Production Planning And Control Systems Research Articles

Interactions between planners’ and PPC systems: Derivation of simulation scenarios with consideration of cognitive bias and disruptions

Despite an increase in the availability of digital solutions, planners’ remain at the center of the production planning and control (PPC) decision-making process and have to make numerous decisions involving different information. Due to the inherent complexities, planners’ are supported by PPC systems to fulfil their tasks. Research has shown that humans are influenced by cognitive bias when making decisions, especially in uncertain and complex environments and when disruptions such as machine failures occur. Researchers have therefore proposed debiasing methods to reduce human errors in decision-making. However, there is currently no environment within this research to test these proposed methods. In this study, we aimed to take the first step toward closing this research gap. We combined existing PPC research with a systematic literature review on disruptions in production as well as perspectives from practice through interviews with 12 experts. We investigated the interactions between planners’ and PPC systems with consideration of different cognitive biases and disturbances to derive relevant simulation uses cases.

Development of tools to support the production planning in a textile company

Technologies are constantly evolving and so with advances in industry 4.0, its intelligent technologies with production planning and control (PPC) give rise to a smart PPC. Nowadays, one of the fundamental systems in a company is the ERP, since it allows the integration of various information from the different areas belonging to an organization, which allows access to data in real time, among other things. Through a work carried out in a textile company, it was noticeable the need to develop a tool to support its installed ERP system and the respective data control. Thus, it is noted that PPC systems, which include the integration of all areas of a company, must be adapted to its environment, work method and characteristics. With the application of this tool, it was possible to perceive an increase in efficiency and speed in obtaining the desired results, being that it is still under development and could evolve even more.

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.

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.

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.

Production planning and control in multi-stage assembly systems: an assessment of Kanban, MRP, OPT (DBR) and DDMRP by simulation

Multi-stage assembly systems where the demand for components depends on the market-driven demand for end products, are commonly encountered in practice. Production Planning and Control (PPC) systems for this production context include Kanban, Materials Requirement Planning (MRP), Optimised Production Technology (OPT), and Demand Driven MRP (DDMRP). All four of these PPC systems are widely applied in practice and literature abounds on each of these systems. Yet, studies comparing these systems are scarce and remain largely inconclusive. In response, this study uses simulation to assess the performance of all four PPC systems under different levels of bottleneck severity and due date tightness. Results show that MRP performs the worst, which can be explained by the enforcement of production start dates. Meanwhile, Kanban and DDMRP perform the best if there is no bottleneck. If there is a bottleneck then DDMRP and OPT perform the best, with DDMRP realising lower inventory levels. If there is a severe bottleneck, then the performance results for DDMRP and OPT converge. This identification of contingency factors not only resolves some of the inconsistencies in the literature but also has important implications for the applicability of these four PPC systems in practice.

Connected production planning and control systems – implementation and the optimization process for subcontracting

Production planning and control (PPC) systems are used to organize operating materials. In order to address connectivity demands of interacting PPC systems a new software architecture has been presented in prior work. This software architecture provides a basis for data exchange in-between such systems. It is up to this paper to describe an implementation approach realizing the theory. Furthermore, a new optimization process which targets subcontracting, dynamics and communication of interconnected PPC systems is presented. As a result, the mandatory and optional content of an order enquiry as well as of a response message is discussed.

Consumers, clothing retailers and production planning and control in the smart city

Though there has been much talk of smart cities, it is still unclear what the concept means and what the role of their inhabitants might be in the development of such cities. There is a tendency to focus on technology and infrastructure and forget that cities are places for people to live. To counter this, smart city planners need to recognise that people’s needs can and should influence and shape their environment, rather than concentrating too much on technology or the built environment. In order to understand how smart cities can serve the needs of their inhabitants, we will examine the specific example of the fashion clothing industry and its development of omni-channel retailing. This will show that retailers, driven by technology-smart and fashion-conscious consumers, are reshaping the clothing supply chain to achieve greater integration, flexibility and speed. Central to this process is the manufacturers’ ability to create production planning and control (PPC) systems capable of operating effectively in an omni-channel environment. The paper presents a Composite Model of Enterprise Planning which can aid the development of such PPC systems. The paper concludes that changes within the fashion clothing industry indicate how smart cities can develop and are developing, and that although these changes are facilitated by technology, they are driven by the necessity to meet consumers’ needs.

Master disassembly scheduling in a remanufacturing system with stochastic routings

Remanufacturing is an important source of sustainable development. Remanufactured products have proven to be high quality and low cost. Due to their unique characteristics, remanufacturing processes have many differences compared to manufacturing processes. These characteristics, which make remanufacturing complex, require good performance from Production Planning and Control (PPC) activities. The goal of the paper is to propose a mathematical model for disassembly master production scheduling considering stochastic routings in the remanufacturing environment. The proposed model is based on stochastic dynamic programming and it is applied to a real case of automotive clutch remanufacturing. The results contribute to the development of theory and practice by filling a gap in knowledge of the use of PPC systems, developing a mathematical method that can be easily implemented in a spreadsheet. The findings also show some decisions that are counterintuitive. For example, in some situations disassemble more products than necessary to meet the demand can result in a lower expected total cost.

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.

Análise do planejamento e controle da produção sob a ótica da Teoria da Complexidade

Neste trabalho buscou-se analisar o sistema de planejamento e controle da produção (PCP) das empresas a partir dos preceitos da Teoria da Complexidade. Esta Teoria sugere que as organizações e seus sistemas sejam vistos como sistemas complexos adaptativos (SCAs), sistemas cuja propriedade básica é a capacidade de ajustar seu comportamento em função das alterações ambientais. Um estudo de caso foi aplicado a duas empresas, no intuito de evidenciar aspectos típicos de SCAs presentes nos sistemas de PCP das mesmas. Os resultados evidenciaram que as implicações da Complexidade estão presentes na rotina de trabalho do PCP das empresas, sendo que foi possível identificar casos em que os preceitos da Teoria contribuíram na solução de problemas emergentes na rotina do PCP. No entanto, estas características podem ser mais desenvolvidas para que o sistema possa responder mais ativamente às alterações que acontecem em seu meio.

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.

Increasing Student Interest and Comprehension of Production Planning and Control and Operations Performance Measurement Concepts Using a Production Line Game

Production planning and control (PPC) systems and operations performance measures are topics that students generally find both boring and difficult to understand. In the article, the authors present a production line game that they have found to be an effective tool to increase student interest in the topics as well as student comprehension. The game demonstrates the interactions of four basic PPC systems with various operations performance measures. Evidence of increased student comprehension and interest is provided.

Opportunistic coordination of operations in job shop production

The starting point of this article is production planning and control (PPC) in job shop production, i.e., processing units in a job shop are investigated executing different operations on part types. In this context, the problem is to allocate the single operations of production orders to processing units with respect to the underlying objectives. PPC systems, however, fail to incorporate the knowledge of production-related theory and tend to be based on relatively simple heuristics (see Steven, 1999, p. 319). This means that the inherent flexibility of a production system will largely be ignored. Existing PPC systems, thus, show a clear theoretical deficit. Given this background, the article aims at • integrating production-related theory more strongly into the world of PPC, and • exploiting developments in the area of distributed problem solving.

Reflections on production planning and control (PPC)

The paper examines the current state of Production Planning and Control (PPC), identifies some technical and systems changes that have occurred over recent years and links these with the requirements being placed on companies by the market. PPC is being asked to respond effectively to these internal and external changes by being more dynamic and providing better control of resources and delivery performance. Some of the requirements to be satisfied by the new PPC systems are identified. To meet these requirements it is suggested that better understanding is required of how different factors affect PPC systems performance and that administrative systems need improving. The quantitative, administrative and behavioural aspects of PPC are discussed. A framework for developing an agenda for action and research is provided.

Modelling and controlling the dynamics of production systems

Nowadays, in a fast-changing production environment, companies have to adapt their production structures rapidly. Therefore, new methods for production planning and control (PPC) are required that consider these dynamic changes. Today's PPC systems are mainly based on static models. Now, a dynamic production model has been developed applying methods of control theory. Using the funnel model and the theory of the logistic operating curve, a continuous model of a single production system has been developed. For the control task, a backlog as well as a work-in-process (wip) controller have been developed. The controllers interact to adjust the capacity and input rate of the work system to eliminate the backlog as soon as possible and to set the wip to a defined level. Simulation experiments confirm that this concept ensures the synchronization of capacity and work. In an ongoing research project, an extended model for several work systems connected via the material flow has been designed, which is based on the elementary one. A suggestion to integrate the strategy into PPC on the planning level has already been generated. The objective of this approach is to develop the present open-loop control realized in PPC into a closed-loop control with defined control and reference variables.

A capacity-oriented hierarchical approach to single-item and small-batch production planning using project-scheduling methods

Most production planning and control (PPC) systems used in practice have an essential weakness in that they do not support hierarchical planning with feedback and do not observe resource constraints at all production levels. Also, PPC systems often do not deal with particular types of production, for example, low-volume production. We propose a capacity-oriented hierarchical approach to single-item and small-batch-production planning for make-to-order production. In particular, the planning stages of capacitated master production scheduling, multi-level lot sizing, temporal and capacity planning, and shop floor scheduling are discussed, where the degree of aggregation of products and resources decreases from stage to stage. It turns out that the optimization problems arising at most stages can be modelled as resourceconstrained project scheduling problems.