Dynamic Multi-Agent Based Variety Formation and Steering in Mass Customization

Large product variety in mass customization involves a high internal complexity level inside a company’s operations, as well as a high external complexity level from a customer’s perspective. To cope with both complexity problems, an information system based on agent technology is able to be identified as a suitable solution approach. The mass customized products are assumed to be based on a modular architecture and each module variant is associated with an autonomous rational agent. Agents have to compete with each other in order to join coalitions representing salable product variants which suit real customers’ requirements. The negotiation process is based on a market mechanism supported by the target costing concept and a Dutch auction. Furthermore, in order to integrate the multi-agent system in the existing information system landscape of the mass customizer, a technical architecture is proposed and a scenario depicting the main communication steps is specified.Product Configuration, Mass Customization, Variety Formation and Steering, Multi Agent System

A Framework for Understanding the Interdependencies between Mass Customization and Complexity

Mass customization is a business strategy that aims at satisfying individual customer needs, nearly with mass production efficiency. It induces a high complexity level because of various customer requirements and a steadily changing environment. However, mass customization has some potential to reduce complexity. The interdependencies between mass customization and complexity are discussed in order to demonstrate that mass customization is not just an oxymoron linking two opposite production concepts, but a business strategy that contributes towards reaching a competitive advantage. On the one hand, mass customization increases the production program, manufacturing and configuration complexities. On the other hand, it contributes to reduce complexity at the levels of order taking process, product and inventories. The main results attained through the analysis are integrated in a comprehensive framework that shows the complexity increasing and complexity decreasing aspects due to mass customization.Mass Customization; Complexity Management; Product Variety

Mass Customization vs. Complexity: A Gordian Knot?

Mass customization is a business strategy that aims at satisfying individual customer needs, nearly with mass production efficiency. It induces a high complexity level because of various customer requirements and a steadily changing environment. However, mass customization has some potential to reduce complexity. These interdependencies between mass customization and complexity form a Gordian knot that should be cut in order to point out that mass customization is not just an oxymoron linking two opposite production concepts, but a business strategy that contributes towards reaching a competitive advantage. On the one hand, mass customization increases the production program, manufacturing and configuration complexities. On the other hand, mass customization can contribute to reduce complexity at the levels of order taking process, product and inventories. The main results attained through the analysis are integrated in a comprehensive framework that shows the complexity increasing and complexity decreasing aspects due to mass customization.complexity; mass customization

Trajectory Optimization of Meteorological Sampling

Swarming involves controlling multiple unmanned aerial systems or UAS in formation through the use of controls and algorithms. Swarm systems may be distributed and not rely on a central controller. As a result, this gives the system the potential to be robust and scalable, allowing for flexibility for the engineers to approach problems differently. Based on a variety of a few models and algorithms, such as artificial potential fields (APFs), agent-based modeling, dynamic data driven application systems (DDDAS), and virtual structures, it may be determined that using a variation of one of these would be the best course of action for formation flight for a swarm of UASs. Choosing the right controller is dependent on what works best for acquiring atmospheric data in a coordinated formation. Current atmospheric data is commonly taken using a weather tower or mesonet. A mesonet is typically a 10m high tower with a pressure, temperature, humidity sensor placed at the top. Deciding which controller can be used to not only take useful atmospheric data, but in many cases replace a mesonet due to mobility and customization is the goal. A wind profile is a transient matter, so using a swarm vs using one drone or a mesonet helps to solve the issues that the latter two run into due to time and space. A swarm can record multiple points at one time due to each agent being a data point representation, whereas a single drone can only account for a single location in time. A swarm using a virtual structure (VS) can cover a variety of amounts of space in a coordinated shape. A meosnet is stationary and only oriented vertically and an uncoordinated group of UAS does not have the capability to operate together. This leaves the capability that a VS swarm has to fill in the gaps or even replace the traditional approaches. An array of sensor packages with mobility, coordinated movement, and endless data points could give the VS swarm the advantage in atmospheric data sampling

Application of product family design for engineered systems in changing market space

The focus of this paper is on the design of an engineered system for a changing market space. Due to the dynamic nature of the customer requirements, the specification of product offerings in a particular market may change. Manufacturers need to strategically design their product portfolio in such a way that their profitability is maximized, while deploying the right number of platforms necessary for deriving product variants. Depending on the system architecture, subsystems can be classified into one of the two types: scalar subsystems and modular subsystems. Each subsystem is defined by various parameters, performance criteria, and physical compatibility constraints. The market demand is modeled as a function of selling price and performance criteria. The objective function is formulated as maximization of total profitability for the current and future markets while meeting the required performance criteria. The profitability of an individual unit is the difference between the selling price and cost of that particular unit. The selling price has been expressed as a linear function of system characteristic and performance parameters. The cost of an individual system is the sum of the cost of all the subsystems involved. The cost of an individual subsystem is a function of parameters of that particular subsystem. Further, different types of technology are considered available at different time periods that impacts the switchover cost. The total profitability is further reduced by the platform development cost of the variants. The complete engineered system level problem is formulated as a non-linear programming optimization problem and solved using the non-linear generalized reduced gradient algorithm. The application of the proposed methodology is demonstrated using a case example of an automotive truck family --Abstract, page iv

Mass Customization vs. Complexity: A Gordian Knot?

Mass customization is a business strategy that aims at satisfying individual customer needs, nearly with mass production efficiency. It induces a high complexity level because of various customer requirements and a steadily changing environment. However, mass customization has some potential to reduce complexity. These interdependencies between mass customization and complexity form a Gordian knot that should be cut in order to point out that mass customization is not just an oxymoron linking two opposite production concepts, but a business strategy that contributes towards reaching a competitive advantage. On the one hand, mass customization increases the production program, manufacturing and configuration complexities. On the other hand, mass customization can contribute to reduce complexity at the levels of order taking process, product and inventories. The main results attained through the analysis are integrated in a comprehensive framework that shows the complexity increasing and complexity decreasing aspects due to mass customization

Mass Customization vs. Complexity: A Gordian Knot?

Mass customization is a business strategy that aims at satisfying individual customer needs, nearly with mass production efficiency. It induces a high complexity level because of various customer requirements and a steadily changing environment. However, mass customization has some potential to reduce complexity. These interdependencies between mass customization and complexity form a Gordian knot that should be cut in order to point out that mass customization is not just an oxymoron linking two opposite production concepts, but a business strategy that contributes towards reaching a competitive advantage. On the one hand, mass customization increases the production program, manufacturing and configuration complexities. On the other hand, mass customization can contribute to reduce complexity at the levels of order taking process, product and inventories. The main results attained through the analysis are integrated in a comprehensive framework that shows the complexity increasing and complexity decreasing aspects due to mass customization