Production Planning and Control for Sustainable Management Systems

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.

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.

This study aims to provide a profound picture of where existing production planning and control (PPC) concepts fail in small to medium sized make-to-order (MTO) companies. While several studies have discussed the specific requirements of small and medium sized enterprises (SMEs) through surveys or observations in order to determine the suitability of existing PPC concepts, in general, in-depth empirical evidence is rare. In this article, seven case study companies are studied with the backbone of the analysis based on order progress data collected from each company. In each case, logistic performance problems are determined and traced back to their root causes. To distinguish between common problems and company specific elements, a comparative analysis across the cases has been performed. The analysis shows that a significant proportion of common PPC problems can be attributed to a limited set of decision points in the flow of orders. Furthermore, we find that most performance losses can actually be predicted before an order is released to the shop floor. Decisions after release are mainly made in order to correct for the delays caused before release. In the pre-production phase, before release, two common problem areas are identified: (1) inadequate capacity planning overviews to support sales decisions, and (2) uncontrolled delays in engineering. Observed problems that appear after release tend to be more diverse and company-specific. A review of available PPC concepts for MTO SMEs indicates that these areas have been neglected in the literature to date, leading to our call for a new focus in PPC research.

The objective of this research is to analyze the Production Planning and Control (PPC) as a dissemination tool of the operation strategy in industries that compete in environmental sustainability. We used a qualitative methodology supported by cases studies, based on interviews with managers responsible for the production planning and control of four big Brazilian industries. The results indicated that PPC can be an efficient tool to dissiminate the operation strategy for its power of integration with the many key areas involved in an industry, what can bring pratical implications to the ones responsible for the formulation and implementation of the operation strategy. The result is of value because PPC can be used as an ally of the operation/production managers to promote and lead the implementation of the operation strategy on the shop floor.

The objective of this paper is to examine the use and applications of reinforcement learning (RL) techniques in the production planning and control (PPC) field addressing the following PPC areas: facility resource planning, capacity planning, purchase and supply management, production scheduling and inventory management. The main RL characteristics, such as method, context, states, actions, reward and highlights, were analysed. The considered number of agents, applications and RL software tools, specifically, programming language, platforms, application programming interfaces and RL frameworks, among others, were identified, and 181 articles were sreviewed. The results showed that RL was applied mainly to production scheduling problems, followed by purchase and supply management. The most revised RL algorithms were model-free and single-agent and were applied to simplified PPC environments. Nevertheless, their results seem to be promising compared to traditional mathematical programming and heuristics/metaheuristics solution methods, and even more so when they incorporate uncertainty or non-linear properties. Finally, RL value-based approaches are the most widely used, specifically Q-learning and its variants and for deep RL, deep Q-networks. In recent years however, the most widely used approach has been the actor-critic method, such as the advantage actor critic, proximal policy optimisation, deep deterministic policy gradient and trust region policy optimisation.

Introduces concepts for the integration of Computer‐Aided Process Planning (CAPP) and Production Planning and Control (PPC). Characterizes the current situation regarding integration by a one‐way communication from CAPP to PPC, since a revised link is not envisaged. Introduces a new approach to full integration by the extension of functions within both systems′ components as well as integration of both data and information technology.

This paper presents the way that expert system technology has been used for the development of a consulting tool in the National Technical University of Athens, aiming to support those who want to apply Production Planning and Control (PPC) software in their manufacturing firm. In the knowledge base that has been created, both a production systems taxonomy and a generic software package with specific modules and functions are presented. The inferences are based on rules, that express the need for the existence of certain software, when a set of production systems characteristics is found in the production system that is under consideration. The results that are given by the expert system include the whole architecture of PPC software package that is applicable in a certain manufacturing setting.KeywordsProduction SystemExpert SystemSemantic NetworkCustomer OrderSoftware FunctionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Production Planning and Control (PPC) requires human decision making in several process steps like production programme planning, production data management and performance measurement. Thereby, human decisions are often biased leading to an aggravation of logistic performance. Exemplary, the lead time syndrome (LTS) shows this connection. While production planners aim to improve due date reliability by updating planned lead times the result is even a decreasing due date reliability. In current research in the field of production logistics the impact of cognitive biases on the decision-making process in PPC remains at a silent place. We aim to close the research gap by combining a systematic literature review on behavioral operations management as well as cognitive biases and applying the Aachen PPC model. Based on a case study from the steel industry we show the influence of cognitive biases on human decision making in several phases of PPC.

Germany pushes its energy industry towards renewable energy sources which results in an increase of distributed energy generation. Therefore, energy supply will fluctuate more and more and companies need to react to higher and volatile prices as well as to a fluctuating supply of energy. One reaction is to reactively consider these changing prices and supply of energy within production planning and control (PPC). PPC therefore acts as a control unit to integrate volatile energy supply into the production process. Within this paper a scenario is considered where a virtual power plant (VPP) combines different types of distributed energy generation. Energy simulation is then used to demonstrate how fluctuating energy prices can be considered within PPC.

Resumo A integração interfuncional é apontada por diversos trabalhos como uma alternativa para o aumento da excelência operacional, melhorando o desempenho organizacional por meio da melhoria da interação e colaboração nos processos organizacionais. O objetivo deste artigo é apresentar e discutir barreiras e desafios para melhoria da integração interfuncional entre Desenvolvimento de Produto (DP) e Planejamento e Controle da Produção (PCP) em Sistemas de Produção que adotam a estratégia de resposta à demanda Engineering-to-Order (ETO). Para atingir o objetivo proposto, foram utilizados um referencial teórico sobre os temas de interesse e constatações obtidas a partir do estudo de caso de três empresas metal mecânicas de grande porte que projetam e fabricam bens de capital sob encomenda. Como principais resultados, foram observadas barreiras e desafios relacionados à interação entre as áreas funcionais durante o processo de atendimento de pedido e a necessidade de ações que busquem melhorar a interação e colaboração entre DP e PCP, levando em conta as peculiaridades do ambiente ETO.

This work describes a model for distributed dynamic Production Planning and Control (PPC) agent based system, which includes interoperation with manufacturing automation. It is presented a demonstration prototype involving distributed software agents and industrial equipment integration, which implements part of the developed model functionalities. Clients can send orders, and resources may apply for those orders fulfilment. Resources with orders allocated to, start automatically the required manufacturing operations. The prototype was implemented integrating several tools, including LabVIEW and LEGO Mindstorms components. This is useful to validate the integration, proposed by the dynamic PPC model, between production planning processes and manufacturing execution operations.

Production Planning and Control (PPC) continues to be a fascinating area of study. With the introduction and implementation of Group Technology (GT), Just-in-Time (JIT) methods and the wide-spread use of Material Requirements Planning (MRP), there is a wide variety of potential interactions available to study from master production scheduling to the shop floor execution.

Insufficient attention has been paid to behavioural influences on the implementation of the ‘Workload Control’ (WLC) concept – a Production Planning and Control (PPC) approach for small and medium sized Make-To-Order companies – and there is an implicit assumption that managers are rationalistic in their decision-making. This paper analyses the effects of both managerial decision-making behaviour and the size of a company’s order book, affecting the number of decisions that have to be made, on two case study implementations of a WLC system. The Recognition-Primed Decision (RPD) model from the Naturalistic Decision-Making literature is used to unpack the first case where implementation failed. This highlighted a misalignment between how the company’s owner-manager initially made operational decisions and how a rationalistic WLC system functions. But the company is studied over six years, allowing us to show how the owner-manager was forced to transition from the RPD model to a more rationalistic approach to PPC as the size of the order book increased. A second case study is then briefly presented in which WLC system implementation was successful; the RPD model was not strongly evident and the size of the order book was greater to begin with. The paper helps to understand the decision-making behaviour of managers in small companies and how it may conflict or be misaligned with the rationalistic assumptions underpinning the WLC concept. This provides a possible explanation for why few successful implementations of the concept have been presented in literature.

Production planning and control (PPC) systems with their planning structures, submodules and the information and data processing show similarities with the structures of multistage multi-product manufacturing. So, here the attempt is being made on the basis of the activity analysis to integrate the information characteristics of goods involved in production with regard to the requirements of production planning into the inputoutput relationships and to represent them in the form of so-called “informational production functions”. Based on the formulation of information activities efficiency criteria are developed, which allow to evaluate the data processing in PPC-modules and thus to eliminate inefficient activities. A concrete application to dynamic lot sizing demonstrates the procedure.

Simulative Assessment of Agent Based Production Planning and Control Strategies