Production Planning And Control Research Articles

Designing production planning and control in smart manufacturing

To make manufacturing technology productive, manufacturers rely on a production planning and control (PPC) framework that plans ahead and monitors ongoing transformation processes. The design of an appropriate framework has far-reaching implications for the manufacturing organization as a whole. Yet, to date, there has been no unified guidance on key PPC design issues. This is strongly needed, as it has been argued that novel information processing technologies – as part of Industry 4.0 – result in PPC frameworks with decentral structures. This conflicts with traditional works arguing for hierarchical or central structures. Therefore, we review the PPC design literature to create a comprehensive overview and summarize design proposals. Based on our review, we come to the intermediate conclusion that PPC frameworks continue to have a hierarchical structure, although decision-making is shifted more to decentral levels compared to traditional hierarchies. Our analysis suggests that the effect of a decentralization shift has potentially strong and poorly understood implications, both from a decision-making and organizational perspective.

Künstliche Intelligenz in der Produktionsplanung und -steuerung

Abstract Production planning and control (PPC) is a central element in the production system and represents a key element of the transformation towards Industry 4.0. Artificial intelligence (AI) is used to evaluate information and to model and optimize systems. The article gives a systematic overview of application potentials of AI in PPS and discusses the challenges on the way towards AI-based production planning and control.

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.

A-109 Application of Lean Manufacturing in Electrophoresis Test Processing in Order to Reduce Lead Time

Abstract Background There has been an increase on the demand for Protein Electrophoresis and Hemoglobin Electrophoresis exams over the past few years. This growth drew attention and motivated the laboratory’s Production Planning and Control (PPC) team to carry out a process flow analysis of the electrophoresis samples coming from the external units, which are responsible for approximately 60% of the total samples analyzed of these two exams. The study aimed to minimize waste throughout the process, optimize the use of resources, standardize continuous flows of activities, and reduce the Turnaround Time (TAT) of the samples in question, disseminating Lean culture and increasing the satisfaction of internal and external customers. Methods In order to reduce the TAT of electrophoresis exams from external units, the PPC team carried out on-site visits and revised the entire flow of processes related to these exams and others that could indirectly influence it, from collection and transport until the analyst’s signature and release of the result to the patient, mapping the entire flow covered by the samples and identifying possible important bottlenecks inherent to the activities, aiming at possible opportunities for improvement, through interviews, brainstorming, flowcharts and Design Thinking. Data such as the volume of monthly tests, percentage of external samples, TAT at the regional units, TAT at the Immunochemistry sector and total TAT were extracted from the laboratory’s automated data system and analyzed by the PPC team. With this, it was possible, through the Pareto analysis, to prioritize the obstacles to be overcome and focus on the main identified bottleneck, the amount of time for signing and releasing the reports, in addition to focusing on the random flow between the receiving and triage area and the processing technical area. Results Upon discovering that the greatest impact on processing time was located in the post-analytical phase of signing and releasing of the reports we suggested the capacitation of three extra analysts for the execution of the mapped action, the retraining of the technicians of the sectors responsible for the distribution of samples and supply of the machines and the standardization of a continuous flow between the areas, strengthening the Lean philosophy and the FIFO dynamics (First in—First out). Thus, we obtained a considerable decrease in the TAT of protein and hemoglobin electrophoresis, in which the total TAT of the operation was reduced by approximately 34% and the TAT referring to the time inside the Center of Technical Operations (CTO) by approximately 40%. Conclusion With the implementation of the above mentioned processes and the use of the Lean philosophy, the laboratory was able to considerably optimize the TAT of the Hemoglobin and Protein Electrophoresis exams, in addition to training employees to release results and enhance the standardization of receipt processes and distribution of these samples, with this, it was possible to optimize resources, increase the productivity of the sector, reduce the time of patient care and consequently maximize added value to the service.

B-038 Applying Robotic Process Automation Technology to Automate the Release of Pathology Results and Reduce Costs, Time and Labor in a Clinical Laboratory

Abstract Background The need for pathological anatomy tests has increased considerably in recent years, especially in one of our regional branches, where the total operational time to release the report to the patient is crucial to obtain competitive advantages in the market. As a result, the branch´s technical team observed the high lead time of the process, the inefficiency of the use of internal resources and the impact of the various fully manual activities in order to deliver the service offered to the customer and, therefore, sought out the Production Planning and Control (PPC) team with the objective of implementing improvements, in order to optimize the total operation time and automate the activities involved, adding more value to the patient and highlighting the laboratory's brand against competitors. Methods In order to implement continuous improvements in the operation of anatomical pathology samples, the involved teams held several brainstorming sessions and mapped out the supply chain for these types of tests. Firstly, the samples were received and outsourced to a partner laboratory and as the results were ready, the reports were made available in their Laboratory Information System (LIS). These reports were then retrieved manually by a capacitated employee whose only job was to download the reports from the partner´s website, revise it and upload it to our LIS so that our patients had access to the result report. Through the application of the principles of the Value Stream Map (VSM) tool, it was possible to determine the time needed for each stage of the process and identify the use of resource in exclusive functions that negatively impact productivity such as the manual activity of retrieving and uploading files. Considering the analysis carried out, it was observed that the best way to automate the process and reduce lead time would be the development of an RPA (Robotic Process Automation) that would login in the partner laboratory´s LIS, search for the patients´ reports, download it, and upload it into our LIS. Once the report was available in our system, the analyst had only to revised it briefly for matching information and release the results. Results With the creation of RPA, the process was redesigned along with the teams involved, manual activities were automated and the need for resource exclusivity was mitigated. Thus, it was possible to obtain a 50% optimization of the designated employee's time, a 75% reduction in the total lead time of the internal operation of the laboratory team and a gain of approximately 38% in the productivity of the technical area, making the process faster and more efficient. Conclusion With the implementation and validation of RPA in the external unit, the manual process of using the systems was eliminated and the new automated process obtained excellent results when compared to the previous scenario, while now we have greater agility and speed in activities and a continuous flow of information that includes the efficient release of pathological anatomy exam results, strengthening the laboratory's competitiveness in the market and ensuring customer loyalty.

Sustainable Production Planning and Control in Manufacturing Contexts: A Bibliometric Review

Production planning and control (PPC), responsible for all the activities that keep production running regularly, plays an essential role in the transition to more sustainable manufacturing systems. PPC decision-making processes need to be driven by sustainable principles even if this makes them more effortful and complex from the strategic to operative level. This study aims to review the scientific literature relating to sustainable PPC. A bibliometric analysis of 437 papers published on the Scopus database was performed to identify the most relevant articles, authors, and journals and to provide the current topic trends and future research themes and gaps. The findings revealed the increasing interest in this topic mainly since 2018. China and the USA are the most productive countries, whereas the Journal of Cleaner Production and Sustainability are the most productive journals. The analysis has also highlighted the ways to address sustainability issues in PPC, e.g., by integrating in scheduling models objectives related to sustainability or by removing barriers to reverse logistics and circular economy at the PPC level. The following topics, instead, deserve further research: attention to the social issues in PPC and the development of decision support systems that will improve companies’ PPC decision-making capabilities in sustainable optics.

Towards an ontology-based dictionary for production planning and control in the domain of injection molding as a basis for standardized asset administration shells

The use of digital technologies in the industrial environment enables great potential for increasing efficiency in manufacturing. One building block are production environments that plan and control their production flow autonomously and decentrally. To this end, all machines and systems (so-called “assets”) need to communicate with each other and derive suitable actions based on the exchanged information. Therefore, all assets need to be represented in the virtual world. This can be realized with digital twins. A concrete implementation of digital twins is the asset administration shell, which comprises all the assets’ properties and the endpoint of the corresponding asset, so intercommunication is possible. Here, the challenges comprise establishing a manufacturer-independent vocabulary that standardizes the assets’ properties and enabling the machines and systems to interpret this vocabulary semantically. Existing standards and information models represent only a fraction of the information requirements (i.e., terms) in this domain, making autonomous production planning and control (PPC) challenging to implement. Furthermore, the information requirements of the machines and peripheral assets as well as the corresponding information flows are insufficiently defined. Therefore, this contribution aims to build a comprehensive vocabulary for the domain of PPC, which serves as a basis for standardized asset administration shells that realize machine-to-machine communication. In particular, PPC processes concerning the injection molding domain’s characteristics are considered since the interaction between the domain’s assets, i.e., injection molding machines, molds, peripheral assets, raw materials, and operators, are especially complex. For this purpose, the relevant input and output information within the injection molding domain was first collected for each process step in PPC. After that, a UML class diagram was modeled under consideration of established standards. The result of this work is an ontology, which can be used as a dictionary for the PPC in the injection molding domain and as a foundation of standardized digital twins in the form of asset administration shells.

A Basic Description Logic for Service-Oriented Architecture in Factory Planning and Operational Control in the Age of Industry 4.0

Manufacturing companies across multiple industries face an increasingly dynamic and unpredictable environment. This development can be seen on both the market and supply side. To respond to these challenges, manufacturing companies must implement smart manufacturing systems and become more flexible and agile. The flexibility in operational planning regarding the scheduling and sequencing of customer orders needs to be increased and new structures must be implemented in manufacturing systems’ fundamental design as they constitute much of the operational flexibility available. To this end, smart and more flexible solutions for production planning and control (PPC) are developed. However, scheduling or sequencing is often only considered isolated in a predefined stable environment. Moreover, their orientation on the fundamental logic of the existing IT solutions and their applicability in a dynamic environment is limited. This paper presents a conceptual model for a task-based description logic that can be applied to factory planning, technology planning, and operational control. By using service-oriented architectures, the goal is to generate smart manufacturing systems. The logic is designed to allow for easy and automated maintenance. It is compatible with the existing resource and process allocation logic across operational and strategic factory and production planning.

A behavioral perspective on workload control concepts: The influence of order release on operators’ reaction behavior

Most research on production planning and control (PPC) focuses on control structures, mathematical models, and algorithms, without explicitly considering human behavior. To consider a behavioral perspective of the PPC concept of workload control (WLC), we conduct a laboratory experiment. The study’s aim is to investigate the effects of changing workload norms, the influence of feedback information, and coworker queue length information on operators’ reaction behavior. To do so, we perform an assessment across two dimensions: operators’ (i) performance and (ii) arousal level. We contribute to the literature on endogenous processing times and highlight the importance of considering behavioral aspects when companies define an implementation strategy and when conducting WLC studies. Furthermore, given that production systems are operated by people, our results show that behavioral effects need to be considered in the specification of workload norms. Based on our results, we conclude that operators exhibit substantial heterogeneity in their reaction behavior, depending on their positioning in a flow shop (gateway station vs. downstream station).

Post-prognostics demand management, production, spare parts and maintenance planning for a single-machine system using Reinforcement Learning

Production Planning and Control (PPC) is crucial for any manufacturer and comprises steps such as demand management, production, or source planning. Manufacturers achieve competitive advantage by sustaining continuous production, which can be realised through Condition-based Maintenance and Prognostics and Health Management. Hereby, the machine’s health can be predicted, and post-prognostics decision-making allows to optimise PPC to meet customer demands and minimise costs. Unfortunately, the complex dynamic, stochastic and intransparent nature of post-prognostics PPC makes it intractable to use ‘traditional’ static or deterministic optimisation techniques or approaches that require an exact mathematical model or objective function. To tackle this, a data-driven post-prognostics Reinforcement Learning model is developed to plan and control the sourcing of spare parts, production, and maintenance of a single-machine production system to maximise production revenue by meeting customer demands and minimising costs. In a case study, Proximal Policy Optimisation, which is well-known from OpenAI’s ChatGPT, is applied to a post-prognostics PPC decision-making problem. The Proximal Policy Optimisation is compared to other state-of-the-art learners, and the performance and robustness are evaluated. Analyses show that our model outperforms other learners, as well as reactive and scheduled preventive maintenance strategies and is robust to noise and cost changes.

Influence of ISO 9001 on the configuration of production planning and control

The target-oriented configuration of production planning and control (PPC) is a critical factor for companies with increasingly complex supply chains. The logistical performance of companies, which can be measured and expressed, e.g. with the logistical objectives delivery reliability and due-date compliance, suffers above all from the multiple influencing factors of the past years. Especially in the last year, it became apparent that disruptions in material supply due to pandemics or geopolitical crises are causing resource shortages that are posing the industry with ever-new challenges. Since, in many companies, PPC tasks are carried out based on past values, disruptive events have a particularly high impact here. In addition to the external disruptive influences, companies also have to manage the complexity factors of the supply chain, such as a high number of variants or a high degree of customization. The consequence of these circumstances is that companies need corresponding robustness in PPC allowing them to stay productive and competitive. The key factor to establishing high robustness is transparency. Furthermore, the faced uncertainties are often exacerbated by insufficient data situation for the companies' PPC.Due to the outlined factors in companies, ongoing PPC tasks must be regularly checked for their quality and target conformity to ensure a high logistics performance. For this purpose, the PPC configuration must be carried out systematically and holistically. Therefore, essential PPC frameworks already exist, such as the Hanoverian Supply Chain Model (HaSupMo). Supporting the examination of the company's internal supply chain, the selected procedures for the fulfilment of the PPC tasks serve on the basis of logistical objectives. For this, comprehensive consideration of the PPC tasks must take into account the correct selection of procedures based on individual company needs and data.At the same time, there is an internationally accepted ISO 9001 standard as a basis for the continuous improvement of all company processes. The described continuous testing and improvement of business processes with a focus on quality management is intended to promote the robustness of the certified companies. Taking a closer look at the contents of ISO 9001 and PPC configuration, structural similarities are noticeable.This paper aims to examine ISO 9001 for characteristics and interfaces that enable a simplified PPC configuration. In addition, similarities should be identified and systematized, which should be taken into account for future interaction.

Exploring Implementation Barriers of Machine Learning in Production Planning and Control

This paper studies the challenges encountered during the implementation of machine learning (ML) applications in production planning and control (PPC) functions of manufacturing companies. The application of ML in PPC promises numerous benefits, and several studies have revealed the positive impact of such an implementation on key performance indicators of production systems, such as lead time, due date reliability, and inventory levels. However, despite the theoretical potential and the increased research interest in the field over the last five years, practical applications remain rare and discourse on the existence of this gap between research and practice remains relatively sparse. In this paper, we identify general ML implementation barriers in the manufacturing domain based on a structured literature review and evaluate the relevance of these barriers within the PPC domain by interviewing ML and PPC experts. This research is beneficial for practitioners and researchers who aim to implement ML applications in PPC by enabling them to evaluate the possible barriers and challenges during implementation projects within their organizations.

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.

PRODUCTION PLANNING CONCEPTS FOR SMALL AND MEDIUM ENTERPRISES (SMES): A CASE STUDY IN A BAKERY COMPANY

Production planning and control (PPC) is a branch of knowledge that commonly discusses the available concepts and methods for operation management. The knowledge and understanding of PPC concepts, together with proper implementation can assist an organization in fulfilling customer demands with minimum cost. Large organizations are exposed to PPC concepts due to the qualification of workers and the availability of training. However, for small and medium enterprises (SMEs), awareness and knowledge of PPC concepts are lacking. This study is carried out in an SME, a bakery company located in Selangor, Malaysia, to determine how PPC concepts can be applied in SMEs. The case study found several problems faced by the company, ABC Bakery, that relate to supplier selection and workforce planning. In order to overcome the problems, two PPC techniques are introduced. Firstly, the quantity discount model is proposed to solve supplier selection issues. Secondly, the aggregate planning concept is utilized to overcome the workforce issue. From the calculation and analysis conducted, it is found that ABC Bakery can make a wise decision in the supplier selection process when the quantity discount model is implemented, in which selection is based on the minimum total cost incurred. Next, by applying aggregate planning; specifically varying workforce size and influencing demand strategies, the number of workforces required can be estimated and then further improved to increase demand in the future. In conclusion, this study shows that PPC concepts are suitable to be implemented in SMEs specifically to solve operations problems, and then help SMEs in increasing productivity and sales in the long term.

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.

To digitalize or not? Navigating and merging human- and technology perspectives in production planning and control

Contemporary manufacturing companies are navigating industrial digitalization anticipating increased production efficiency and competitiveness in a volatile environment. This study focuses on the implementation processes of digital tools for production planning and control (PPC), i.e., advanced planning and scheduling (APS) software, in relation to the application of analog planning with physical flow boards. Digital tools can support understanding the consequences of production changes and variations, hence facilitating adaptable and resilient manufacturing. However, technological changes can be daunting, and effective implementations require dynamic capabilities to remain competitive in elusive environments. The aim is to study the implementation processes of an APS software to understand the requirements of fruitfully moving from analog planning to next-generation digital tools for decision support in PPC. The paper presents an explorative case study, at a manufacturing company within the energy sector. The interview study took place over 9 months during 2020–2021, investigating current and retrospective aspects of the case across 2019–2021. The case study comprises 17 in-depth interviews with a range of company employees, e.g., logistics managers and functions responsible for digitalization development. The results highlight the challenges of implementing and especially trusting digital tools for PPC. To realize the value of digital tools for PPC, it is argued that it is imperative to simultaneously apply a human-centric perspective in decision making to ensure trustworthy, sustainable, and resilient human-data-technology nexus implementations towards smart manufacturing.

Towards digital shadows for production planning and control in injection molding

In dynamic production environments, bringing flexibility to production planning and control (PPC) against continuously changing requirements is key to fulfill production objectives. Such environments often involve various information systems that make the integration of knowledge on the current state of production more challenging. In this paper, we present a conceptual approach of Digital Shadows that allow for a holistic data view on PPC-specific tasks based on an ontology, serving as a knowledge base of semantic-enriched data and production constraints. An exemplary real-world application for PPC in injection molding demonstrates the benefits of the concept. • Stakeholders in PPC are overwhelmed with information from less interlinked IT system. • Digital Shadows are a promising approach to support decisions in PPC. • Concept of Digital Shadows for Production Planning and Control. • Decision support provided through purpose driven information generation. • Exemplary concept application for minimizing rejection rates in injection molding.

One-of-a-kind production (OKP) planning and control: a comprehensive review and future research directions

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.

Approaches of production planning and control under industry 4.0: A literature review

Purpose: Industry 4.0 technologies influence how production is planned, scheduled, and controlled. In literature, different classifications of the tasks and functions of production planning and control (PPC) exist, of which one is the German Aachen PPC model. This research aims to identify and classify current Industry 4.0 approaches for planning and controlling production processes and to reveal researched and unexplored areas of the model.Design/methodology/approach: In an exploratory literature review, we review and classify 48 publications on a full-text basis with the Aachen PPC model's tasks and functions. Two cluster analyses reveal researched and unexplored tasks and functions of the Aachen PPC model. Additionally, we give a summary of each reviewed publication.Findings: We propose a cyber-physical PPC architecture, which incorporates current Industry 4.0 technologies, current optimization methods, optimization objectives, and disturbances relevant for realizing a PPC system in a smart factory. Current approaches focus on the in-house PPC, particularly on the control using real-time information from the shop floor. We propose future research directions for the unexplored tasks and functions of the Aachen PPC model.Research limitations/implications: The selection of search terms and the texts' interpretation is based on an individual assessment. The revelation of unexplored tasks and functions of the Aachen PPC model might have a different outcome if the search term combination is parameterized differently.Originality/value: Using the Aachen PPC model, which holistically models PPC, the findings give comprehensive insights into the current advances of tools, methods, and challenges relevant to planning and controlling production processes under Industry 4.0.

Applied Machine Learning for Production Planning and Control: Overview and Potentials

Manufacturing companies are under constant pressure to increase efficiency and to achieve logistical objectives. Improving production planning and control (PPC) has significant impact on these efforts. At the same time, increasing complexity and dynamics of PPC environments make PPC more difficult. One way to cope with this situation is the application of machine learning (ML) methods. In this article, we therefore address the current state of PPC-ML research and show, based on the Aachen PPC model, in which PPC tasks and subtasks ML is already applied and to what degree the task is covered by ML. The analysis is limited to core and cross-sectional tasks of the Aachen PPC model, procurement and network tasks are not included. Furthermore, a broad analysis of the targeted data mining, business and logistic objectives is conducted. In addition, we also identify motivations which prompted researchers to apply ML in PPC.