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This chapter discusses the empirical data analysis that will form the basis of the early pricing framework. It focusses on the complexity of furniture production and describes the historical production data collected from companies, along with the potential applications of machine learning for knowledge management purposes. The chapter then presents the results of machine learning for early cost estimation as part of a lean innovation that is affordable and accessible for small and medium-sized enterprises (SMEs). Finally, the chapter describes an experiment on the structured expert evaluation methodology, which shows that a well-formed panel of experts can increase the predictive power of machine learning solutions, particularly at extreme points.

The chapter is designed to discuss the preconditions for the competitiveness of the furniture industry, global networks and regional perspectives, as well as the competitive advantages of different regions such as the USA, Europe and the East. The challenges created by customisation and the needs of consumers for individual products are also discussed. As consumers become more and more focussed on furniture designed exclusively for them, the furniture business has to reorient its production and has to deal with a number of management issues. It is necessary to reconsider not only how to involve consumers but also how to keep prices competitive because even for an individual order, the customer is less and less willing to pay more. The issue of new product development is also discussed. It delves into the management of furniture companies, the characteristic organisational structures, and management models that could ensure the sustainability of the business. Particular attention is paid to the digital issues of furniture manufacturing and enterprise resource planning (ERP) in particular. An examination of how the furniture sector evaluates prices and costs, which are the most popular methods and which can be used for forecasting, looks at the most important global trends. Such cost estimation methods as cost-based, competition-based, analogous-based, and expert-based are discussed, highlighting the limits of their applications. Then discusses current trends and the current IT supply, which unfortunately does not fully meet the needs of customised furniture production, and digitisation within a small company becomes more difficult. So, companies have to recognise the limits of digitisation.

The purpose of this paper is to present research on the flight demonstration of avionics technology for CS-23 commuter category aircraft. The Integrated Mission Management System (IMMS) is designed to reduce pilot workload by aggregating hazard information from multiple domains (airspace, traffic, weather and terrain) and automatically prefiltering this data to display only hazards relevant to the flight plan, from origin to destination. This paper details the design of the IMMS, along with the process of the integration on aircraft and flight demonstration results.

The IMMS integrates several technologies, including the Advanced Weather Awareness System, Tactical Separation System, Compact Computing Platform and Flight Reconfiguration System. Hazards are consolidated in a Unified Hazard Database (UHD) and assigned severity levels, providing automated hazard filtering and path planning.

Simulations and flight tests demonstrated that the IMMS effectively reduces the information displayed to pilots in real-time without loss of critical safety data. Feedbacks from test pilots on IMMS usage, as well as suggestions for improving the multi-source Graphical User Interface, are also discussed.

Limitations of the UHD were identified, offering insights into potential expansions to support more efficient automatic flight planning. The technology was validated through extensive laboratory testing and real-world flight trials, achieving Technology Readiness Level 5. This validation demonstrated how the severity of hazards can be linked to their transparency level on the display, with the aim of reducing information overload.

The IMMS shows potential to be ground-breaking system in the CS-23 aircraft category, autonomously supporting route planning and flight execution while adapting to in-flight weather changes and ensuring tactical separation from other aircraft. It also shows that multi-domain hazard information can be processed on limited on-board avionics systems.

This study highlights the importance of Hardware-In-The-Loop testing in verifying new technologies and mitigating risks related to software reliability, flight demonstrations and system integration.