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

Journal of Intelligent Manufacturing - In furtherance of emerging research within smart production planning and control (PPC), this paper prescribes a methodology for the design and development of...     

Workforce planning and financing on a production/capital discrete‐time model

Traditionally, companies have considered production planning and financial commitments separately. Production planning involves planning when to produce how much of a product, while the financial commitment considers which loans to take and how to repay them. In the current difficult financial environment with new challenges and with different opportunities, such as short‐term flexible loans for paying salaries, these related problems must be considered together. In this paper, we model the two processes (production and cash flows) in a single framework, using a mixed‐integer programming discrete‐time formulation. When taken individually, each of the problems has been thoroughly discussed in the literature, while the combined version that also incorporates labor financial costs and workforce sizing is more scarce. The main contribution of the paper involves new strategies for financing labor costs in strong agreement with the company's production plan and financial commitments. The new strategy relates credit ceiling with employment funding, using a sequence of flexible short‐term loans. We consider applications and propose mathematical programming based tools that can be used by companies’ managers for conducting their own solutions analysis, following their own findings and discussion of alternative scenarios.     

A smart world needs smart solutions

ACI Worldwide has over 500 customers in more than 73 countries, and conducts its smart card development activities from a specialist Business Unit in the Netherlands. Pieter Hoogendoorn, director of business development, with responsibility for developing ACI's smart card business, in particular in the Asia Pacific region, talks about the company's plans for serving converging industry sectors.     

Dynamic Drum-Buffer-Rope approach for production planning and control in capacitated flow-shop manufacturing systems

Drum-Buffer-Rope-based production planning and control (PPC) approaches provide production managers with effective tools to manage production disruptions and improve operational performance. The corner stone of these approaches is the proper selection of time-buffers which are considered as exogenously defined constant. However, the majority of real-world manufacturing systems are characterized by the dynamic change of demand and by stochastic production times. This fact calls for a dynamic approach in supporting the decision making on time-buffer policies. To this end, we study a capacitated, single-product, three-operation, flow-shop manufacturing system. We propose a dynamic time-buffer control mechanism for short/medium-term PPC with adaptive response to demand changes and robustness to sudden disturbances in both internal and external shop environment. By integrating the control mechanism into the flow-shop system, we develop a system dynamics model to support the decision-making on time-buffer policies. Using the model, we study the effect of policies on shop performance by means of analysis of variance. Extensive numerical investigation reveals the insensitivity of time-buffer policies to key factors related to demand, demand due date and operational characteristics such as protective capacity and production times.     

Framework and deployment of a cloud-based advanced planning and scheduling system

Many small and medium-sized manufacturing enterprises (SMEs) have already implemented enterprise resource planning (ERP) and manufacturing execution system (MES) and began to start the journey of cloud manufacturing; however, the high cost of hardware and software investment, implementation, and maintenance usually hinder SMEs from adopting an advanced planning and scheduling (APS) system. This paper aims to develop a cloud-based APS (C-APS) system framework, the service structure, and approach of deploying the C-APS system in a public cloud infrastructure platform and service provider or hybrid cloud platform. The package diagram is proposed for building the C-APS system's virtual factory model to improve modeling efficiency and data stability. The C-APS system is a cloud-based and object-oriented software; its simulation-based scheduling engine can generate the significant production and operations schedule, and has the characteristics of on-demand self-service, quickly expanding and adjusting to the virtual plant model. The C-APS system's application in a leading automotive part assembly company's printed circuit board production scheduling shows that the input planning data model is easy to maintain. The scheduling quality is high; the computing time is short and acceptable for practical application.     

Production planning and control for remanufacturing: a state-of-the-art survey

Remanufacturing is rapidly emerging as an important form of waste prevention and environmentally conscious manufacturing. Firms are discovering it to be a profitable approach while at the same time enhancing their image as environmentally responsible, for a wide range of products. In this paper the characteristics of the remanufacturing environment are discussed first to distinguish this environment from other manufacturing environments. The production planning and control function of the remanufacturing firm is examined in this environment. The research in the various decision-making areas that comprise the production planning and control function is evaluated. There are many areas where the research is still scant. The lack of any overall integrated framework and models for the production planning and control function is noted. It is also pointed out that most firms are still grappling with these problems and do not have any formal mechanisms in place. There is a need to develop models and frameworks grounded in the problems and needs of these remanufacturing firms.     

Quality control planning to prevent excessive scrap production

This paper presents a risk-based approach for quality control planning of complex discrete manufacturing processes, to prevent massive scraps to occur. An analytical model is developed to optimize the quality control plan (QCP) subject to inspection capacity limitation and risk exposure objectives. The problem is then formulated as a constrained capacity allocation problem. A dedicated heuristic that solves a simplified instance of an industrial case study, from semiconductor manufacturing, is presented to provide insights into the applicability and the operational use of the approach and its potential gains in terms of risk exposure reduction. The main advancement resulting from this work is the proposal of a model of quality control allocation and an understandable algorithm to prevent the production of excessive amounts of scrap. The industrial illustration shows a decrease in potential losses by a factor of 3.     

IoT-enabled smart appliances under industry 4.0: A case study

Manufacturers expect the extra value of Industry 4.0 as the world is experiencing digital transformation. Studies have proved the potential of the Internet of Things (IoT) for reducing cost, improving efficiency, quality, and achieving data-oriented predictive maintenance services. Collecting a wide range of real-time data from products and the environment requires smart sensors, reliable communications, and seamless integration. IoT, as a critical Industry 4.0 enabler emerges smart home appliances for higher customer satisfaction, energy efficiency, personalisation, and advanced Big data analytics. However, established factories with limited resources are facing challenges to change the longstanding production lines and meet customer’s requirements. This study aims to fulfil the gaps by transforming conventional home appliances to IoT-enabled smart systems with the ability to integrate into a smart home system. An industry-led case study demonstrates how to turn conventional appliances to smart products and systems (SPS) by utilising the state-of-the-art Industry 4.0 technologies.     

A smart process controller framework for Industry 4.0 settings

This paper presents a smart supervisory framework for a single process controller, designed for Industry 4.0 shop floors. This digitization of a full supervisory suite for a single process controller enables self-awareness, self-diagnosis, self-prognosis, and self-healing (by definition, these "self" elements are missing from other supervisory frameworks diagnosing numerous controllers in parallel). The proposed framework is aligned with the concept of a Cyber Physical System (CPS), since its implementation generates a rich cyber physical entity of the controlled process. This CPS entity can either be considered as the process digital twin, or can provide a solid basis for generating it. Finally, the framework includes the main characteristics of Industry 4.0, such as advanced use of Artificial Intelligence (AI) and big data analysis. The framework is based on four modules: (1) Control and Awareness module—performing both continuous process control and adjustments, as well as machine learning (ML) and statistical process control (SPC) for identifying abnormalities that require further diagnosis; (2) Process -diagnosis module—performing continual (recurrent) analysis of the process state and trends; (3) Prognosis and Healing module—performing prognosis and automated intervention via parameter changes, re-configurations, and automated maintenance; (4) External Interaction Platform—an interactive module for interfacing with experts, presenting them with the process analysis information and obtaining feedback from them as part of a learning process. Using an implementation showcase to illustrate the methodological framework’s applicability, we demonstrate its real-world potential. The proposed framework could serve as a guide for implementing smart process control and maintenance systems in Industry 4.0 shop floors. It could also provide a firm basis for comparison with future suggested frameworks. Future research directions could include pursuing improvements to the proposed process control framework and validating the framework by case studies of its implementation.

     

Turning Organizational Culture from Fad into Management Tool*

Talking about a company's culture has become a fad. To turn the concept into something of practical managerial use, we need to know how to assess a company's culture in ways that permit realistic proposals for actions with some chance of moving the company in a desired direction. The author has found six dimensions of corporate culture, some of which are consistent with earlier work. He shows how the dimensions can be a framework for introducing and monitoring proposed organizational changes.     

Mixed reality participants in smart meeting rooms and smart home environments

Human–computer interaction requires modeling of the user. A user profile typically contains preferences, interests, characteristics, and interaction behavior. However, in its multimodal interaction with a smart environment the user displays characteristics that show how the user, not necessarily consciously, verbally and nonverbally provides the smart environment with useful input and feedback. Especially in ambient intelligence environments we encounter situations where the environment supports interaction between the environment, smart objects (e.g., mobile robots, smart furniture) and human participants in the environment. Therefore it is useful for the profile to contain a physical representation of the user obtained by multi-modal capturing techniques. We discuss the modeling and simulation of interacting participants in a virtual meeting room, we discuss how remote meeting participants can take part in meeting activities and they have some observations on translating research results to smart home environments.     

Growing online-to-offline platform businesses: How Vytal became the world-leading provider of smart reusable food packaging

Online platform businesses can grow massively and rapidly. But what if a company's business model features both an online platform application and a physical product, as is the case in logistics (e.g. container distribution), consumer retail (e.g. food delivery), or mobility (e.g. booking of shared rental vehicles)? Online-to-offline platform businesses must synchronise online platform growth with offline product transaction growth because online platforms may attract users, but value generation and capture occur through offline product transactions. If one outpaces the other, either demand cannot be satisfied, or costs increase disproportionally. How do online-to-offline platform businesses navigate this dilemma? We report on the exemplary case of Vytal, an innovative startup that has managed to become the world-largest provider of smart reusable packaging solutions in the food retail sector. Vytal couples the distribution of offline food containers with an online transaction platform application that connects restaurants, canteens, and supermarkets with consumers. We trace how Vytal strategically organised and orchestrated their growth online and offline. Based on our analysis, we offer a framework containing practical lessons for how companies can grow online-to-offline platform business models and navigate the trade-offs between the online and offline components of such business models.     

Graphical tools for production planning in small medium industries (SMIs) based on pinch analysis

Small medium industries (SMIs) routinely face supply variations in their production cycle. Such firms are typically characterised by limited resources and insufficiency of funds. Thus, SMIs need simple solutions to cope with the production planning issues. Pinch analysis has been proven as a strategy for planning of efficient use of scarce resources. Recently, it has been extended to various production planning problems. In this paper, a simple novel graphical approach is proposed to address two common production planning problems in SMI's, which are warehouse space allocation and production capacity planning. Two industrial case studies are shown in this paper to illustrate the proposed approach.     

Proplant: Multiagent system for production planning

The so-called ''tribase'' acquaintance model of the agent's behavior is presented in this paper. This represents an extension of the twin-base model (Cao et al., 1997). Based on practical experience, the new model tries to cope with parallel processing, precedence constraints, and sparse resources. The idea of substituting the interagent negotiation processes by the periodical internal planning activity of the agents is stressed. A multiagent system, ProPlanT, as an application of the tribase model for the project-oriented production planning developed for TESLA TV company is described in detail. Three types of agents production planning agent (PPA), production management agent (PMA), and production agent (PA) are distinguished. The corresponding tribase models and potential role of metaagents are discussed.     

智能电网中基于MQTT协议的ABAC访问控制方案

在智能电网环境中,电力运营商和消费者通过智能电表进行大量高精度的用电数据的实时监测,用户机密数据持续暴露于未经授权的访问,在这种传统通信模式下,智能电表对家庭用户能源消耗的细粒度测量造成了严重的隐私安全问题,而现有的静态访问控制方法并不满足智能电网环境基于上下文的动态访问特性。针对此问题,提出一种基于物联网通信协议（MQTT协议）的访问控制方案,通过在MQTT协议中对树型结构的主题列表设计基于ABAC访问控制模型的动态上下文授权策略,并在WSO2系统使用XACML策略语言实现了提出的访问控制方案。性能评估结果表明,该方案能在较低的通信开销内支持动态的访问控制,以解决智能电网中用户的用电信息未经授权而泄露的隐私安全问题。     

A big data-driven framework for sustainable and smart additive manufacturing

From the last decade, additive manufacturing (AM) has been evolving speedily and has revealed the great potential for energy-saving and cleaner environmental production due to a reduction in material and resource consumption and other tooling requirements. In this modern era, with the advancements in manufacturing technologies, academia and industry have been given more interest in smart manufacturing for taking benefits for making their production more sustainable and effective. In the present study, the significant techniques of smart manufacturing, sustainable manufacturing, and additive manufacturing are combined to make a unified term of sustainable and smart additive manufacturing (SSAM). The paper aims to develop framework by combining big data analytics, additive manufacturing, and sustainable smart manufacturing technologies which is beneficial to the additive manufacturing enterprises. So, a framework of big data-driven sustainable and smart additive manufacturing (BD-SSAM) is proposed which helped AM industry leaders to make better decisions for the beginning of life (BOL) stage of product life cycle. Finally, an application scenario of the additive manufacturing industry was presented to demonstrate the proposed framework. The proposed framework is implemented on the BOL stage of product lifecycle due to limitation of available resources and for fabrication of AlSi10Mg alloy components by using selective laser melting (SLM) technique of AM. The results indicate that energy consumption and quality of the product are adequately controlled which is helpful for smart sustainable manufacturing, emission reduction, and cleaner production.     

An IIoT-driven and AI-enabled framework for smart manufacturing system based on three-terminal collaborative platform

Smart manufacturing has great potential in the development of network collaboration, mass personalised customisation, sustainability and flexibility. Customised production can better meet the dynamic user needs, and network collaboration can significantly improve production efficiency. Industrial internet of things (IIoT) and artificial intelligence (AI) have penetrated the manufacturing environment, improving production efficiency and facilitating customised and collaborative production. However, these technologies are isolated and dispersed in the applications of machine design and manufacturing processes. It is a challenge to integrate AI and IIoT technologies based on the platform, to develop autonomous connect manufacturing machines (ACMMs), matching with smart manufacturing and to facilitate the smart manufacturing services (SMSs) from the overall product life cycle. This paper firstly proposes a three-terminal collaborative platform (TTCP) consisting of cloud servers, embedded controllers and mobile terminals to integrate AI and IIoT technologies for the ACMM design. Then, based on the ACMMs, a framework for SMS to generate more IIoT-driven and AI-enabled services is presented. Finally, as an illustrative case, a more autonomous engraving machine and a smart manufacturing scenario are designed through the above-mentioned method. This case implements basic engraving functions along with AI-enabled automatic detection of broken tool service for collaborative production, remote human-machine interface service for customised production and network collaboration, and energy consumption analysis service for production optimisation. The systematic method proposed can provide some inspirations for the manufacturing industry to generate SMSs and facilitate the optimisation production and customised and collaborative production.     

A modelling framework for complementary design of production planning and control systems

In a business era characterized by a dazzling rate of change, the improvement of production planning and control begins to be a main objective for manufacturing industries. This paper postulates four main statements to be considered for the design of production planning and control systems (PPC-systems) comprising human and technical sub-systems. The first is that production models required for the design of PPC-systems (i.e. design models) cannot be identical to production models required for planning and control of production systems (i.e. regulatory models). The design of PPC-systems must primarily focus on the quality of interaction between the regulatory models. This insight supports the second statement, which postulates that the design of PPC-systems requires a complementary design approach. Complementary design means to take explicitly into account that human and technical sub-systems- based on the differences in strengths and weaknesses of both- can achieve through their interaction a new quality, possible neither to human nor technical sub-systems alone. The third statement is that a complementary design of PPC-systems will only be possible if a fundamental change of mind from a static to a dynamic as well as from a technical to a socio-technical perception (i.e. a complete perception) of production systems takes place. Without a complete perception of production systems, designed PPC-systems will not be sufficiently reliable, maintainable and flexible, will be difficult to comprehend, and their elements will not be re-usable for further applications. The fourth statement is that the integral support of the design process requires a dual modelling framework comprising a meta- and an object-model. Considering these fundamental insights that were confirmed by a practical case study, a dual modelling framework for the design of PPC-systems which incorporates criteria for complementary design is outlined.     

A cyber-physical robotic mobile fulfillment system in smart manufacturing: The simulation aspect

Incorporating mobile robots into the production shop-floor helps realize the concept of smart production, and it is considered one of the approaches to enhance manufacturing and operational efficiency and effectiveness by academics and industrial practitioners. This paper develops a cyber-physical robotic mobile fulfillment system (CPRMFS) for tool storage in smart manufacturing. The purpose is to enable Just-in-Time material transfer on the production shop-floor during manufacturing. A decentralized multi-robot path planning adopts graph neural networks (GNN) in the new proposed CPRMFS. We compare multiple classification algorithms for the mobile robots' action prediction, including proposing a spatial-temporal graph convolutional network (ST-GNN) under these circumstances. We also extend the research with the enhanced conflict-based search path planning algorithm. Compared with the existing literature, ST-GNN, under the enhanced conflict-based search, could obtain higher accuracy with an average value of 90% under different scenarios. The practical applicability of the proposed system with the further consideration of ST-GNN is further explained as a reference for manufacturing practitioners who looked out on a confrontation of introducing the mobile robot solutions in their manufacturing site with the goal of enhancing the operation processes.     

基于时间窗的AGV动态避碰路径规划方法

为了解决多AGV在动态不稳环境下的无碰撞路径规划和系统效率提升的问题,提出了基于时间窗的AGV无碰撞路径规划方法。首先建立了多AGV的避碰模型,并结合时间窗模型,将多AGV的无碰撞路径规划分为预先规划和实时规划两阶段,预先规划阶段进行多AGV无冲突时间窗的计算和最大化系统中AGV的流通量,实时规划阶段通过改变AGV在避碰模型上的占用优先级和局部重规划的方法进行动态避碰。最后以某智能仓储为应用案例进行仿真实验,证明了该算法能有效避免多AGV的碰撞,提高AGV的流通量,同时在动态环境下具有较好的鲁棒性和柔性。