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

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

Production planning and control (PPC) systems have to cope with rising complexity and dynamics that arise from a higher demand for individualized goods, short delivery times, a strict adherence to due dates and internal unexpected events, e.g. machine breakdowns or rush orders. Conventional production planning and control methods cannot handle unpredictable events and disturbances in a satisfactory manner because in practice the complexity of centralized architectures tends to grow rapidly with size, resulting in rapid deterioration of fault tolerance, adaptability and flexibility [8]. One approach to overcome these difficulties is to develop decentralized systems with autonomous control methods to reduce the complexity that has to be taken into account for rendering decisions [13].

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

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

In an environment of constantly growing market dynamics and associated increasing complexity of company structures and their production processes, manufacturing companies are forced to adapt to this environment. Information technology is thereby a key for manufacturing companies to regain sovereignty over their own production processes. Digital networking via their own company and as well, the overall supply chain, can only succeed if digital planning reflects reality as accurately as possible and if production control can react to deviations in real time. In essence, this leads to a development of process control towards process regulation. While long-term production and resource planning is usually mapped by Enterprise Resource Planning (ERP) systems, detailed planning, including short-term deviations and real-time data at the production level, is increasingly supported by Manufacturing Execution Systems (MES) at the production control level. However, in order to bring the underlying system concepts into line with Industry 4.0 efforts in a standardized manner, mutual functional integration within the framework of interoperable production planning and control is of crucial importance. For this purpose, studies were carried out in particular into cause-effect relationships. Thus, the overarching research objective is a valid design model to increase the controllability of production planning and control systems (PPC) in the context of Industry 4.0.

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

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.

The presented model of production planning is a result of research on improvement in the production planning and control system for river shipyards. River shipyards produce and repair a numerous assortment of products /river ships/ what significantly complicates designing the production planning and control system. The production planning requires a strict determination of a number of quantities — generally called — planning parameters /items/. From among them the most important are the parameters determining the planning process in terms of product structure, enterprise production structure and time structure.

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

At present, it is still impossible to accurately solve realistic problems of production planning and control (PPC) in an acceptable period of time. Therefore numerous heuristic procedures were developed to solve problems of PPC. The efficiency of these procedures is usually appraised by means of statistical analyses. The data necessary for the analyses must meet high requirements to obtain significant and generalizable results from the statistical analyses. A procedure for the generation of test data is presented in the article. The procedure is based on given characteristics. It stochastically produces PPC data of arbitrary complexity. The procedure for the generation of test data is based on a hierarchical concept. The user enters values of aggregated classification numbers which globally characterize the modeled production system and the amount of released production orders. The values of detailed classification numbers are stochastically generated from the aggregated classification numbers. These values form the input for the generation of the PPC data wanted. This generation is stochastically, too. The user may modify both classification numbers and PPC data in order to generate test data equivalent to known practical data or to acquire test data with slightly changing characteristics for analyses of parameter sensitivity.

Designing production planning and control systems

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

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

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

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