Generate and Test for Formulated Product Variants With Information Extraction and an In-Silico Model

The chemical industry is expanding its focus from process-centered products to product-centered products. Of these, consumer chemical products and other similar formulated products are especially ubiquitous. State of the art in the formulated product design relies heavily on experts and their expertise, leading to extended time to market and increased costs. The authors show that it is possible to construct a graph database of various details of products from textual sources, both offline and online. Similar to the “generate and test” approach, they propose that it is possible to generate feasible design variants of a given type of formulated product using the database so constructed. If they restrict the set of products that are applied to the skin, they propose to test the generated design variants using an in-silico model. Even though this chapter is an account of the work in progress, the authors believe the gains they can obtain from a readily accessible database and its integration with an in-silico model are substantial.

The Digital Enterprise as an Emerging Landscape for Universities and Their Operation

This chapter presents a framing discussion around the notion of a digital enterprise in the context of higher education. The chapter makes the assumption that a university like a commercial enterprise can draw significant benefit from acting as a digital enterprise. The discussion indicates that some of a university's existing and historical activities are in line with notions of a digital enterprise. The chapter proposes a framework for assessing the readiness of a university with respect to its actions as a digital enterprise recognising the complexity of domains residing within the confines of a university environment. Critically, the chapter argues that such a future systems project should not only consider positive use cases but also recognise that a digital enterprise may have unplanned and unintentional consequences. Hence, this chapter argues that new forms of governance may also be required alongside the planned journey to a digital enterprise world.

Model-Driven Adaptive Enterprise

Modern enterprises are large complex systems operating in dynamic environments and are therefore required to respond quickly to a variety of change drivers. Moreover, they are systems of systems wherein understanding is only available in localized contexts and is partial and uncertain. Given that the overall system behaviour is hard to know a-priori and that conventional techniques for systemwide analysis either lack rigour or are defeated by the scale of the problem, the current practice often exclusively relies on human expertise for adaptation. This chapter outlines the concept of model-driven adaptive enterprise that leverages principles from modeling, artificial intelligence, control theory, and information systems design leading to a knowledge-guided simulation-aided data-driven model-based evidence-backed approach to impart adaptability to enterprises. At the heart of a model-driven adaptive enterprise lies a digital twin (i.e., a simulatable digital replica of the enterprise). The authors discuss how the digital twin can be used to analyze, control, adapt, transform, and design enterprises.

Orchestrating the C3 Journey of the Digital Enterprise

As businesses embrace digital technologies and drive business growth, this transformation demands a reimagination of their products, processes, and work beyond just “digitalization.” The enterprise starts by capturing diverse sensor data and integrates just-in-time data to achieve “connected” stage. It further uses data along with contextual intelligence to drive integrated decisions in “collaborative” stage. It aspires to share decision making between machines and humans and evolves into “cognitive” stage. In this C3 journey—connected to collaborative, further to cognitive—enterprises need to take advantage of innovative technologies across machines, facilities, and operations in the ecosystem of products, processes, and partners. The authors highlight the nuances and opportunities across the C3 journey focusing on manufacturing value chains. Customers can orchestrate their C3 journey using innovative digital solutions outlined here for information sharing and interactive analytics that will deliver best business results with data-driven decisions.

Materials Design, Development, and Deployment in Manufacturing Industry

The materials and manufacturing industry is undergoing transformation through adoption of various digital technologies. Though the adoption of digital platforms for operational needs is significant, their adoption for core design and development of products and their manufacturing are limited. While the use of physics and data-driven modeling-and-simulation tools is increasing, these are not systematically leveraged for larger benefit. Besides these tools, product design and development requires deep contextual knowledge necessitating systematic capture of data and knowledge. To achieve this, we need flexible digital platforms that enable integration of diverse design domains and tools through a common semantic basis and construction of engineering decision workflows leveraging various simulation tools and knowledge. This chapter builds these requirements through presenting three case studies from the materials manufacturing industry and presents requirements for a digital platform. Finally, one such platform, TCS PREMAP, being developed by the authors is described in some detail.

People and the Digital Enterprise

The transformation of a conventional enterprise from a people-centric model to a technology-centric one has important implications for its human workforce. In this chapter, the authors look at three representative people-related focus areas for the digital enterprise, enhancing workplace wellbeing, enabling continuous learning and compliance to information security. They discuss each of these problems and then look at the technological infrastructure a digital enterprise would require to manage these areas.

Enterprise Modelling in the Digital Age

Our society is transitioning from the industrial age to the digital age, thus also revolutionising the enterprise landscape. In addition, one can observe how the notion of economic exchange is shifting from goods-dominant logic to service-dominant logic, putting the focus on continuous value co-creation between providers and consumers. Combined, these trends drive enterprises to transform continuously. During enterprise transformations, coordination among the stakeholders involved is key. Enterprise models are traditionally regarded as an effective way to enable informed coordination. At the same time, the digital age also provides ample challenges and opportunities for enterprise modelling. The objective of this chapter is therefore threefold. The first aim is to reflect on the role of enterprise modelling for coordinated enterprise transformation. The second aim is to explore the challenges posed by digital transformations to enterprise modelling. The third aim is to reflect on how enterprise modelling itself may benefit from the new digital technologies.

Enterprise Security

As we increasingly depend on technology, cyber threats and vulnerabilities are creating trust issues for businesses and enterprises, and cybersecurity is being considered as the number one threat to the global economy over the next 5-10 years. In this chapter, the authors explain this phenomenon by first describing the changing cyber ecosystem due to extreme digitalization and then its ramifications that are plainly visible in the latest trends in cyber-attacks. In the process, they arrive at five key implications that any modern enterprise needs to be cognizant of and discuss eight emerging measures that may help address consequences of those implications substantially. It is hoped that these measures will play a critical role in making enterprise security more proactive, cognitive, automated, connected, invisible, and risk aware.

Towards Adaptive Enterprise

The key to a successful adaptive enterprise lies in techniques and algorithms that enable the enterprise to learn about its environment and use the learning to make decisions that maximize its objectives. The volatile nature of the contemporary business environment means that learning needs to be continuous and reliable, and the decision-making rapid and accurate. In this chapter, the authors investigate two promising families of tools that can be used to design such algorithms: adaptive control and reinforcement learning. Both methodologies have evolved over the years into mathematically rigorous and practically reliable solutions. They review the foundations, the state-of-the-art, and the limitations of these methodologies. They discuss possible ways to bring together these techniques in a way that brings out the best of their capabilities.

IT Systems for the Digital Enterprise

Modern digital enterprises operate in a dynamic environment where the business objectives, underlying technologies, and expectations from the end-users change over the time. Therefore, developing agile and adaptive IT systems is a critical need for most of the large business-critical enterprises. However, it is observed that the traditional IT system development approaches are not capable of ensuring all desired characteristics. This chapter discusses a set of established concepts and techniques that collectively help to achieve the desired agility and adaptiveness. The chapter reflects on the core concept of model-driven engineering for agility, technology independence, and retargetability; focuses on component abstraction to introduce divide-and-concur and separation of concerns; and proposes the use of variability and the concept of productline for developing configurable and extensible IT system.