Conclusion

Overall, the aim of this book is to explain nascent topics within innovation analytics and suggest future research directions and meaningful use of data engineering with advanced AI-based techniques that could contribute to radical and incremental product and process innovations. Innovation is a function of performance and time. A higher probability for success of innovation depends on continuous improvements and reduction in time. Based on the discussion and insights offered under the three themes in this book, this chapter summarizes the potential research pathways in the innovation analytics domain. These are only indicative and there are plenty of other research gaps to consider

Introduction

Innovation analytics (IA) is an emerging paradigm that integrates advances in the data engineering field, innovation field, and artificial intelligence field to support and manage the entire life cycle of a product and processes. In this chapter, we have identified several possibilities where analytics can help in innovation. First, we aim to explain using a few cases how analytics can help in innovating new products to the market specifically through collaborative engagement of designers and data. Second, we will explain the use of artificial intelligence (AI) techniques in the manufacturing context, which progresses at different levels, i.e., from process, function to function interaction, and factory-level innovations.</p

Flowshop scheduling with sequence dependent setup times and batch delivery in supply chain

With the emergence of advanced manufacturing and Industry 4.0 technologies, there is a growing interest in coordinating the production and distribution in supply chain management. This paper addresses the production and distribution problems with sequence dependent setup time for multiple customers in flow shop environments. In this complex decision-making problem, an efficient scheduling approach is required to optimize the trade-off between the total cost of tardiness and batch delivery. To achieve this, three new metaheuristic algorithms such as Differential Evolution with different mutation strategy variation and a Moth Flame Optimization, and Lévy-Flight Moth Flame Optimization algorithm are proposed and presented. In addition, a design-of-experiment method is used to identify the best possible parameters for the proposed approaches for the problem under study. The proposed algorithms are validated on a set of problem instances. The variants of differential evolution performed better than the other compared algorithms and this demonstrates the effectiveness of the proposed approach. The algorithms are also validated using an industrial case study