Production Planning And Control System Research Articles

A conceptual framework for smart production planning and control in Industry 4.0

This article aims to introduce the challenge (i.e., integration of new collaborative models and tools) posed by the automation and collaboration of industrial processes in Industry 4.0 (I4.0) smart factories. Small- and medium-sized enterprises (SMEs) are particularly confronted with new technological and organisational changes, but a conceptual framework for production planning and control (PPC) systems in the I4.0 context is lacking. The main contributions of this article are to: (i) identify the functions making up traditional PPC and smart production planning and control in I4.0 (SPPC 4.0); (ii) analyse the impact of I4.0 technologies on PPC systems; (iii) propose a conceptual framework that provides the systematic structuring of how a PPC system operates in the I4.0 context, dubbed SPPC 4.0. Thus SPPC 4.0 is proposed by adopting the axes of the RAMI 4.0 reference architecture model, which compiles and contains the main concepts of PPC systems and I4.0. It also provides the technical description, organisation and understanding of each aspect, which can provide a guide for academic research and industrial practitioners to transform PPC systems towards I4.0 implementations. Finally, theoretical implications and research gaps are provided.

Approaches of production planning and control under industry 4.0: A literature review

Purpose: Industry 4.0 technologies influence how production is planned, scheduled, and controlled. In literature, different classifications of the tasks and functions of production planning and control (PPC) exist, of which one is the German Aachen PPC model. This research aims to identify and classify current Industry 4.0 approaches for planning and controlling production processes and to reveal researched and unexplored areas of the model.Design/methodology/approach: In an exploratory literature review, we review and classify 48 publications on a full-text basis with the Aachen PPC model's tasks and functions. Two cluster analyses reveal researched and unexplored tasks and functions of the Aachen PPC model. Additionally, we give a summary of each reviewed publication.Findings: We propose a cyber-physical PPC architecture, which incorporates current Industry 4.0 technologies, current optimization methods, optimization objectives, and disturbances relevant for realizing a PPC system in a smart factory. Current approaches focus on the in-house PPC, particularly on the control using real-time information from the shop floor. We propose future research directions for the unexplored tasks and functions of the Aachen PPC model.Research limitations/implications: The selection of search terms and the texts' interpretation is based on an individual assessment. The revelation of unexplored tasks and functions of the Aachen PPC model might have a different outcome if the search term combination is parameterized differently.Originality/value: Using the Aachen PPC model, which holistically models PPC, the findings give comprehensive insights into the current advances of tools, methods, and challenges relevant to planning and controlling production processes under Industry 4.0.

MODEL PERENCANAAN DAN PENGENDALIAN PRODUKSI DI INDUSTRI PENGOLAHAN BUAH CARICA (CARICA PUBESCENS)

The competition in the carica fruit processing industry is increasing, so that efforts are needed to increase production efficiency through good production planning and control (PPC). CV XYZ is one of the carica fruit processing companies which has PPC problems. PPC activities done intuitively and not systematically causing a decision in PPC is ineffective and production process is inefficient. Those problems can be resolved by developing a PPC model in accordance with CV XYZ’s business process. So, this research objective was to develop a PPC model at CV XYZ. The model consisted of four sub-models which were related to each other. The first sub-model was Sub-model of Demand Forecasting and the second sub-model was Sub-model of Carica Fruit Availability Forecasting. Those two sub-models used the Auto-regressive Integrated Moving Average (ARIMA). The third sub-model was Sub-model of Production and Packaging Planning using Integer Linear Programming (ILP). The fourth sub-model was Sub-model of Carica Fruit Inventory Control using Material Requirement Planning (MRP). PPC model can clarify the PPC system at CV XYZ and produce optimal plans. PPC model improves efficiency of total production cost up to 39.86% and total inventory cost up to 27.56%. Keywords: carica industry, model, optimal, production planning and control

Production Planning and Control for Sustainable Management Systems

Optimization models applied in a production system are significantly affected by particular objectives and constraints that reflect business processes and rules. This paper begins to develop a close-loop knowledge sharing business model to add main sustainability indicators to sustainable management systems. Although there are much work on production planning and control (PPC) problems, few of them review from functional perspectives. Second, we reveal that the shortage of sustainability considerations complicate rapid responses to changes. Developing feedback loops in multi-stage PPC system, including updates in production schedules and the shortages of machines and materials, can benefit companies.

Order release in production planning and control systems: challenges and opportunities

Production planning and control (PPC) systems are important to the competitiveness of manufacturing firms and their ability to benefit from recent technological advances. We give an overview of research and unsolved research questions in PPC systems related to the order release function that sets output targets for autonomous production units that manage production at the shop-floor level. We describe the hierarchical Manufacturing Planning and Control (MPC) and Advanced Planning and Scheduling (APS) architectures for PPC systems prevalent in practice and the positioning of the order release task within these frameworks. We then describe the research streams relevant to order release, optimisation models for order release planning, and the relation of these models to the overall PPC system. Modelling the dynamic response of production units to time-varying work input, which represents an extension of classical production theory by incorporating the time dimension, is identified as the major modelling challenge. We conclude by suggesting several unsolved research questions that should encourage researchers to work on this topic which is far from mature.

Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study

In furtherance of emerging research within smart production planning and control (PPC), this paper prescribes a methodology for the design and development of a smart PPC system. A smart PPC system uses emerging technologies such as the internet of things, big-data analytics tools and machine learning running on the cloud or on edge devices to enhance performance of PPC processes. It achieves this by using a wider range of data sources from the production system, capturing and utilizing the experience of production planners, using analytics and machine learning to harness insights from the data and allowing dynamic and near real-time action to the continuously changing production system. The proposed methodology is illustrated with a case study in a sweets and snacks manufacturing company, to highlight the key considerations and challenges production managers might face during its application. The case further demonstrates considerations for scalability and flexibility via a loosely coupled, service-oriented architecture and the selection of fitting algorithms respectively to address a business requirement for a short-term, multi-criteria and event-driven production planning and control solution. Finally, the paper further discusses the challenges of PPC in smart manufacturing and the importance of fitting smart technologies to planning environment characteristics.

Identification of a novel architecture for production planning and control in consideration of biomimetic algorithms

Despite great progress in the academic study of production planning and control (PPC), current PPC systems still fail to fully meet industry’s needs. The approach presented here aims at a more functional and user-friendly PPC by conceptualising a novel idea that leverages the potentials of biomimetic algorithms. The proposed architecture consists of four core modules: order release module, digital shadow, event detection and processing, and decision-making module. Their interactions provide the functionality of the system while emphasising the workers’ key role in the socio-technical PPC system. Requirements for the implementation of the architecture as well as starting points for future research are identified along with the concept.

A literature review and cluster analysis of the Aachen production planning and control model under Industry 4.0

Industry 4.0 technologies influence how production is planned, scheduled, and controlled. In literature, different classifications of the tasks and functions of production planning and control (PPC) exist, of which one is the German Aachen PPC model. This paper conducts an exploratory literature review by reviewing 48 publications on a full-text basis. Based on the review, a cyber-physical PPC architecture is proposed, which incorporates current Industry 4.0 technologies, current optimisation methods, optimisation objectives, and disturbances, relevant for the realisation of a PPC system in a smart factory. A classification scheme is developed as a basis for two cluster analyses that reveal researched and unexplored tasks and functions of the Aachen PPC model. Current approaches focus on the in-house PPC, particularly on the control using real-time information from the shop floor. Future research directions are proposed for the unexplored tasks and functions of the Aachen PPC model.

Remanufacturing of electric vehicles: Challenges in production planning and control

Abstract Due to the conceptual degrees of freedom in their product structure, electric vehicles offer high potential for remanufacturing-oriented product design. Remanufacturing as one fundamental element of a circular economy is characterized by specific challenges caused by uncertain information about the condition of the returning product. By means of a standardized survey within the remanufacturing industry, the occurrence of uncertainties is confirmed and put into relation with challenges in the production planning and control (PPC) of remanufacturing operations. Furthermore, it is shown that strategies to deal with uncertainties currently used cause major inefficiencies that hinder economic remanufacturing of complex products like electric vehicles. Based on the result of the survey the requirements for a remanufacturing specific PPC system are derived from a practitioner’s point of view.

Successful implementation of an order release mechanism based on workload control: a case study of a make-to-stock manufacturer

This paper deals with improving the lead-time performance of a large crystal manufacturer that uses a state-of-the-art commercial Enterprise Resource Planning system. Since the company encountered some limitations of the standard production planning and control (PPC) system it sought for improvements by implementing an order release mechanism based on workload control (WLC). WLC employs certain rules for releasing orders in order to maintain a certain level of work in process to achieve a certain utilisation of the production system and thus control the flow times in order to meet the required due dates of the orders. We describe the successful implementation of an order release mechanism based on the WLC concept in this make-to-stock company. The paper describes the implemented order release mechanism, the implementation process and its impact on the company’s performance. We show that the core function of WLC – the order release mechanism – can be integrated successfully into an existing PPC system. Furthermore, this study highlights the applicability of WLC to a wider range of companies, especially to make-to-stock manufacturers.

Real-time production planning and control system for job-shop manufacturing: A system dynamics analysis

Much attention has been paid to production planning and control (PPC) in job-shop manufacturing systems. However, there is a remaining gap between theory and practice, in the ability of PPC systems to capture the dynamic disturbances in manufacturing process. Since most job-shop manufacturing systems operate in a stochastic environment, the need for sound PPC systems has emerged, to identify the discrepancy between planned and actual activities in real-time and also to provide corrective measures. By integrating production ordering and batch sizing control mechanisms into a dynamic model, we propose a comprehensive real-time PPC system for arbitrary capacitated job-shop manufacturing. We adopt a system dynamics (SD) approach which is proved to be appropriate for studying the dynamic behavior of complex manufacturing systems. We study the system’s response, under different arrival patterns for customer orders and the existence of various types real-time events related to customer orders and machine failures. We determine the near-optimal values of control variables, which improve the shop performance in terms of average backlogged orders, work in process inventories and tardy jobs. The results of extensive numerical investigation are statistically examined by using analysis of variance (ANOVA). The examination reveals an insensitivity of near-optimal values to real-time events and to arrival pattern and variability of customer orders. In addition, it reveals a positive impact of the proposed real-time PPC system on the shop performance. The efficiency of PPC system is further examined by implementing data from a real-world manufacturer.

PRODUCTION PLANNING AND CONTROL IN BIO-INSPIRED HOLONIC MANUFACTURING EXECUTION SYSTEMS

In manufacturing systems, production planning and control performs a key function. This paper discusses the production planning and control (PPC) aspects of a Bio-Inspired Holonic Manufacturing Execution Systems. The PPC system in such a bio-inspired system has a design that distinguishes it from more conventional systems. This design renders the PPC system robust and responsive when facing changes and disturbances that occur on a shop floor.

Distributed production planning and control agent-based system

A model of an Agent-based Production Planning and Control (PPC) system that can be dynamically adaptable to local and distributed utilization of production resources and materials is presented. The PPC system is based on the selection of resources to deal with one order of different quantities of one product each time. In this way, one scheduling solution is provided for that particular order. The production resources are selected and scheduled using a multi-agent system supported by an implementation of the Smith Contract Net, using Java Spaces technology. The multi-agent system is based on three main agents: Client, Resource, and Manager. These agents negotiate the final product, and the correspondent components, requested by the client. An order for each product (component) triggers a process of dynamic design of a production system to fulfil that particular order. This system exists until the end of the order.

Stumbling blocks of PPC: Towards the holistic configuration of PPC systems

Manufacturing companies often complain about the difficulties they face in meeting their customers’ logistic requirements. Many blame the perceived inadequacies of their production planning and control (PPC) software for their performance deficits. The paper illustrates why this is only a partial view of the causes of the shortcomings. PPC software is just one of six configuration aspects of the entire PPC system. The authors argue that the configuration of the PPC aspects objectives, processes, objects, functions, responsibilities and tools has to be carried out methodically and consistently in order for the PPC system to function properly. The analysis of examples of so-called ‘stumbling blocks’ of PPC, inadequate configurations of one or several of the aspects, supports this claim. The paper closes with the proposal of a checklist that the authors suggest as a first approach to ensure the consistent configuration of PPC systems.

Decentralized system for production planning and control of soft drink production using generalized nets

A Production Planning and Control (PPC) system for soft drink production is developed as a distributed system to cope with the uncertain economic environment, diversified customers needs and plant disturbances. The paper describes a novel approach for design a small intelligent PPC system based on holonic concepts and using the methodology of Generalized Nets (GNs). The proposed approach is very suitable for planning and control of hybrid production systems and is applied using the software tool “GNET” with DDE connection to the MS - Office.

A multi-dimensional classification of production systems for the design and selection of production planning and control systems

A primary requirement for improved understanding of the management of production systems is an appropriate classification of such systems. This paper proposes a classification that facilitates a better understanding of real production systems. It combines all the essential features, e.g. the flow of materials with new classification perspectives with respect to response time, repetitiveness and work organization. As much previous work has influenced the approach proposed here, the paper also presents a review of relevant literature. The classification has four groups of characteristics, comprising eight dimensions of descriptors, encompassing 12 variables. Choosing or designing an appropriate production planning and control system (PPCS) is a difficult task due to the integrative character of the production planning and control area: it tends to interface with all functional areas of the enterprise. An important objective of the classification system proposed here is to provide a tool to assist in undertaking this difficult task. Real production systems are becoming more hybrid in order to be able to cope with change. We show that our classification can successfully deal with such systems.

Selection and implementation of a PPC system

There are many computer-aided production planning and control (PPC) systems available on the market which can provide a solution to the complex task of production planning and control. However, the question remains, how can a company find an optimal system from the vast amount of available systems? This article proposes that a company, having decided to buy one of the available systems, starts a project for selection and implementation of the PPC system.

Flexible assembly systems: The scheduling problem for multiple products

Abstract Flexible assembly systems (FAS) promise to be an efficient processing approach for producing small and medium-sized batches of products. One vitally important element which affects the achievement of high performance by the FAS is its scheduling/dispatching component operating within the production planning and control system (PPCS). A detailed definition of the FAS scheduling process is given and key differences between this process and the classical simple job shop scheduling process are discussed. A mathematical model is developed for the minimize makespan problem, and it is shown that the problem is NP-complete, that it is one the most difficult problems in the class of decision problems that can be solved by polynomial time nondeterministic algorithm (NP). The design of heuristic solution procedures is also discussed.