Disassembly sequencing in the regeneration of complex capital goods

Despite constantly increasing condition monitoring parallel to operation, maintenance, repair & overhaul (MRO) processes in the regeneration of complex capital goods are still characterised by a high degree of uncertainty regarding the capacity and material demands to be expected from a regeneration order. In order to meet the committed delivery times and dates, the disassembly at the beginning of the regeneration supply chain is of particular importance for the performance of the entire downstream regeneration process. High potential for improving logistical performance lies in an intelligent and logistics-oriented sequencing strategy in disassembly. In addition to technical-physical boundary conditions, the interaction of the disassembly process with downstream process steps and additional other control measures must also be taken into account. The logistical description and evaluation of the sequencing-oriented measures for improving the logistical performance of disassembly in the context of the regeneration of complex capital goods makes their modelling a necessary prerequisite and basis. This paper presents a basic logistical design and modelling approach

Regeneration Supply Chain Model and Pool Stock Dimensioning

When regenerating complex capital goods, on-time delivery as the most valuable customers’ requirement is crucial. Schedule reliability and throughput times are being trimmed to meet their targets as precisely as possible while keeping the logistic costs in-check. After disassembly, a significant number of components face relatively long repair times and need to be reassembled on a timed schedule. The configuration of internal supply chains offers the potential to improve schedule reliability. Pooling strategies are developed and discussed to achieve higher flexibility and positive effects on logistical performance. Pools help with reducing throughput times and short-term capacity allocation to satisfy an optimal on-time schedule. Serviceable (SA) components that have already been repaired, are stored in SA-pools and, if necessary, are allocated to the reassembly. The focus of this paper is the non-serviceable pool (nSA-pool), which provides repairable components to the repair stage. The nSA-pool helps to streamline the workflow before components reach several repair shops and has a direct impact on the repair process. Therefore, a model that allows the comprehension of interactions within the internal supply chain was developed and expedient pooling strategies were derived. Furthermore, the related pool stock dimensioning of preceding pools (nSA) before the repair stage and following pools (SA) are put into perspective

Assessing product portfolios from a production logistics perspective

The increasing individualization and the growing customer demand for product variety leads to a constant shortening of product life cycles and to the necessity of periodically rationalizing product portfolios. For this reason, approaches to product portfolio assessment offer methods that allow a financial or market-oriented valuation of existing products in portfolios. When assessing products in product portfolios, conventional approaches do not explicitly take the logistical impact of products on the logistics performance or costs of the production into account. The consequence of neglecting the logistical assessment dimension to product portfolios is that products, that have a negative impact on the logistics performance of a company, are not part of a critical examination. This paper therefore presents an approach that aims at developing a methodology to assess product portfolios both from a logistical as well as from financial or market-oriented perspectives. To this end, the approach initially works the influence of individual products and product characteristics on the logistics performance and logistics costs of production out. The consolidation of these findings with further evaluation variables then enables a product portfolio optimization with explicit consideration of a logistic assessment dimension

Model-Based Approach for Assessing Planning Quality in Production Logistics

For manufacturing companies, reliable production planning and scheduling not only is the basis for efficient order processing but at the same time is an essential prerequisite for the integration and coordination of all participants along the entire supply chain. At the same time, the increasing delegation of planning activities to dynamic software solutions leads to increasing intransparency regarding the planning behavior. It thus becomes increasingly difficult to identify and address inefficiencies or problems caused by the planning processes within industrial supply chains. This paper presents an easy-to-use method for describing, visualizing and analyzing scheduling behavior in manufacturing companies requiring only very few data. In addition, an overview of key planning quality indicators (KPQIs) to be considered in the evaluation of the planning quality is given and structured along the assessment dimensions of plan stability and planning accuracy. The specific application at a maintenance, repair and overhaul (MRO) service provider for complex capital goods demonstrates the benefits and insights to be gained from the model's application, especially in highly dynamic market environments. Using machine learning, characteristic planning patterns can also be statistically determined with the developed description logic and KPQI system

Towards an autonomous maintenance, repair and overhaul process: Exemplary holistic data management approach for the regeneration of aero-engine blades

The maintenance, repair and overhaul (MRO) processes of aircraft engines are dominated by a high proportion of manual work and subjective condition assessment of used parts. This leads to inefficiency due to additional, partially not required workload and high scrap rates. Further, there is a lack of knowledge about the effects of the respective repair measures on the performance of the parts. So far, there are no autonomous repair solutions that allow an optimal and individually tailored regeneration. In order to realize such a process, it is necessary to bring together the manufacturing, function-simulating and logistics-oriented disciplines in an integrated system. For this, data management along the process chain is an important success factor. In particular, the provision and linking of the data and data formats required for simulation and the production environment is of fundamental importance. This paper presents a data architecture that can serve as a framework for data integration within a representative process chain for regeneration

Spare Parts Demand Forecasting in Maintenance, Repair & Overhaul

Despite a high degree of uncertainty about the scope of future orders and the corresponding capacity and material demands, Maintenance, Repair & Overhaul (MRO) service providers face high expectations regarding due date reliability by their customers. To meet these requirements while at the same time keeping delivery times short, the availability of the required spare parts or pool parts is an essential success factor. As these cannot be kept in stock in large quantities due to their high monetary value, reliable spare parts demand forecasts are of vital importance for the profitability of MRO service providers. As a result of a high degree of information uncertainty and the mostly lumpy demand patterns, conventional time-based and statistical methods do not show sufficient forecasting quality for application in the MRO industry. Data-based approaches incorporating machine learning methods offer promising capabilities to achieve improved predictive accuracy but still need to be adequately linked to production planning and control to realize their full potential. This paper first analyses potential approaches to spare parts demand forecasting in the MRO industry, focusing on forecast accuracy and potential for integration into material and production planning. Based on this, a classification of demand forecasting approaches is presented and an approach for order-based material demand forecasting with two-step feature selection is proposed. Finally, the presented approach is applied on a real dataset provided by a MRO service provider

Improving MRO order processing by means of advanced technological diagnostics and data mining approaches

Production planning based on uncertain load information may lead to low schedule adherence or low capacity utilization. Thus, maintenance, repair and overhaul (MRO) service providers are striving to improve their business processes to achieve high logistics efficiency. To estimate repair expenditures and material demands as early as possible, different approaches may be pursued. In this paper, the advancement of technological diagnostics to enable condition assessment without prior disassembly and the use of data mining to generate reliable forecasts are discussed. Thereby, the potential for planning MRO order processing is focused using the example of aircraft engines and rail vehicle transformers

Approaching Automation of Production Planning and Control: A Theoretical Framework

The ongoing digitization of production has led to a significant increase in global competition. Manufacturing companies located in high-wage countries such as Germany must find ways to transform their production to economically competitive technologies. Automation is seen as a suitable method for reducing process costs in the manufacturing industry. Especially the automation of production planning and control (PPC) is a major challenge for companies. Since it often is not easy to determine the objectives for automation, identifying automation potentials is a problem. In addition, there are organizational, technical and personnel challenges to automation. While most companies have recognized the potentials of automation they often fail to achieve these in practice. This paper presents a process model that supports companies in systematically automate PPC. The model provides users with guidelines to help to identify automation potentials. It offers the ability to overcome typical barriers to automation through recommendations for appropriate measures and determines the common challenges companies are facing in the field of automation. The process model is divided into two sub-models. In the phase model, the required phases and steps of the model are identified. The task model provides a detailed description of tasks to perform to successfully approach the automation of PPC

Einführung einer autonomen Produktion: Ein Reifegradmodell mit Handlungsempfehlungen für produzierende Unternehmen

The vision of industry 4.0 is characterized by autonomous production systems. Achieving this autonomy is a major challenge for many industrial companies. Supporting the implementation of autonomy is an essential success factor. The maturity model presented in this article offers an opportunity for companies to identify the current state of development, their individual objectives and the corresponding evolutionary path for implementation