application of data analytics (DA) within the realm of supply chain management (SCM), attracting attention from both practitioners and researchers. This paper presents a comprehensive examination of recent implementations of DA in SCM. Employing a systematic literature review (SLR), we conducted a meticulous analysis of over 354 papers. Building upon a prior SLR conducted in 2018, we identify contemporary areas where DA has been applied across various functions within the supply chain and scrutinize the DA models and techniques that have been employed. A comparison between past findings and the current literature reveals a notable upsurge in the utilization of DA across most SCM functions, with a particular emphasis on the prevalence of predictive analytics models in contemporary SCM applications. The findings of this paper offer a detailed insight into the specific DA models and techniques currently in use across various SCM functions. Additionally, a discernible increase in the adoption of mixed or hybrid DA models is observed. However, several research gaps persist, including the need for more attention to real-time DA in SCM, the integration of publicly available data, and the application of DA to mitigate uncertainty in SCM. To address these areas and guide future research endeavors, the paper concludes by delineating six concrete research directions. These directions offer valuable avenues for further exploration in the field.

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Section: In What Areas Of Scm Is Da Being Applied?supporting

confidence: 86%

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Darbanian,

Brandtner,

Falatouri

et al. 2024

OSCM: An Int. Journal

View full text Add to dashboard Cite

show abstract

“…Planning quality is a commonly used term to indicate how good production planning is [26]. Planning quality can be understood as a key indicator for the planner to assess the reliability of the production plan in the planning phase and to continuously improve the operational reliability of production plans in forthcoming phases [27,28].…”

Section: Planning Qualitymentioning

confidence: 99%

“…The deviations can be caused by uncertainties such as inaccurate or incomplete planning data, unforeseeable external events, or inappropriate PPC systems. Planning quality can also be assessed with regards to planning accuracy and plan stability [26].…”

Section: Planning Qualitymentioning

confidence: 99%

Smart Production Planning and Control; Concept for Improving Planning Quality with Production Feedback Data

Rahmani,

Syversen,

Romsdal

et al. 2023

IFIP Advances in Information and Communication Technology

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Planning quality depends on the use of correct, accurate, realistic, and reliable planning data. Industry 4.0 has facilitated large-scale data collection from a variety of sources, including production feedback data. The hierarchical nature of traditional production planning and control (PPC) limits the ability to use such data to improve planning quality. This paper explores how planning quality can be improved through the application of production feedback data into tactical production planning. The paper shows that while current tactical planning is mainly based on static master data, some of the master data for planning should instead be dynamically determined based on analysis of production feedback data. The paper develops a conceptual model for how production feedback data can be linked to tactical planning, illustrates how production feedback data can be applied in tactical planning, and proposes a method for how companies can integrate production feedback data into their tactical planning. Future work includes application and testing of the proposed concept in real-life cases and studies to better understand the specific relationship between the accuracy of master data and the performance of production plans.

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“…Therefore, it is increasingly difficult to identify and solve the inefficiency or other problems induced by the planning process of the industrial supply chain. Lucht et al [27] proposed a convenient way to describe, visualize, and analyze the scheduling behavior of manufacturing enterprises with very few data, summarized the key evaluation indices of planning quality, and constructed an evaluation model covering two dimensions: planning stability, and planning accuracy.…”

Section: Introductionmentioning

confidence: 99%

Quality Management and Control for the Whole-Process Logistics Service of Multi-Variety Small-Batch Production and Manufacturing

Wang¹

2023

JESA

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Guided by market orientation, the traditional mass production mode has gradually shifted to the multi-variety small-batch production mode, so it's of certain necessity and practical and theoretical value to research the logistics service quality of multi-variety small-batch production. This paper explores the quality management and control for the whole-process logistics service of multi-variety small-batch production (MVSBP) manufacturing. Firstly, the authors analyzed the factors affecting the MVSBP logistics system of production and manufacturing enterprises, and plotted the distribution paths between different functional areas in the production workshop. Then, the simulation steps of the MVSBP logistics system were detailed, and the simulation software was introduced. After that, the three elements of MVSBP logistics, namely, site selection, path design, and warehousing, were optimized, before providing the optimization objective function. Finally, the MVSBP logistics system was simulated on a real case: the production logistics management of a porcelain blank processing workshop. The simulation results demonstrate the effectiveness of our quality management and control strategy for production logistics.

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Model-Based Approach for Assessing Planning Quality in Production Logistics

Abstract: Funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) "SFB 871/3" -119193472 and "Systematic analysis of the effect of production planning and control processes on logistical objectives" -434659386.

Cited by 9 publications

References 26 publications

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Smart Production Planning and Control; Concept for Improving Planning Quality with Production Feedback Data

Quality Management and Control for the Whole-Process Logistics Service of Multi-Variety Small-Batch Production and Manufacturing

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