A state-of-the-art on production planning in Industry 4.0

Luo, Dan; Thevenin, Simon; Dolgui, Alexandre

doi:10.1080/00207543.2022.2122622

Cited by 39 publications

(5 citation statements)

References 186 publications

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“…However, the main challenges of state-of-the-art technologies in production planning remain, such as the integration of data, software, and decisions; massive data to develop an effective production plan using cutting-edge technologies; and dynamic and automatic management support for production planning [51]. The challenge is to develop tools that can react in real time and interact properly with the shop floor managers and the workers.…”

Section: Industry 50 Elements In Production Planningmentioning

confidence: 99%

Integrated Multilevel Production Planning Solution According to Industry 5.0 Principles

Trstenjak,

Gregurić,

Janić

et al. 2023

Applied Sciences

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This paper presents the development and implementation of Integrated Multilevel Planning Solution (IMPS) a solution adhering to Industry 4.0 and 5.0 standards. Today, companies face challenges in understanding how new orders would impact existing production plans when there is limited traceability and information flow in their manufacturing process. The digital transformation of the production planning system enables a company to overcome the current challenges; however, to overcome the usual barriers of digital transformation a specialized solution for each company should be developed. IMPS was developed by first understanding the problems in the existing production planning process through a gemba (jap. for “actual place”) walk and interviews with stakeholders. The solution was designed with a human-centric approach and consists of seven components (Design System App (DSA), SAP (Systems Applications and Products in Data Processing), Microsoft Project, Microsoft Project Server, The Project Group (TPG) PSLink software, TPG ProjectLink, Tableau, and Smart Digital Assistance), which are well connected and integrated into the existing design. The system is accessible to the end user to find information, as the principles of Industry 5.0 require. A multivariant and multiuser planning capability was achieved with an interconnected Gantt chart of the master project with the ability to drill down into individual projects and custom views for various types of internal users. Most of the production planning solutions found in the literature were optimization-oriented, related to the improvements of the calculation methods within the planning activities in order to achieve a better efficiency of the planning system. Here, the goal was to achieve a system architecture that enabled a unique solution for design-to-order manufacturing without complex interventions into the existing system, which overcomes the most common barriers in Industry 4.0 implementations which are the human resistance to change, high investments, a lack of needed skills and knowledge for its implementation and use, and challenges of the adaptability to the new system. IMPS (ver 1.0) is a hybrid solution for SMEs, which aims to advance their planning system from the most commonly used Excel sheets towards a more advanced system but has financial and knowledge limitations from its implementation of highly complex software (ver. 1.0).

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Section: Industry 50 Elements In Production Planningmentioning

confidence: 99%

Integrated Multilevel Production Planning Solution According to Industry 5.0 Principles

Trstenjak,

Gregurić,

Janić

et al. 2023

Applied Sciences

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“…This plan delineates the optimal quantities and the synchronized timing of subassemblies, essential parts, and raw materials, essential to the assembly of the final product. According to Stevenson [8] and Lou et al [9], the MRP includes three principal inputs. First, the BOM (Bill of Materials) is a guide detailing the composition of the final product [10].…”

Section: Fundamentals Of Materials Requirement Planningmentioning

confidence: 99%

“…The MRP processing is a part of the PPC that ensures timely and efficient order fulfillment while minimizing resource wastage and delays [9]. Python can be used to automate and optimize orders to create material timetables by accounting for lead times in procurement, calculating reorder points, and triggering timely alerts.…”

Section: Mrp Processingmentioning

confidence: 99%

Exploring Applications and Practical Examples by Streamlining Material Requirements Planning (MRP) with Python

Reis

2023

Logistics

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Background: Material Requirements Planning (MRP) is critical in Supply Chain Management (SCM), facilitating effective inventory management and meeting production demands in the manufacturing sector. Despite the potential benefits of automating the MRP tasks to meet the demand for expedited and efficient management, the field appears to be lagging behind in harnessing the advancements offered by Artificial Intelligence (AI) and sophisticated programming languages. Consequently, this study aims to address this gap by exploring the applications of Python in simplifying the MRP processes. Methods: This article offers a twofold approach: firstly, it conducts research to uncover the potential applications of the Python code in streamlining the MRP operations, and the practical examples serve as evidence of Python’s efficacy in simplifying the MRP tasks; secondly, this article introduces a conceptual framework that showcases the Python ecosystem, highlighting libraries and structures that enable efficient data manipulation, analysis, and optimization techniques. Results: This study presents a versatile framework that integrates a variety of Python tools, including but not limited to Pandas, Matplotlib, and Plotly, to streamline and actualize an 8-step MRP process. Additionally, it offers preliminary insights into the integration of the Python-based MRP solution (MRP.py) with Enterprise Resource Planning (ERP) systems. Conclusions: While the article focuses on demonstrating the practicality of Python in MRP, future endeavors will entail empirically integrating MRP.py with the ERP systems in small- and medium-sized companies. This integration will establish real-time data synchronization between the Python and ERP systems, leading to accurate MRP calculations and enhanced decision-making processes.

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“…However, flexibility in the manufacturing environment can lead to increased complexity, posing challenges for production managers when making decisions [5,6]. Industry 4.0 has led to the emergence and evolution of various technologies that support decision-making in manufacturing systems [7,8]. This enables continuous iteration and upgrading of both hardware and software in manufacturing systems.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Search Algorithm for Multiplicity Dynamic Flexible Job Shop Scheduling with New Order Arrivals

Ding,

Guan,

Luo

et al. 2024

Symmetry

Self Cite

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In today’s customer-centric economy, the demand for personalized products has compelled corporations to develop manufacturing processes that are more flexible, efficient, and cost-effective. Flexible job shops offer organizations the agility and cost-efficiency that traditional manufacturing processes lack. However, the dynamics of modern manufacturing, including machine breakdown and new order arrivals, introduce unpredictability and complexity. This study investigates the multiplicity dynamic flexible job shop scheduling problem (MDFJSP) with new order arrivals. To address this problem, we incorporate the fluid model to propose a fluid randomized adaptive search (FRAS) algorithm, comprising a construction phase and a local search phase. Firstly, in the construction phase, a fluid construction heuristic with an online fluid dynamic tracking policy generates high-quality initial solutions. Secondly, in the local search phase, we employ an improved tabu search procedure to enhance search efficiency in the solution space, incorporating symmetry considerations. The results of the numerical experiments demonstrate the superior effectiveness of the FRAS algorithm in solving the MDFJSP when compared to other algorithms. Specifically, the proposed algorithm demonstrates a superior quality of solution relative to existing algorithms, with an average improvement of 29.90%; and exhibits an acceleration in solution speed, with an average increase of 1.95%.

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A state-of-the-art on production planning in Industry 4.0

Cited by 39 publications

References 186 publications

Integrated Multilevel Production Planning Solution According to Industry 5.0 Principles

Integrated Multilevel Production Planning Solution According to Industry 5.0 Principles

Exploring Applications and Practical Examples by Streamlining Material Requirements Planning (MRP) with Python

An Adaptive Search Algorithm for Multiplicity Dynamic Flexible Job Shop Scheduling with New Order Arrivals

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