Generation of a Data Model for Quotation Costing of Make to Order Manufacturers from Case Studies

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

Evaluation Of An Capacity-oriented, Agent-based Order Release For Matrix-structured Assembly Systems

To address growing challenges in automotive assembly with ever shorter innovation cycles, increasing variant diversity and uncertain market development, innovative concepts for assembly systems are needed. As a response, the concept of matrix-structured assembly system was introduced. Matrix-structured assembly systems break up with the rigid line structure of assembly stations and replace the cycle time-bound and product-specific station assignment of rigid line structure. A major challenge in the design of matrix-structured assembly systems is the assembly control. While certain approaches, mostly decentral and agent-based, are already capable to assign orders to assembly stations based on the availability of production resources, order release as part of the assembly control has been largely neglected. This is because routing and sequence flexibility lead to temporal uncertainty in the prediction of station-specific capacity requirements. Accordingly, the authors' previous work includes a conceptual methodology for capacity-oriented order release in matrix-structured assembly systems. After implementing this methodology, the actual benefit needs to be determined. For this purpose, the present paper suggests and applies a testing strategy based on the fundamentals of successful testing in software development domain. The testing aims to demonstrate the basic functionality of the implemented methodology as well as to compare it with other order release procedures that have been used for simulations in the context of matrix-structured assembly systems so far. It can be shown that the methodology for capacity-oriented order release in matrix-structured assembly systems achieves better adherence to delivery dates and lead times by anticipating bottlenecks compared to ConWIP control with a random order release. The knowledge gained from the testing strategy contributes to the improvement of order release procedures in matrix-structured assembly systems

Function Analysis for Selecting Automated Machine Learning Solutions

Methods of machine learning (ML) are notoriously difficult for enterprises to employ productively. Data science is not a core skill of most companies, and acquiring external talent is expensive. Automated machine learning (Auto-ML) aims to alleviate this, democratising machine learning by introducing elements such as low-code / no-code functionalities into its model creation process. Multiple applications are possible for Auto-ML, such as Natural Language Processing (NLP), predictive modelling and optimization. However, employing Auto-ML still proves difficult for companies due to the dynamic vendor market: The solutions vary in scope and functionality while providers do little to delineate their offerings from related solutions like industrial IoT-Platforms. Additionally, the current research on Auto-ML focuses on mathematical optimization of the underlying algorithms, with diminishing returns for end users. The aim of this paper is to provide an overview over available, user-friendly ML technology through a descriptive model of the functions of current Auto-ML solutions. The model was created based on case studies of available solutions and an analysis of relevant literature. This method yielded a comprehensive function tree for Auto-ML solutions along with a methodology to update the descriptive model in case the dynamic provider market changes. Thus, the paper catalyses the use of ML in companies by providing companies and stakeholders with a framework to assess the functional scope of Auto-ML solutions

Initiation of therapy for obstructive sleep apnea syndrome: a randomized comparison of outcomes of telemetry-supported home-based vs. sleep lab-based therapy initiation

Purpose: Diagnosis and treatment of obstructive sleep apnea are traditionally performed in sleep laboratories with polysomnography (PSG) and are associated with significant waiting times for patients and high cost. We investigated if initiation of auto-titrating CPAP (APAP) treatment at home in patients with obstructive sleep apnea (OSA) and subsequent telemonitoring by a homecare provider would be non-inferior to in-lab management with diagnostic PSG, subsequent in-lab APAP initiation, and standard follow-up regarding compliance and disease-specific quality of life. Methods: This randomized, open-label, single-center study was conducted in Germany. Screening occurred between December 2013 and November 2015. Eligible patients with moderate-to-severe OSA documented by polygraphy (PG) were randomized to home management or standard care. All patients were managed by certified sleep physicians. The home management group received APAP therapy at home, followed by telemonitoring. The control group received a diagnostic PSG, followed by therapy initiation in the sleep laboratory. The primary endpoint was therapy compliance, measured as average APAP usage after 6 months. Results: The intention-to-treat population (ITT) included 224 patients (110 home therapy, 114 controls); the per-protocol population (PP) included 182 patients with 6-month device usage data (89 home therapy, 93 controls). In the PP analysis, mean APAP usage at 6 months was not different in the home therapy and control groups (4.38 +/- 2.04 vs. 4.32 +/- 2.28, p = 0.845). The pre-specified non-inferiority margin (NIM) of 0.3 h/day was not achieved (p = 0.130); statistical significance was achieved in a post hoc analysis when NIM was set at 0.5 h/day (p < 0.05). Time to APAP initiation was significantly shorter in the home therapy group (7.6 +/- 7.2 vs. 46.1 +/- 23.8 days; p < 0.0001). Conclusion: Use of a home-based telemonitoring strategy for initiation of APAP in selected patients with OSA managed by sleep physicians is feasible, appears to be non-inferior to standard sleep laboratory procedures, and facilitates faster access to therapy

Associated factors and comorbidities in patients with pyoderma gangrenosum in Germany: a retrospective multicentric analysis in 259 patients

Background: Pyoderma gangrenosum (PG) is a rarely diagnosed ulcerative neutrophilic dermatosis with unknown origin that has been poorly characterized in clinical studies so far. Consequently there have been significant discussions about its associated factors and comorbidities. The aim of our multicenter study was to analyze current data from patients in dermatologic wound care centers in Germany in order to describe associated factors and comorbidities in patients with PG. Methods: Retrospective clinical investigation of patients with PG from dermatologic wound care centers in Germany. Results: We received data from 259 patients with PG from 20 different dermatologic wound care centers in Germany. Of these 142 (54.8\%) patients were female, 117 (45.2\%) were male; with an age range of 21 to 95 years, and a mean of 58 years. In our patient population we found 45.6\% with anemia, 44.8\% with endocrine diseases, 12.4\% with internal malignancies, 9.3\% with chronic inflammatory bowel diseases and 4.3\% with elevated creatinine levels. Moreover 25.5\% of all patients had a diabetes mellitus with some aspects of potential association with the metabolic syndrome. Conclusions: Our study describes one of the world's largest populations with PG. Beside the well-known association with chronic bowel diseases and neoplasms, a potentially relevant new aspect is an association with endocrine diseases, in particular the metabolic syndrome, thyroid dysfunctions and renal disorders. Our findings represent clinically relevant new aspects. This may help to describe the patients' characteristics and help to understand the underlying pathophysiology in these often misdiagnosed patients

A Systematic Literature Review On The Suitability Of Cloud Migration Methods For Small And Medium-Sized Enterprises

The COVID-19 pandemic has shown companies that their on-premise infrastructures often reach their limits with a large number of remote accesses. The transition to cloud-based solutions could represent a more efficient alternative. However, many German companies, especially small and medium-sized enterprises (SME), are still hesitant to take this big step of transferring applications to the cloud. For this reason, this paper examines the question of whether existing migration approaches in the analysis phase fit the specific requirements of SMEs. Using a literature review methodology, we first identify and analyze determinant factors for cloud adoption in SMEs. On this basis, we analyze existing methods in the analysis phase for migrations from on-premise software to cloud solutions. We investigate whether these factors are considered in the analysis phase of the approaches and conclude their suitability for SMEs. Of the migration approaches we examined, none included all the factors we identified as relevant to SMEs. Fewer have considered all factors fully and in detail. We present the results of the literature search process in tabular form and conclude this paper with a discussion and synthesis of the literature as well as an outlook on further research fields

Aetiology, comorbidities and cofactors of chronic leg ulcers: retrospective evaluation of 1 000 patients from 10 specialised dermatological wound care centers in Germany

Numerous comorbidities and cofactors have been known to influence wound healing processes. In this multicentre study, clinical data of 1 000 patients with chronic leg ulcers from ten specialised dermatological wound care centers were analysed. The patient cohort comprised 567 females and 433 males with an average age of 69.9 years. The wounds persisted on average for 40.8months and had a mean size of 43.7 cm(2). Venous leg ulcers represented the most common entity accounting for 51.3% of all chronic wounds, followed by mixed-type ulcers in 12.9% and arterial ulcerations in 11.0% of the patients. Vasculitis was diagnosed in 4.5%, trauma in 3.2%, pyoderma gangrenosum in 2.8%, lymphoedema in 1.7%, neoplasia in 1.0% and delayed post-surgical wound healing in 0.6% of the included patients. In total, 70.5% of patients suffered from arterial hypertension, 45.2% were obese, 27.2% had non-insulin dependent diabetes, and 24.4% dyslipidaemia. Altogether 18.4% suffered from metabolic syndrome. Cofactors and comorbidities of patients with chronic leg ulcers have previously been studied but not in detail. Here, we were able to demonstrate the existence of several potentially relevant cofactors, comorbidities of their associations and geographical distributions, which should be routinely examined in patients with chronic leg ulcers and - if possible - treated