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A stronger work orientation or even the integration of learning into activities will be one of the central basic requirements for the success of Industrie 4.0. Using the example of the project 'E-Mas – Exporting blended vocational education and training for industrial process design and optimization into the Mexican automotive sector', the paper discusses the development and implementation of a highly work oriented further education program. Together the partners Research Institute for Industrial Management (FIR) e. V. at RWTH Aachen University, MTM ASSOCIATION e. V. [MTMA], WBA Aachener Werkzeugbau-Akademie GmbH [WBA] in cooperation with the Mexican Instituto Tecnológico y de Estudios Superiores de Monterrey [ITESM] pursue the goal of designing and exporting innovative further education programs for skilled workers, developers, and operative management personnel of the Mexican automotive sector and especially German companies operating in Mexico.
The Mexican automotive sector is growing so rapidly that the existing system for education and continued training is already unable to meet the demand without the effects of digitalisation. New, technology-supported forms of learning can offer advantageous developmental possibilities in this area, as can classic work-related forms of learning. Work-related learning presupposes that work is designed to promote learning. This means that working conditions must be created to enable learning at work. Work-oriented learning processes and the design of productive and health-promoting work processes can contribute significantly to a positive development of the Mexican automotive sector.
Objectives and Key Results (OKR) is an approach that focuses on the company's goals through trust-based agreements between leaders and employees. With the OKR framework in its original form, strategic business goals are aligned with the employees' active involvement, which promotes intrinsic motivation, transparency, commitment, and alignment. Inspired by the successes at Google and Intel and shaped by its use in the tech industry, the use of OKR increased across industries. Although companies within all sectors use the OKR framework, numerous implementation efforts fail. The challenges of practitioners are not fully addressed in the development of implementation concepts for OKR. One main reason is that these challenges are not taken into account in scientific publications. The paper aims to investigate to what extent existing OKR frameworks need to be adapted to provide companies with suiting implementation guidance. Firstly, OKR is placed in the context of academically widely discussed Performance Management Systems (PMS).
Secondly, criteria for successful PMS implementation are identified and used as a baseline for analyzing existing OKR implementation concepts. A systematic literature review shows the current state of research, identifying existing OKR implementation concepts from practice and theory. The OKR implementation concepts identified are systematically mapped to the series of identified criteria for PMS implementation. It is shown that the existing OKR frameworks do not address the described criteria necessary for a successful implementation of PMS, thus the adaptation of existing OKR implementation concepts is required.
This research shines a light on the problem of slow moving inventory and obsolete stock. Obsolete stocks is a dead weight for companies, with a high probability of a write-off, tied up captial and warehouse space. The main motivation of this research is the high amount of accumulated obsolete stocks in satellite warehouses across the globe. The main objective of this research is to minimize obsolete stock and understand the insidious transformation of slow movers into obsolete stock as well as prevention of this transformation in the future. Moreover the research will also lightly touch upon visualtization of data using business intelligence using Power BI software.
The two princples of this thesis are : complete disposal and preventing write-off of obsolete stocks and managing net inventory levels of slow movers to prevent their transformation to obsolete stock. This research focuses on creating a network between satellites to alleviate the obsolete stock.
Viable Production System for adaptable and flexible production planning and control processes
(2009)
High Resolution Supply Chain Management (HRSCM) aims at designing adaptable and flexible production planning and control (PPC) processes according to the needs of the company’s supply chain environment. To reach this goal a model for a Viable Production System (VPS) has been elaborated and is presented in this paper. Based on the Viable System Model (VSM) developed by Stafford Beer current production systems are analyzed in terms of integrity. With the gained knowledge a complete recursive framework of a VPS is developed. The framework allows the design of a decentralized production system that meets all requirements of a dynamic environment. Flexible and adaptable PPC processes can be developed for each identified subsystem of the VPS. Hence, further research focuses on the development of process and control loops in order to assure the application of the framework. Exemplarily the decentralised control loop for inventory management is elaborated in a case study.
This chapter presents key challenges of digital pricing: selling value propositions, data-driven quantification of value, the design of value-driven pricing models, and the definition of subscription-based price metrics. To structure the pricing for smart-product-service offerings promisingly, a framework with four specific elements has been developed. To address the value propositions properly, this chapter presents four archetypes for offering smart-product-service systems. The chapter concludes by presenting an approach to quantify customer value for digital products and services.
Two major trends are driving many companies in the manufacturing industry to rethink and reconfigure their business logic: the trends towards applying a service dominant business logic, and the trends towards collecting and using information about the market life cycle of products. The pursuit of market lifecycle information has lately been one that is driven mostly by tremendous developments in the area of the Internet of Things and information system integration. Companies in the manufacturing industry are reconfiguring their value chains, tending towards a higher degree of service orientation. This transformation requires an understanding of the principles behind offering additional value through industrial product service systems. The design of an adequate information architecture and the subsequent management model are the key factors for a successful implementation. This article focuses on how information gathering, analysis, and the meaningful use of information have been linked to the success of those companies within the German manufacturing industry which have made the transformation towards service-orientation. On the basis of an empirical study, five success factors with a significant impact on either innovation performance and/or operational performance are identified. These findings are enhanced to derive guidelines for an adequate information architecture. The guidelines are underpinned by best practices of prosperous companies with a strong product-service-orientation. Links between best practice application and performance are analyzed, and significant relations are identified.
The quarrying industry, which largely consists of less digitized SMEs, is an integral part of the German economy. More than 95% of the primary raw materials produced are used by the domestic construction industry. Quarrying companies operate demand-oriented with short planning horizons at several locations simultaneously. Due to the low level of digitization and the reluctance to share data, untapped efficiency potential in data-based demand forecasting and capacity planning arises. The situation is aggravated by the fact that SMEs have a heterogeneous mobile machinery so as not to become dependent on individual suppliers, and that transport distances of over 50 kilometers are uneconomical due to high transport costs and low material values. Within the research project PROmining a data-centric platform which improves demand forecast accuracy and multi-site capacity utilization is developed. One of the core functionalities of this platform is an industry-specific demand forecasting model. Against this background, this paper presents a methodology for establishing this forecasting model. To this end, expected demands of secondary industry sectors will be analyzed to improve mid-term volume-forecasting accuracy for the local quarrying industry. The data-centric platform will connect demand forecasting data with relevant key performance indicators of multi-site asset utilization. Following this methodology, operational planning horizons can be extended while significantly improving overall production efficiency. Thus, quarrying businesses are enabled to respond to fluctuating demand volumes effectively and can increase their personnel and machine utilization across multiple quarry sites.
The number of available technologies is constantly rising. Be it additive manufacturing, artificial intelligence (AI) or distributed ledger technologies. The choice of the right technologies may decide the fate of a company. Due to the overwhelming amount of information sources, regular technology market research becomes increasingly challenging, especially for SMEs. In order to assist the technology management process, the authors will introduce the architecture of an automated, AI-based technology radar. The architecture will automatically collect data from relevant sources, assess the relevance of the respective technology (i.e. their maturity level) and then visualize it on the radar map.