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The use of chatbots has hardly been established in B2B companies to date and involves various challenges. The goal of this paper is to identify the biggest barriers to the successful implementation of chatbots in B2B customer service and to develop measures to overcome them. The barriers are identified by conducting expert interviews within the framework of Eisenhardt's case study research. These are examined through a socio-technical analysis focusing on people, technology, and organization. By means of systematic literature research and in-depth interviews with German chatbot providers and customers of chatbots, measures for overcoming the barriers are identified. Using interviews with experts from German chatbot providers, the responsible stakeholders of each measure according to the RASCI Responsibility Matrix are determined. A total of 46 implementation barriers and 100 measures to overcome these barriers are identified. The study shows that there are major barriers in the areas of people, technology, and organization of a socio-technical system that can cause the implementation of a chatbot to fail. A holistic view is therefore essential. The results provide firms with a guideline on how to overcome potential barriers during chatbot implementation in B2B customer service.
Auf Basis einer systematischen Literaturanalyse wurden insgesamt 11 Kennzahlen identifiziert, welche die Grundlage zur Beschreibung der operativen Leistungsfähigkeit von Unternehmen bilden. Die Kennzahlen wurden in die vier Leistungsdimensionen Effizienz, Qualität, Zeit und Flexibilität eingeteilt.
In the age of digitalization, manufacturing companies are under increased pressure to change due to product complexity, growing customer requirements and digital business models. The increasing digitization of processes and products is opening up numerous opportunities for mechanical engineering companies to exploit the resulting potential for value creation. Subscription business is a new form of business model in the mechanical engineering industry, which aims to continuously increase customer benefit to align the interests of both companies and customers. Characterized by a permanent data exchange, databased learning about customer behavior, and the transfer into continuous innovations to increase customer value, subscription business helps to make Industry 4.0 profitable. The fact that machines and plants are connected to the internet and exchange large amounts of data results in critical information security risks. In addition, the loss of knowledge and control, data misuse and espionage, as well as the manipulation of transaction or production data in the context of subscription transactions are particularly high risks. Complementary to direct and obvious consequences such as loss of production, the attacks are increasingly shifting to non-transparent and creeping impairments of production or product quality, which are only apparent at a late stage, or the influencing of payment flows. A transparent presentation of possible risks and their scope, as well as their interrelationships, does not exist. This paper shows a research approach in which the structure of subscription models and their different manifestations based on their risks and vulnerabilities are characterized. This allows suitable cyber security measures to be taken at an early stage. From this basis, companies can secure existing or planned subscription business models and thus strengthen the trust of business partners and customers.
High Resolution Supply Chain Management (HRSCM) aims to stop the trend of continuously increasing planning complexity. Today, companies in high-wage countries mostly strive for further optimization of their processes with sophisticated, capital-intensive planning approaches. The capability to adapt flexibly to dynamically changing conditions is limited by the inflexible and centralized planning logic. Thus, flexibility is reached currently by expensive inventory stocks and overcapacities in order to cope with rescheduling of supply or delivery. HRSCM describes the establishment of a complete information transparency in supply chains with the goal of assuring the availability of goods through decentralized, self-optimizing control loops for Production Planning and Control (PPC). HRSCM pursues the idea of enabling organization structures and processes to adapt to dynamic conditions. The approach includes the strengths of the existing planning models as well as the process of decision making in organizations. A precondition for this decentralized adaptation is the synchronization of the objectives of the several units or process owners. The basis for this new PPC Model are information transparency, stable processes, consistent customer orientation, increased capacity flexibility and the understanding of the production system as a viable, socio-technical system.
High Resolution Supply Chain Management aims to counteract the trend towards more and more centralised and rigid enterprises. Today, most companies strive to increase efficiency of business processes applying highly sophisticated, centralised planning approaches. These centralised approaches limit the companies’ ability to react flexibly and act adaptively due to external and internal turbulences. In today’s buyer’s markets companies usually try to bypass these turbulences keeping high levels of inventory resulting in a low overall efficiency. High Resolution Supply Chain Management tries to solve the problem at its root from a holistic perspective. Based on the Viable System Model developed by Stafford Beer a four-dimensional holistic production management system model, embedding an organisational structure view, an cause and action view, a control loop perspective and a decision making level has been elaborated. The basis of this model is the integration of all four perspectives into an interacting framework.
Generation of a Data Model For Quotation Costing Of Make To Order Manufacturers From Case Studies
(2022)
For contract or make to order manufacturers, quotation costing is a complex process that is mainly performed based on experience. Due to the high diversity of the product range of these mostly small or medium-sized companies (SMEs) and the poor data situation at the time of quotation preparation, the quality of the calculation is subject to strong variations and uncertainties. The gap between the initial quotation costing and the actual costs to be spent (pre- and post-calculation) is crucial to the existence of SMEs. Digitalization in general can help companies to get a better understanding of processes and to generate data. For improving these processes, an understanding of the important data for that specific process is crucial. Accurate quotation costing for customized products is time-consuming and resource-intensive, as there is a lack of an overview of data to be used within the process. This paper therefore derives a data model for supporting quotation costing in the company, based on literature-based costing procedures and recorded case studies for quotation and calculation. Based on the results, SMEs will have a first overview of the needed data for quotation costing to optimize their calculation process.
Since data becomes more and more important in industrial context, the question arises on how data-driven added value can be measured consistently and comprehensively by manufacturing companies. Currently, attempts on data valuation are primarily taking place on internal company level and qualitative scale. This leads to inconclusive results and unused opportunities in data monetization. Existing approaches in theory to determine quantitative data value are seldom used and less sophisticated. Although quantitative valuation frameworks could enable entities to transfer data valuation from an internal to an external level to take account of progress in digital transformation into external reporting. This paper contributes to data value assessment by presenting a four-part valuation framework that specifies how to transfer internal, qualitative to external, quantitative data valuation. The proposed framework builds on insights derived from practice-oriented action research. The framework is finally tested with a machine tool manufacturer using a single case study approach. Placing value on data will contribute to management’s capability to manage data as well as to realize data-driven benefits and revenue. [https://link.springer.com/chapter/10.1007/978-3-030-85902-2_19]
Industrie 4.0 is said to have major positive effects on productivity in manufacturing companies. However, these effects are not visible yet. One reason for this is the lack of understanding of maintenance services as a crucial value contributing partner in production processes, although scientific literature already highlighted the importance of indirect maintenance costs. In order to retrieve the unused potential of maintenance services, a digital shadow in form of a sufficiently precise digital representation is required, providing a data model for the value of maintenance actions so that asset and maintenance strategies can be optimized later on. Using case study research for process manufacturers, the first research contribution of this paper consists of 21 value contributing elements being identified. The second contribution is a reference processes model, showing seven major process steps as well as the required intra-organization interaction on an information technology system level. Therefore, it provides the base for the missing data model shaping the targeted digital shadow of maintenance services’ value contribution. [https://link.springer.com/chapter/10.1007/978-3-030-57993-7_69]
Augmented reality seems to offer great potential benefits in the field of industrial services. However, the question of the exact benefits, both monetary and qualitative, is difficult to evaluate, as is the case with IT investments in gen-eral. Within the framework of the DM4AR research project, an evaluation model was therefore developed. Based on group discussions and interviews on potential AR use cases, a list of monetary and qualitative benefits was compiled to form the basis for selecting suitable evaluation modules in the existing literature. These include an impact chain analysis in the form of a strategy map, a monetary eval-uation as a calculation of the return on investment, based on the assumptions of the use case as well as existing studies, and a qualitative evaluation in the form of a utility analysis. The outcome is an evaluation model in the form of a multi-perspective approach that considers the impact of AR in the four perspectives of the balanced scorecard (financial, customer, internal business processes, learning and growth). The results of the qualitative and monetary evaluation can be sum-marized in a 2D matrix to support decision-making.
It is crucial today that economies harness renewable energies and integrate them into the existing grid. Conventionally, energy has been generated based on forecasts of peak and low demands. Renewable energy can neither be produced on demand nor stored efficiently. Thus, the aim of this paper is to evaluate Deep Learning-based forecasts of energy consumption to align energy consumption with renewable energy production. Using a dataset from a use-case related to landfill leachate management, multiple prediction models were used to forecast energy demand.The results were validated based on the same dataset from the recycling industry. Shallow models showed the lowest Mean Absolute Percentage Error (MAPE), significantly outperforming a persistence baseline for both, long-term (30 days), mid-term (7 days) and short-term (1 day) forecasts. A potential decrease of up to 23% in peak energy demand was found that could lead to a reduction of 3,091 kg in CO2-emissions per year. Our approach requires low finanacial investments for energy-management hardware, making it suitable for usage in Small and Medium sized Enterprises (SMEs).