Production Planning 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.

Multiobjective Planning for Logistics Distribution of Consumer Electronic Items Based on Improved Genetic Algorithm

With the increasing uncertainty of customer demand, order management that takes into account the uncertainty of customer demand is important in the supply chain between distribution bases (distribution centers, RDCs, etc.) and large retailers. A multisite delivery planning system is recommended to scale past to handle a single product. Aiming at the logistics distribution problem of consumer electronic products, the problem is attributed to a multiobjective optimization problem, and an improved genetic algorithm is used to deal with it. Combined with the situation, this paper proposes a multiproject (multiproject delivery mass production planning system) and studies the narrative problem description, formula, solution sequence, and case analysis results. The experimental results show that this method can effectively improve the logistics distribution efficiency by 18% and reduce the distribution cost by 34%, by finding the optimal distribution path, reducing the total cost of low-carbon distribution of fresh products, improving customer satisfaction, reducing energy consumption, and achieving economic and social benefits for enterprises.

Pendampingan Perencanaan dan Pengendalian Produksi di PT. Ganding Toolsindo

Achieving production targets is the goal of all companies, especially manufacturing companies. PT Ganding Toolsindo is an automotive component manufacturing company that aims to provide a high level of service and is committed to its clients, so achieving production targets is essential. However, it is unfortunate that there are still problems regarding the non-fulfillment of production targets. In addition, issues regarding rusty raw materials and waste also occur there. The cause of this problem is the advantages and disadvantages of raw materials because production planning and control have not been appropriately implemented. Therefore, this Community Service (PkM) aims to assist partners in solving these problems through training, production planning, and control assistance. The PkM series is carried out with a preliminary survey, material preparation, training, and mentoring. The material given in this PkM activity is about "Introduction to Production Planning and Control" and "Production Planning and Control Systems for Make to Order (MTO) Type Companies.” In addition, assistance is provided to solve problems that are the company's focus, namely making Side Tube part products by making dual kanban for the parts. Based on the evaluation results, this PkM activity has been carried out well and received a positive response from PkM partners, namely PT. Ganding Toolsindo.

A skill- and feature-based approach to planning process monitoring in assembly planning

Frequent changes in customer needs and large product variety are forcing manufacturing companies to move from mass production to mass customization. Customized production can be achieved by introducing reconfigurable production systems (RMS). The customized flexibility and several characteristics of RMSs provide many opportunities in terms of process and production planning. However, those characteristics greatly increase the complexity of the design and planning of production systems. This paper presents a decision support system relying on a skill-based approach to design a reconfigurable assembly line considering the planning of assembly processes and monitoring. The proposed decision aid system is modular in design and is composed of four modules. The main input data is a CAD model of a new product variant for the identification of the assembly and monitoring requirements. Besides, a current assembly system layout with its resource descriptions exists. In the first developed module, assembly-by-disassembly and a skill-based approach are used to generate different assembly plans. In the second module, feature recognition and skill-based approaches generate process monitoring alternatives. The third module uses a linear program (LP) that aims to minimize the total cost of workstation activation and reconfiguration, as well as cycle time, and to maximize the process quality of the assembly tasks. A user-based generative model design approach is applied to optimize the values of three objective functions. In the fourth and final module, a simulation of the optimized assembly plan allows either the validation of the assembly plan and process monitoring plan or initiates a new iteration due to their infeasibility. To further demonstrate how the proposed methodology works, some computational experiments are provided for two use cases.

A new perspective on Workload Control by measuring operating performances through an economic valorization

Workload Control (WLC) is a production planning and control system conceived to reduce queuing times of job-shop systems, and to offer a solution to the lead time syndrome; a critical issue that often bewilders make-to-order manufacturers. Nowadays, advantages of WLC are unanimously acknowledged, but real successful stories are still limited. This paper starts from the lack of a consistent way to assess performance of WLC, an important burden for its acceptance in the industry. As researchers often put more focus on the performance measures that better confirm their hypotheses, many measures, related to different WLC features, have emerged over years. However, this excess of measures may even mislead practitioners, in the evaluation of alternative production planning and control systems. To close this gap, we propose quantifying the main benefit of WLC in economic terms, as this is the easiest, and probably only way, to compare different and even conflicting performance measures. Costs and incomes are identified and used to develop an overall economic measure that can be used to evaluate, or even to fine tune, the operating features of WLC. The quality of our approach is finally demonstrated via simulation, considering the 6-machines job-shop scenario typically adopted as benchmark in technical literature.

A system dynamics archetype to mitigate rework effects in engineer-to-order supply chains

There are established archetypes that demonstrate the dynamic properties of make-to-order/stock and assemble-to-order production planning and inventory control systems and their impact on total on-costs, allowing for performance benchmarks to be established. However, the dynamic properties of engineer-to-order (ETO) production system, where products are designed and made to a specific customer order, are not well understood. Time and cost-overrun, poor capacity planning and high rates of rework are difficulties faced by ETO managers and, for now, solutions for these problems are still lacking.Therefore, this paper develops an ETO production model which merges a service-orientated design subsystem with a working-unit-orientated production subsystem to establish an order book-controlled ETO system. The developed model realises automatic capacity control to maintain the expected lead time and order book. At the same time, we also conduct transfer function and stability analysis on this holistic ETO model to investigate the system's dynamic properties using Control Theory and System Dynamics.This paper's contributions could be summarised from four perspectives. 1. It provides an automatic capacity-controlled archetype for practice benchmarking and demonstrating the advantage of a whole system level order book controller. 2. The order book proportional controller, at a whole system level rather than just in the local subsystems, can offset the rework's negative impact, while achieving target order book and service times. 3. The dynamic analysis provides transfer functions, demonstrating the dynamic relationship between demand (input) with order book and lead time (outputs). 4. The derived critical condition for system stability provides guidelines for system managers to prevent the system becoming unstable.The limitation of this paper is that we assume the rework could only happen in the production system and could be rectified in the production system. However, in practice, rework could happen and be detected everywhere. Further research could relax this assumption and explore the dynamics of these scenarios.

Advances in Adaptive Scheduling in Industry 4.0

The shift of traditional mass-producing industries towards mass customisation practices is nowadays evident. However, if not implemented properly, mass customisation can lead to disturbances in material flow and severe reduction in productivity. Moreover, manufacturing enterprises often face the challenge of manufacturing highly customized products in small lot sizes. One solution to adapt to the ever-changing demands, which increases resource flexibility, lies in the digitization of the manufacturing systems. Furthermore, the distributed manufacturing environment and the ever-increasing product variety and complexity result in reduced time-to market, ubiquitous data access and sharing and adaptability and responsiveness to changes. These requirements can be achieved through smart manufacturing tools and especially Wireless Sensor Networks (WSN). Thus, the aim of this position paper is to summarize the design and development of solutions based on cutting-edge technologies such as Cloud Computing, Artificial Intelligence (AI), Internet of Things (IoT), Simulation, 5G, and so on. Concretely, the first part discusses the development of a Cloud-based production planning and control system for discrete manufacturing environments. The proposed approach takes into consideration capacity constraints, lot sizing and priority control in a “bucket-less” manufacturing environment. Then, an open and interoperable Internet of Things platform is discussed, which is enhanced by innovative tools and methods that transform them into Cyber-Physical Systems (CPS), supporting smart customized shopping, through gathering customers’ requirements, adaptive production, and logistics of vending machines replenishment and Internet of Things and Wireless Sensor Networks for Smart Manufacturing. To that end, all the proposed methodologies are validated using data derived from Computer Numerical Control (CNC) machine building industry, from European Metal-cutting and mold-making SMEs, from white goods industry and SMEs that produces solar panels.

A control algorithm for a non-stationary batch service production system using Kalman filter

This paper presents a dynamically optimized control algorithm for a fully automated batch service production process, with non-stationary nature of order arrivals. The problem of interest is a non-stationary SELSP, i.e., stochastic economic lot sizing problem, with erratic order flow, where the objective is to continuously optimize the production lot size according to the real-time detection of demand fluctuations. Such a problem has a dual nature of being a planning problem, which used to be dealt with within the operational research (OR) community and a control problem, often dealt with within the control theory (CT) circle.To tackle the problem a Kalman Filter (KF) based approach has been employed, as one of the most reliable methods in CT, with an edge over its previously studied alternatives, i.e., Exponential Filter (EF) and its fuzzified version. It is illustrated that the adopted KF performs much better both in terms of efficiency metrics of OR, and responsiveness metrics of CT. The results advocate that the new generation of automated production planning and control systems may benefit from the concepts and methods from both communities of OR and CT, in an integrated fashion.

Application of Scheduling Systems in Smart Manufacturing

At the present time, where products are more and more often made for a specific customer, the production of such products requires more and more decisions to be made in the shortest possible time. This causes that solutions and methods are sought to support production planning and scheduling in such a dynamic environment of various variables. The problem concerns both the decision support system and the relevant data sets, on the basis of which schedules are generated. The article presents an algorithm related with generating schedules in the production planning system, as well as an application for supporting the process of decision making the purpose of which is choosing the best schedule from the set of available solutions. This is very important from the point of view of “product customization” and intelligent manufacturing.

Choosing Industrial Zones Multi-Criteria Problem Solution for Chemical Industries Development Using the Additive Global Criterion Method

The safe development of chemical industries requires adequate control of the environmental sustainability of the areas where enterprises are located. The purpose of the article is to develop and test a methodology for solving the multicriteria problem of choosing industrial zones for the development of chemical industries using the method of an additive global criterion. The novelty of the methodology lies in the multi-criteria and complexity of the tool and the presence of a statistical base, which allows it to be used for various socio-economic purposes at all levels of government. As the main research tools, the methods of multi-criteria selection of objects, one-dimensional data scaling, additive convolution of criteria, and methods of multivariate statistical analysis for verifying the results obtained and making a decision were used. The article describes the mathematical apparatus of the technique for solving the multicriteria problem of selecting objects by the method of an additive global criterion. The solution algorithm provides for a three-level integration of particular indicators using the methods of mathematical processing of an array of different-dimensional values. The procedure for selecting the vectors of the criterion space makes it possible to select industrial zones and obtain a global criterion using the additive convolution method. In order to test the methodology, the problem of choosing industrial zones for the potential development of chemical industries in the Russian region was solved. For the development of chemical production, industrial zones have been selected that are included in the above-average environmental sustainability group: Bavlinskaya, Nurlatskaya, Bugulminskaya, and Leninogorskaya. Tendencies of decrease in ecological stability of the zones, which have relatively safe industries on their territory but are adjacent to the zones of location of environmentally unfavorable industries, are revealed. The materials of the article can be used in the development of intelligent systems for monitoring and controlling the development of chemical industries, which allow monitoring the level of environmental safety of industrial zones, identifying sources of negative environmental impact with pursuing decision-making on the organization and planning of production systems in the territorial space.

Requirements for developing production planning and control systems for engineer-to-order industrialized building systems

Several efforts for industrializing construction have been made in different countries, and there has been a growing demand for engineer-to-order (ETO) industrialized building systems. In this context, products are unique for specific clients, as customer orders are placed at the design stage. There are many challenges for planning and controlling those building systems, due to the high level of complexity involved. The aim of this research is to propose a set of core requirements for production planning and control systems in ETO industrialized building systems. The methodological approach adopted in this investigation was design science research. It was based on a literature review on different planning and control models, and also on an empirical study carried out in a steel fabricator company. The proposed set of requirements is aligned with the management-as-organizing approach, challenging some traditional project management assumptions, including the use of metrics and practices that are not suitable for the high degree of complexity that exists in ETO environments. As a practical contribution, the requirements can be used by ETO construction supplier companies to support the conception and development of planning and control systems.

Dynamic planned lead times in production planning and control systems: does the lead time syndrome matter?

Many companies in practice want to dynamically adjust planned lead times in their production planning and control systems in response to demand fluctuations. But for decades it has been recognised that this can lead to escalating planned lead times and realised throughput times. Authors have highlighted the negative impact of this ‘lead time syndrome’, especially in the context of Material Requirements Planning systems, prompting the development of alternative concepts intended to overcome its vicious cycle, such as Workload Control. Yet some authors have shown that increasing planned lead times has advantages – it can improve end-item service levels. To resolve this paradox, we conjecture that the effects of the lead time syndrome are limited when demand is independent of internally planned lead times, such as in make-to-order companies, and subsequently use simulation to prove this conjecture. We show that although dynamic planned lead times have a detrimental effect on performance in make-to-order systems, it is not an increase in planned lead times that leads to a performance loss. Rather, it is the decrease of lead times in low load periods that increases workloads in upcoming periods of high load. This questions the use of upper bounds (WIP-cap) in these contexts.

Adaptive Simulation-Based Optimization for Production Scheduling: A Comparative Study

Production planning and control is an essential activity for all production systems, as its performance directly affects important factors that are fundamental for business competitiveness. The flow shop scheduling problem is one of the most common problems involved in the planning of production systems, being often applied in the real world. The solution to this problem cannot be easily found due to its complexity, which makes it difficult to use methods that return optimal results due to their high computational execution time. Thus, Adaptive Simulation-Based Optimization is a tool with potential application as it considers the stochasticity of the problem, seeking solutions that are aligned with the real scenario through a simulation and optimizing the desired variables. The simulation-based optimization approach consists in a loop between an optimization heuristic/meta-heuristic and a simulation. The optimization inputs the simulation with a parameter set, while the simulation tests all the models generated by the optimization and returns the fitness function values. However, the literature presents several different heuristics, simulation approaches and optimization methodologies to solve the problem. The general objective of the present work is to comparatively evaluate different adaptive simulation-based optimization models for flow shop production systems. Two methods for comparison were considered, which apply a genetic algorithm integrated with a discrete events simulation. The first method seeks to find the best dispatching rule for each machine in the production system, while the second seeks to directly find the best sequencing of production orders without a predetermined rule. The results show that the rule-free sequencing of production orders returns a result 3.56% better than annual makespan and 5.62% better in execution time compared to the model based on dispatching rules.

Support Method for The Digital Transformation of Manufacturing Systems Based on the ‘Digital Triplet’ Concept

In recent years, optimization and efficiency improvement of manufacturing systems have been promoted through digital transformation. However, the complexity of manufacturing systems and diversified digital technologies make it difficult to realize such transformation successfully. This study proposes a method to support the development of digital solutions to realize DX, based on the 'digital triplet' concept. Digital Triplet is a manufacturing concept that aims to effectively utilize engineers' knowledge with digital technology. In this paper, digital solutions include fault detection systems and production planning systems in manufacturing systems. This method represents the process knowledge explicitly for developing digital solutions and reuses the knowledge. As a case study, we described the process of developing fault detection systems based on interviews with engineers. This made it possible to clarify and share the engineers’ process that has not been explicit. In addition, we supported a novice engineer by utilizing the descriptions. This clarified the effectiveness and challenges of the proposed method.

Valuation: A New Approach to Measure the Performance of Last Planner System

The Last Planner System (LPS) is a production planning and control system that utilizes the lean construction philosophy to improve workflow reliability. Although there are several metrics developed to measure the performance of LPS, the current metrics fail to genuinely reflect the actual performance of LPS. The literature review shows no evidence of existing researches that provide a holistic approach to measure the performance of LPS. Consequently, this research aims to propose a new holistic approach to measure the performance of LPS called valuation. The objectives of this research are: (1) to provide an overview of the current metrics used to measure the performance of LPS, (2) to propose 10 steps to measure the performance of LPS based on the valuation approach, (3) to utilize the valuation approach to derive numerous metrics based on all possible relationships between the main and sub (i.e., activity and constraint) categories of LPS that currently available or may emerge in the future. This research contributes to the body of knowledge by deriving metrics based on the valuation approach that are significantly more comprehensive and mathematically more robust since they integrate several criteria and rely on the value rather than the number or amount of activities or constraints. Hence, the valuation approach generates more accurate results. Moreover, the valuation approach can help the construction professionals to track the performance of LPS across phases or even projects by accumulating the data and measuring the proposed metrics.

Human-centricity in the design of production planning and control systems: A first approach towards Industry 5.0

Planners are supported in the fulfilment of their tasks by so-called PPC systems. Among other things, planners are dissatisfied with the way PPC systems present information and results. As a result, there is a risk that the number of errors due to misinterpretations will increase. These are primarily caused by Cognitive Biases. Even increasing automation will not change this, since according to experts, humans will remain central, especially in decision-making. The European Union points out that Industry 4.0 focuses more on technical feasibility and implementation and less on human needs. In response, it proposes the human-centered approach of Industry 5.0. Here, the human being should move to the center of the process. In this regard, there is still little research activity in the field of PPC and in the context of Cognitive Biases. Therefore, we want to take a first step towards the human-centered approach with this work. To this end, we conduct a systematic literature review and develop a questionnaire to determine the human-centered maturity level. The results will then be compared with already researched Cognitive Biases.

Maturity Model for AI in Smart Production Planning and Control System

The utilization of Artificial Intelligence (AI) to improve processes constitutes a main subject for many enterprises. The area of Production Planning and Control (PPC) possesses several functions that could profit from such approaches. However, manufacturing companies find themselves often limited in the application of these approaches. This paper concentrates on three elements to assist enterprises: 1) the clarification of what AI is (in the manufacturing context) and its application to the field of PPC; 2) a review performed together with manufacturing enterprises in Germany and Hungary in order to understand the obstacles for the implementation of AI; and 3) the proposal of a maturity model to help enterprises understand where they are in regards to AI, as a way to help them create a roadmap to achieve their objectives.

Automated Derivation of Optimal Production Sequences from Product Data

Customer specific, individual products nowadays lead to larger product variance and shorter time to market. This requires efficient production system planning. In addition, due to a larger system complexity, each iteration of the planning process itself gets soaringly complex. Time constraints and complexity, therefore, emphasize the necessity of supporting humans in planning modern production systems.Especially the determination of the production sequence holds immense potential and tends to get even more complex within specific production technologies. Exemplarily, this article focuses on welding sequences. Here, domain knowledge from product development and production planning needs to be holistically integrated. Furthermore, implicit, historic knowledge needs to be formalized and used in today’s planning tasks.This article introduces a methodical approach and a corresponding toolchain to derive optimal production sequences from customer product data which is validated using welding processes. For this, firstly, a reference system is build up consisting of historic product data (e.g. part list, CAD data) and corresponding production system characteristics (e.g. number and specifications of machines). The main aspect is to use similarities between the new product variant and assemblies from the reference system, to determine implications of product specifications on the process sequence. Overall, such restrictions can be displayed using Model-Based Systems Engineering. Relevant information (e.g. weld seam lengths) can be used to compute the optimal weld seam order regarding minimal cycle times, for example. This requires a parametric encoding of product and production system. In a nutshell, this approach covers the automated derivation of an optimal production sequence for new product variants, based on system information and product similarities, to tackle time constraints and complexity by suggesting initial planning drafts.

Quantifying Impacts of Future Climate on the Crop Water Requirement, Growth Period, and Drought on the Agricultural Watershed, in Ethiopia

Quantifying the influence of climate change on the crop growth period, water requirement, and drought conditions is essential for integrated crop production system planning. In this study, the effects of climate models from the Coupled Model Inter-comparison Product (CMIP5) on Crop Water Requirement (CWR), Length of Growth Period (LGP), and drought conditions were quantified for Lake Hawassa watershed in Ethiopia. In this study, two regional climate models were selected that showed better performance on the evaluation criteria after applying a quantile mapping bias correction procedure. The impact analysis was conducted for two Representative Concentration Pathways (RCPs) (RCP4.5 and RCP 8.5). Drought analysis was performed using the standardized anomalies of rainfall (S-index). The future growing season of the area is projected to be between April 15 and May 1 on average for all years. The total crop water requirement was projected to increase to a value of 3,258.7 mm on average under both the RCP4.5 and RCP8.5 scenarios for all the stages at the end of 2080s from its baseline value of 3,180.4 mm. In addition, the drought forecast analysis shows extreme drought with S-index values <−1.6 in the 2050s and 2080s under RCP 8.5. Of all the time periods, the 2050s recorded the smallest number of years (10 out of 30 years) with a positive S-index value, indicating projected precipitation shortages during these time periods under RCP 8.5. With this result, the combined impacts of climate change on crop production factors are expected to be high in the region. The results suggest an early warning for the study region considering low economic and technological development as in many developing parts of the world. Therefore, understanding the future changes in climate variables and their impacts can be an important input for developing a better plan for adaptation and mitigation measures.

A heijunka study for the production of standard parts included in a customized finished product.

Efficient order execution plays a crucial role in the activity of every company. In production planning it is important to find a balance between the fluctuations of orders and stability of production flow regarding the company. One of the methods of achieving this goal is heijunka (production leveling). This paper presents a study of choosing the best variant of the production planning and control system for the production of standard parts. Three variants are investigated regarding delays in order delivery. The analysis of variants was conducted using a simulation method. The method of choosing the best variant for the production system being investigated is also proposed. The results show that the best variant is a mix of production leveling and production "for stock".