The effect of variability modeling on requirements satisfaction for the configuration and implementation of off-the-shelf software packages

An industrial experience of the use of a method for discovering customer requirements with which to configure an off-the-shelf software package for implementation is reported. The method uses an adapted form of product variability model to provide common ground between the customer and supplier about requirements and capabilities. An associated decision support software tool guides the supplier and customer through a model-based walkthrough to discover new requirements, based on equivalent capabilities described in the product variability model. We applied the method in the work processes of the commercial provider of a software-based learning management system, and collected quantitative and qualitative data from supplier-customer interactions. Our first experiences with the method led to an increased exposure and expression of customer requirements in the customer-supplier dialogue, compared to the baseline dialogue during software package demonstrations. The paper also reports some first lessons learned to improve the method and adopt its use with other software supplier organizations

Defects in Product Line Models and How to Identify Them

This chapter is about generic (language-independent) verification criteria of product line models, its identification, formalisation, categorization, implementation with constraint programming techniques and its evaluation on several industrial and academic product line models represented with several languages

Powertrain Assembly Lines Automatic Configuration Using a Knowledge Based Engineering Approach

Technical knowledge and experience are intangible assets crucial for competitiveness. Knowledge is particularly important when it comes to complex design activities such as the configuration of manufacturing systems. The preliminary design of manufacturing systems relies significantly on experience of designers and engineers, lessons learned and complex sets of rules and is subject to a huge variability of inputs and outputs and involves decisions which must satisfy many competing requirements. This complicated design process is associated with high costs, long lead times and high probability of risks and reworks. It is estimated that around 20% of the designer’s time is dedicated to searching and analyzing past available knowledge, while 40% of the information required for design is identified through personally stored information. At a company level, the design of a new production line does not start from scratch. Based on the basic requirements of the customers, engineers use their own knowledge and try to recall past layout ideas searching for production line designs stored locally in their CAD systems [1]. A lot of knowledge is already stored, and has been used for a long time and evolved over time. There is a need to retrieve this knowledge and integrate it into a common and reachable framework. Knowledge Based Engineering (KBE) and knowledge representation techniques are considered to be a successful way to tackle this design problem at an industrial level. KBE is, in fact, a research field that studies methodologies and technologies for capturing and re-using product and process engineering knowledge to achieve automation of repetitive design tasks [2]. This study presents a methodology to support the configuration of powertrain assembly lines, reducing design times by introducing a best practice for production systems provider companies. The methodology is developed in a real industrial environment, within Comau S.p.A., introducing the role of a knowledge engineer. The approach includes extraction of existing technical knowledge and implementation in a knowledge-based software framework. The macro system design requirements (e.g. cycle time, production mix, etc.) are taken as input. A user driven procedure guides the designer in the definition of the macro layout-related decisions and in the selection of the equipment to be allocated within the project. The framework is then integrated with other software tools allowing the first phase design of the line including a technical description and a 2D and 3D CAD line layout. The KBE application is developed and tested on a specific powertrain assembly case study. Finally, a first validation among design engineers is presented, comparing traditional and new approach and estimating a cost-benefit analysis useful for future possible KBE implementations

Developing Supply Chain Agility for the High-Volume and High-Variety Industry

Supply chains are under pressure to meet performance expectations under conditions in which access to the global network of suppliers and customers is fluid. Most studies accept the importance of agility to enhance performance using flexibility as a key dimension. Moreover, based on literature and empirical implications, it is essentially noticeable that there is an agreement on the need for flexibility in manufacturing to address both internal changes at the manufacturing echelon (e.g., a variation of process times) and external uncertainties (e.g., availability of ingredients, delivery schedules).However, there is a lack of adoptable metrics of manufacturing flexibility that can be used to evaluate manufacturing flexibility’s impact to enhance TH and reduce cost, both at the manufacturing echelon and the supply chain as a system as well as its impact on other echelons. Therefore, focusing on manufacturing flexibility as a competitive strategy induces a driving force for the success of the performance of supply chains. The purpose of this research is to present an applicable methodology for the evaluation of flexibility in a supply chain called Flexible Discrete Supply Chain (FDSC). The FDSC structure consists of a supplier, manufacturer, distributor, and customer as its conceptual model. Two main performance indicators – TH and cost are used to study the FDSC performance. This study utilizes four dimensions: volume, delivery, mix, and innovation (VDMI) flexibility. Quality function deployment is used to translate the dimensions of flexibility to key metrics that can be controlled in a discrete-event simulation (DES) model. The DES model is used to generate data, and for configuring VDMI metrics. The data is used for further sensitivity analysis. The developed methodology is verified and validated using data from a real case study. It is applicable to all supply chains within the FDSC criteria. This study contributes to the body of knowledge of supply chain flexibility through technical, methodical, and managerial implications. It clearly illustrated scenarios and provided guidelines for operations managers, to test among VMDI flexibility to maximize TH constrained by cost. Key directions for future research are identified