Aspects Regarding the Redesign of a Component of the Vertical Lifting Module Used in Sustainable Production Management

To remain competitive on the market with a developed product, it’s very important to analyze the manufacturing costs and times, from the concept stage of the product. Design for manufacturing and assembly (DFMA) is one of the engineering methods that can be applied to reduce manufacturing costs and times, right from the design stage, without compromising product performance and reliability. The 3D modeling of the tray was made in Solidworks, and for the analysis of it’s manufacture and assembly, the Boothroyd and Dewhurst principle and recommendations from the DFMA software were followed. This paper presents a case study for a subassembly called a tray, used in automatic vertical storage systems. For the redesigned model, substantial improvements were obtained, through cost reductions of 12% and an increase in design efficiency from 4.86 to 12.03. Product analysis using DFMA has proven to be a key point in the development of a product that meets engineers.

Threats to Established Companies from Increasing Digitalization

In today's world, companies have to deal more than ever with increasing globalization/internationalization and enormous competitive pressure. Strong competitive pressure leads to faster and faster innovation cycles, constant technical innovations and programmes for further cost reductions. In recent years, digitalization has added another challenge for established companies. New competitors are blurring industry models through digitalization and offerings based on disruptive innovation, proven business models are no longer suitable from one day to the next and established companies are competing with service or usage offerings. Such service or usage offerings will almost always be considered by the millennial or always-on generation and pose significant risks to established companies.

Design Implications and Opportunities of Considering Fatigue Strength, Manufacturing Variations and Predictive LCC in Welds

Fatigue strength dictates life and cost of welded structures and is often a direct result of initial manufacturing variations and defects. This paper addresses this coupling through proposing and applying the methodology of predictive life-cycle costing (PLCC) to evaluate a welded structure exhibiting manufacturing-induced variations in penetration depth. It is found that if a full-width crack is a fact, a 50% thicker design can result in life-cycle cost reductions of 60% due to reduced repair costs. The paper demonstrates the importance of incorporating manufacturing variations in an early design stage to ensure an overall minimized life-cycle cost.

A Capacity Market for the Transition towards Renewable-Based Electricity Generation with Enhanced Political Feasibility

There is a debate if electricity markets on the basis of energy-only markets ensure a sufficient generation capacity. Various capacity mechanisms are discussed to tackle this potential problem. Capacity auctions with reliability options are seen as one market-based solution. Assuming a perfect energy-only market, this mechanism leads to an equilibrium with an optimal capacity mix. This optimum is missed if there are distorted price signals at the electricity market. This is a serious problem since, despite substantial cost reductions, renewable-based electricity generation still depends on subsidies, which are not internalized at electricity markets. We develop a capacity market that internalizes subsidies for RES without direct intervention in the electricity market. The result is an endogenous discrimination of capacity prices, which enhances acceptance for a capacity market. Arising incentives direct the capacity mix to an equilibrium where discriminated prices converge to one uniform capacity price. The equilibrium is the optimal answer of fossil capacity to RES-based electricity generation.

Applying Endogenous Learning Models in Energy System Optimization

Conventional energy production based on fossil fuels causes emissions that contribute to global warming. Accurate energy system models are required for a cost-optimal transition to a zero-emission energy system, which is an endeavor that requires a methodical modeling of cost reductions due to technological learning effects. In this review, we summarize common methodologies for modeling technological learning and associated cost reductions via learning curves. This is followed by a literature survey to uncover learning rates for relevant low-carbon technologies required to model future energy systems. The focus is on (i) learning effects in hydrogen production technologies and (ii) the application of endogenous learning in energy system models. Finally, we discuss methodological shortcomings of typical learning curves and possible remedies. One of our main results is an up-to-date overview of learning rates that can be applied in energy system models.