Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

Serrano-Ruiz, Julio C.; Mula, Josefa

doi:10.3390/computers10120156

Cited by 21 publications

(6 citation statements)

References 90 publications

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“…Fluctuations in demand can affect stock conditions, so companies must schedule optimal production to avoid stock buildup [39], [40]. The Master Production Schedule (MPS) consists of managing Gross Requirements Customer Orders, Project on Hand (POH), Production Schedule and Production Receipt with the following formulation: [41], [42] POH = Stock t-1 + JIP Receipt -Max [Gross Requirement; Customer Order]…”

Section: Master Production Schedulementioning

confidence: 99%

Production Scheduling Based on Smart Forecasting Model of Bottled Mineral Water Products

Husen Santosa,

Prayudha Hidayat,

Siskandar

et al. 2023

E3S Web Conf.

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Optimal production planning is a problem that causes product stock buildup at PT Marina. Factors that affect production planning in the company are the conditions of Demand, Safety Stock, and Production Costs. The results of the demand forecasting method chosen are the Moving Average with N = 3 and the Mean Absolute Percentage Error (MAPE) = 0.05 is 68,084 boxes/period with a production cost of IDR 544,672.00/period. The Safety Stock of bottle products in the ninth forecasting period is 8076 Boxes. Based on the three factors above, an intelligent production planning model was developed using a fuzzy logic approach. The result of defuzzification of demand planning for bottle products for the ninth or three months using the Center of Area (COA) method was 59,917 Boxes. Based on the defuzzification of production planning, the total cost of aggregate production planning for the next three months using the chase strategy method is 617,235,300. 1500 ml = 17,105 boxes. Based on the aggregate planning model above, the company can schedule production and raw materials so that the warehouse’s product stock management is maintained optimally.

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Section: Master Production Schedulementioning

confidence: 99%

Production Scheduling Based on Smart Forecasting Model of Bottled Mineral Water Products

Husen Santosa,

Prayudha Hidayat,

Siskandar

et al. 2023

E3S Web Conf.

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“…The integration of DTs helps to manage disruption risks and fosters SC resilience [7,24]. DTs enhance resilience by improving asset management, maintenance, and mitigating equipment failures and production disruptions.…”

Section: Performance Objectives Of Dt-enabled Manufacturing and Sc Sy...mentioning

confidence: 99%

Expanding the Scope of Manufacturing Digital Twins to Supply Chain

Luo,

Ball

2023

Advances in Transdisciplinary Engineering

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The rapid emergence of digital technologies is revolutionising the manufacturing industry, with digital twins (DTs) emerging as a transformative innovation. DTs, virtual counterparts of physical entities or systems, hold immense potential for optimizing and overseeing manufacturing processes. By proposing a three-dimensional classification framework that considers DT integration levels, supply chain (SC) structural hierarchies, and SC processes, this study examines the evolution of manufacturing DTs to encompass the SC. An analysis of existing literature reveals a significant gap in extending manufacturing DTs to the SC system level without incorporating structural and process aspects of DT models. The paper also investigates the key performance objectives, including efficiency, resilience, and sustainability, as well as the challenges associated with DT implementation. The insights provided in this paper contribute to a better understanding and practical application of DTs in the dynamic contexts of manufacturing and the SC.

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“…Alemany et al [21] presented a decision support tool based on a mathematical model to calculate the amount available to promise. Serrano-Ruiz et al [22] proposed a conceptual framework of the MPS 4.0 based on digital twins and machine learning for zero-defect management in the supply chain. Despite these recent advances, the whole MPS process itself has still not been studied to take advantage of all technologies now available in the era of the fourth industrial revolution.…”

Section: Introductionmentioning

confidence: 99%

Modeling of the Master Production Schedule for the Digital Transition of Manufacturing SMEs in the Context of Industry 4.0

Tobon-Valencia¹,

Lamouri

Pellerin

et al. 2022

Sustainability

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The purpose of this article is to propose a guide for the digital transformation (4.0) of a manufacturing SME’s medium-term production planning process, the master production schedule (MPS). A model of the current MPS process of a group of SMEs is presented as a starting point toward digitization. The current state of this process reveals a lack of tools to support decision making and the need to increase the reliability of input data and to make the process more agile. Industry 4.0 technologies and process modeling could increase agility in the planning process. However, the digital transformation of medium-term planning activities in SMEs has not been studied. To fill this gap, a group of six experts was consulted. The novelty of this study was to identify the Industry 4.0 technologies that could improve medium-term planning and integrate them into a standardized MPS process model. This model is an ultimate point of digitization that cannot be achieved immediately by any SME, but only after several cycles of planning, deployment, and improvement. Therefore, this research also provides a method to help SMEs determine how to start the digitization of their MPS process.

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Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

Cited by 21 publications

References 90 publications

Production Scheduling Based on Smart Forecasting Model of Bottled Mineral Water Products

Production Scheduling Based on Smart Forecasting Model of Bottled Mineral Water Products

Expanding the Scope of Manufacturing Digital Twins to Supply Chain

Modeling of the Master Production Schedule for the Digital Transition of Manufacturing SMEs in the Context of Industry 4.0

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