Integrating Energy Flexibility in Production Planning and Control - An Energy Flexibility Data Model-Based Approach

Although the production planning and control (PPC) area is not recent and a large number of research articles have been written on the subject, there is no agreement regarding who the PPC top authors are. The main goal of this article is to identify the top PPC authors by means of a survey on PPC researchers. The 10 top PPC authors were identified, as well as their influence (direct, indirect, or concerning some specific PPC problem) and impact (in theory or in an industry; PPC teaching or research) on the PPC area. The main contributions of this article are as follows: (1) to identify (among a great number of books regarding PPC) the books that have more influence on the PPC area; (2) to guide the study of PPC by means of presenting the main books and authors of PPC; (3) to show the most valuable work of the PPC top authors, initiating a process of determining who the PPC gurus are.

O planejamento e controle da produção (PCP), apesar de ser uma área antiga e possuir uma enorme quantidade de pesquisas já desenvolvidas, ainda não tem um consenso sobre quem são os seus principais autores. O objetivo deste trabalho é identificar os principais autores da área PCP por meio de um survey mundial com pesquisadores da área. Foram identificados os dez principais autores na opinião dos pesquisadores entrevistados, bem como a influência (direta ou indireta ou sobre algum problema específico do PCP) e o impacto (na indústria ou na teoria e na pesquisa ou no ensino do PCP) dos principais autores. As principais contribuições deste artigo são: i) detectar, dentre uma infinidade de obras relacionadas ao PCP, quais são as de maior impacto para a área; ii) direcionar os estudos daqueles que têm interesse em aprofundar seus conhecimentos na área de PCP por meio da apresentação dos principais livros e autores da área; e iii) valorizar os principais autores da área e dar o primeiro passo na determinação dos grandes mentores ("gurus") do PCP.

The influence of human actors on production planning and control (PPC) systems is significant. This paper describes a number of ways, in which human interaction with PPC systems affects the logistic performance of production. It demonstrates how human decisions and behaviour can act as stumbling blocks for PPC. Logistic models and PPC procedures that remove these stumbling blocks are presented. Moreover, the paper proposes the concept of 3-Sigma PPC as a holistic approach to PPC. 3-Sigma PPC recognises the influence of human factors on PPC and incorporates methods that improve human decision-making so that a better logistic performance can be achieved.Key wordsProduction planning and controlDisturbancesHuman factors3-Sigma PPC

Production planning and control (PPC) is the heart of any manufacturing company and entails tasks such as resource planning, sequencing, or capacity control. While an increasing complexity within production makes it difficult to determine the best production plan, the advances in PHM and the emergence of predictive maintenance also offer new opportunities to optimize PPC. While there is much research on PHM and PPC, little has been done to align both disciplines. Through post-prognostics decision-making, different PPC decisions, such as continuing the production, shutting down a machine, or reducing its workload, can be elevated by a remaining useful life (RUL) estimation. However, it is unclear how exactly this prognostics information can be exploited and how processes, organization, and technology must be aligned to attain a more efficient and flexible production. Further, PHM has long been implemented beyond research, but it is unknown whether and how practitioners intertwine it with their PPC. This work aims to analyze how processual, organizational, and technological changes through PHM can lead to advanced PPC. This goal is attained by means of a multivocal literature review (MLR) in which scientific PPC and PHM literature and standards are analyzed, and an aligned PPC process proposed. The findings are juxtaposed with grey literature, revealing fits and gaps between research and practice, and a research agenda is presented.

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

Demand planning strategies for the control of energy flexible components of machine tools

ABSTRACTThe manufacturing sector as a whole has undergone remarkable changes in terms of scale, complexity and technology over the past decades and this applies across most modern high-technology manufacturing such as electronics, semiconductor, aerospace and automotive industries. In order to remain competitive, manufacturers have to produce high-quality products at low cost, and at the same time retain sufficient flexibility and to meet rapidly changing customer demands. Production planning and control (PPC) is a key role which enables the manufacturer to gain visibility and control over all aspects of manufacturing activities. PPC in itself forms a subject of study, within which simulation techniques have proven themselves to be one of the most practical methodologies available to investigate and evaluate manufacturing issues. In this review paper, we focus on state-of-the art applications of simulation techniques in PPC to demonstrate their applicability to modern manufacturing issues. The review reports on academic publications on simulation applications in manufacturing from 2002 to 2014, incorporating surveys of peer-reviewed literature. The review covers three types of simulation techniques (system dynamic, discrete event simulation and agent-based simulation) and eight PPC issues (facility resource planning, capacity planning, job planning, process planning, scheduling, inventory management, production and process design, purchase and supply management). Literature survey is analysed on the basis of simulation application to PPC problems which can give a guideline for simulation technique selection and also can help for simulation modelling in PPC problemsWould you consider changing the term “modeling” to “modelling” in the title. Please check, and correct if necessary.

Via production planning and control (PPC), eco-efficient practices can be added to conventional tools/activities for environmental and economic gains. In this study, we examine the scientific research focussing on the relationship between PPC and eco-efficient practices to determine how PPC tools/activities improve eco-efficiency using bibliometrics and a systematic literature review. Furthermore, the relationship between conventional PPC tools/activities and eco-efficient practices is analysed using UCINet-Draw. The results demonstrate the theoretical and practical contributions (such as the evolution of the use of PPC in conjunction with eco-efficiency in industries) and drivers for future research. We conclude that PPC improves operational eco-efficiency because it contributes to remanufacture, recycle, and reuse of materials, utilisation of renewable materials, reduction in wastes, and minimisation of energy and water consumption.

Production planning and control in a virtual One-of-a-Kind Production company

PurposeThe aim of this paper is to identify some specificities of production planning and control (PPC) activities in the one-of-a-kind-production (OKP) process through an extensive literature review. Relevant aspects related to systems and PPC activities in the context of OKP environment are discussed, and six opportunities for future research are highlighted.Design/methodology/approachThe following research is based on a review of 53 articles published in peer-reviewed journals over the past three decades. After an initial descriptive analysis based on bibliometric indicators, a cluster analysis of 15 most cited articles was carried out using multivariate data analysis techniques and in-depth analysis.FindingsThe results reveal some specificities inherent to the clusters featured in the research, including aspects of planning, control and systems for OKP process. This cluster addresses information regarding next-generation manufacturing systems, scheduling and design science, computer simulation and project approach. On the other hand, the authors point out six topics for future research regarding contemporary issues associated with PPC in the context of OKP.Originality/valueThis paper fills an important gap regarding OKP production planning and control practices. The results provide a theoretical overview of different PPC practices suitable for the OKP environment. Furthermore, it can provide insights for scientific developments in order to manage the complexity inherent in the OKP process.

Production planning and control (PPC) is the mainstay of every manufacturer and ensures flawless production processes. However, PPC is jeopardized by breakdowns that can only be tackled with appropriate maintenance. In the past, static strategies, such as reactive and scheduled maintenance, have been used. Yet, with growing system complexity, Industry 4.0, and abundant sensor data, dynamic strategies through PHM have emerged. The most advanced maintenance strategy is prescriptive maintenance (PxM), which allows manufacturers not only to predict failures but also to establish condition-based production plans and controls. To this end, our study explores the integration of PxM with PPC. First, we propose a fault prediction model based on health indicators and future loads of a single-machine system. The proposed fault prediction is integrated into a joint PxM and PPC simulation model that compares the make­span of three joint PxM and PPC strategies inter se and versus reactive and scheduled maintenance. A simulation study using industrial data from an extrusion process evaluates the different strategies across different time horizons (one month to a year). The findings indicate that joint PxM and PPC outperform other strategies, providing significant time savings over traditional methods. Further, a sensitivity analysis is conducted to assess the robustness of the PxM strategies under varying levels of measurement noise, revealing potential challenges under high noise conditions. The study contributes to the field of PHM by providing insights into the effectiveness of joint PxM and PPC strategies and offering a comprehensive analysis of their performance under different conditions.

RETRACTED: Sustainable industries: Production planning and control as an ally to implement strategy

This paper presents a rule-based expert system that can be used for the selection of a suitable production planning and control (PPC) software package to be applied in a manufacturing firm. A production system's typology and a compact PPC software reference model are included in the knowledge base which has been created. The inferences made are based on rules that relate a semantic network of PPC software features with a semantic network of production systems' attributes. The results given by the expert system include the module architecture and the set of features of the PPC software package, which are applicable in a certain manufacturing setting.

At present, it is still impossible to accurately solve realistic problems of production planning and control (PPC) in an acceptable period of time. Therefore numerous heuristic procedures were developed to solve problems of PPC. The efficiency of these procedures is usually appraised by means of statistical analyses. The data necessary for the analyses must meet high requirements to obtain significant and generalizable results from the statistical analyses. A procedure for the generation of test data is presented in the article. The procedure is based on given characteristics. It stochastically produces PPC data of arbitrary complexity. The procedure for the generation of test data is based on a hierarchical concept. The user enters values of aggregated classification numbers which globally characterize the modeled production system and the amount of released production orders. The values of detailed classification numbers are stochastically generated from the aggregated classification numbers. These values form the input for the generation of the PPC data wanted. This generation is stochastically, too. The user may modify both classification numbers and PPC data in order to generate test data equivalent to known practical data or to acquire test data with slightly changing characteristics for analyses of parameter sensitivity.

Exploring Implementation Barriers of Machine Learning in Production Planning and Control