Using Microservices to Customize Multi-Tenant SaaS: From Intrusive to Non-Intrusive

Customization is a widely adopted practice on enterprise software applications such as Enterprise resource planning (ERP) or Customer relation management (CRM). Software vendors deploy their enterprise software product on the premises of a customer, which is then often customized for different specific needs of the customer. When enterprise applications are moving to the cloud as mutli-tenant Software-as-a-Service (SaaS), the traditional way of on-premises customization faces new challenges because a customer no longer has an exclusive control to the application. To empower businesses with specific requirements on top of the shared standard SaaS, vendors need a novel approach to support the customization on the multi-tenant SaaS. In this paper, we summarize our two approaches for customizing multi-tenant SaaS using microservices: intrusive and non-intrusive. The paper clarifies the key concepts related to the problem of multi-tenant customization, and describes a design with a reference architecture and high-level principles. We also discuss the key technical challenges and the feasible solutions to implement this architecture. Our microservice-based customization solution is promising to meet the general customization requirements, and achieves a balance between isolation, assimilation and economy of scale

B2C Mass Customization in the Classroom

The purpose of this article is to describe an internet-based mass customization assignment in Operations Management/Supply Chain Management classes where students utilize the Web site of a company that offers a customized product. Students evaluate the user interface, judge the value proposition of the product they demonstrate, and discuss issues of product design, process design and scheduling, inventory management, Supply Chain Management, marketing, and competitors. The students learn about mass customization from both the producer\u27s perspective and the consumer\u27s perspective. Through their own research and the class presentations students are able to develop a better understanding of the implementation requirements and challenges of mass customization. The assignment is highly interactive and has been successfully used in Operations Management and Supply Chain Management courses at under-graduate and graduate levels and at multiple universities. In addition, practitioners interested in implementing a mass customization process can use the assignment as a brainstorming or benchmarking exercise

Feature placement algorithms for high-variability applications in cloud environments

While the use of cloud computing is on the rise, many obstacles to its adoption remain. One of the weaknesses of current cloud offerings is the difficulty of developing highly customizable applications while retaining the increased scalability and lower cost offered by the multi-tenant nature of cloud applications. In this paper we describe a Software Product Line Engineering (SPLE) approach to the modelling and deployment of customizable Software as a Service (SaaS) applications. Afterwards we define a formal feature placement problem to manage these applications, and compare several heuristic approaches to solve the problem. The scalability and performance of the algorithms is investigated in detail. Our experiments show that the heuristics scale and perform well for systems with a reasonable load

The Evolution of Work

The division of labor first increased during industrialization and then decreased again after 1970 as job roles have expanded. We explain these trends in the organization of work through a simple model where (a) machines require standardization to exploit economies of scale and (b) more customized products are subject to trends and fashions which make production tasks less predictable and a strict division of labor impractical. At the onset of industrialization, the market supports only a small number of generic varieties which can be mass-produced under a strict division of labor. Thanks to productivity growth, niche markets gradually expand, producers eventually move into customized production and the division of labor decreases again. The model predicts capital-skill substitutability during industrialization and capital skill complementarity in the maturing industrial economy. Moreover, conventional calculations of the factor content of trade underestimate the impact of globalization because they do not take into account changes in product market competition induced by trade. We test our model by exploiting the time-lags in the introduction of bar-coding in three-digit SIC manufacturing industries in the US. We find that both increases in investments in computers and bar-coding have led to skill-upgrading. However, consistent with our model bar-coding has affected mainly the center of the skill distribution by shifting demand away from the high-school educated to the less-than-college educated.

Consumer Preferences for Mass Customization

Increasingly, firms allow consumers to mass customize their products. In this study, the authors investigate consumers’ evaluations of different mass customization configurations when asked to mass customize a product. For instance, mass customization configurations may differ in the number of modules that may be mass customized. The authors find – in the context of mass customization of personal computers – that mass customization configuration affects the product utility consumers can achieve in mass customization as well as their perception of mass customization complexity. In turn, product utility and complexity affect the utility consumers derive from using a certain mass customization configuration. More specifically, product utility has a positive, and complexity has a negative effect on mass customization configuration utility. The effect of complexity is direct as well as indirect, because complexity also lowers product utility. The authors also find that consumers with high product expertise find mass customization configurations less complex than consumers with low product expertise and that for more expert consumers complexity has a less negative impact on product utility. The study has important managerial implications for how companies can design their mass customization configuration to increase utility and decrease complexity.marketing ;

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF