The modern manufacturing process encounters the challenges of organization and control in low-volume, high-quality personalized production. The complexity of the new requirements and the increasing use of technology characterize the integration of the entire spectrum of manufacturing activities, from order booking through design, production, and marketing.

Two terms from this book define the featured manufacturing systems: holonic and multiagent. Holonic is from holon, which A. Koestler derived from the Greek word for “whole,” in 1967, to refer to the building block of a process that imitates, in some aspects, the organization of biological systems. Holons integrate the information and physical parts of processing and can be in a hierarchy. Multiagent systems, as evolved in artificial intelligence (AI), indicate a distributed solution where multiple autonomous (software) agents cooperate. According to the volume’s editors, service-oriented architecture (SOA) is the new glue of the field, able to integrate the two approaches in the newly proposed service-oriented enterprise architecture.

The papers are from presentations given at the Second International Workshop on Service Orientation in Holonic and Multi Agent Manufacturing and Robotics (SOHOMA). The book contains 24 chapters by more than 60 authors, organized in four parts: “Holonic and Multi-Agent Systems for Manufacturing,” “Intelligent Products and Product Driven Manufacturing,” “Service Orientation in Manufacturing Management Control,” and “Distributed Intelligence for Sustainable Manufacturing.”

With so many papers, I will not try to summarize them all. Instead, I will choose one paper from each part--hopefully the one that best introduces the topic for that part and gives some background--to look at in some detail. The other chapters report on more technical aspects.

In Part 1, chapter 1, “A Collaborative Framework Between a Scheduling System and Holonic Manufacturing Execution System,” by Novas et al., shows the integration of a centralized scheduling system (SS) and a decentralized manufacturing execution system (MES). The scheduler is implemented with constraint programming and generates a good, though not necessarily optimal, schedule. The holonic MES is agent based. The systems interact through data exchange. When disruptions occur, the prediction capabilities of the MES make it possible to provide the SS with the likely future state at a given time point, so the SS can generate a new schedule. This new schedule is automatically taken by the MES as the new guidance. In the illustrated case study, different performance measures are given, and it is of interest to see that the balance between stability and makespan (the elapsed time for a production process) is a matter of setting the level of the guidance parameter.

Part 2 addresses the concept of intelligence in products and in processes. The first chapter in this part is chapter 7, “Intelligent Products in the Supply Chain – 10 Years On,” by McFarlane et al. It is an interesting overview of the last ten years, from the introduction of the intelligent product model to the present. The two ways of implementing intelligent products, through intelligence in the object or intelligence in the network, have received different emphasis in industry, with most of the advancements concentrated on the latter, using multiagent systems and holonic manufacturing systems. The authors argue that emerging technologies such as smart objects and the Internet of Things are in fact pursuing the idea of putting intelligence in the objects. Manufacturing could benefit from intelligent objects in a variety of business challenges, including energy policy, pollution reduction, new shopping habits, and logistics.

Part 3 explores the service orientation approach, and the chapters in this part are more oriented toward information technology (IT). The task involves rethinking the business process in manufacturing companies as a disaggregation of loosely coupled services to be provided by a distributed information system. The first chapter of this part, chapter 13, “Complex Manufacturing and Service Enterprise Systems: Modeling and Computational Framework,” by Babiceanu, introduces the scenario and the effort involved in building new enterprise systems. The author makes the point that engineering complex enterprise systems requires extensive modeling and simulations to analyze systems that often exhibit behaviors that are not immediately understandable. An identification model is needed to allow for the prediction of behavior and the simulation of stochastic events.

Part 4 describes research in sustainable manufacturing based on distributed intelligence. Among the topics covered, from policies designed to reduce energy consumption to the use of gestures to support natural interaction with robots, I was intrigued by the notion of exploiting knowledge in manufacturing systems. Chapter 23, “Knowledge-Based Technologies for Future Factory Engineering and Control,” by Legat et al., reports on an ongoing and partially deployed project in the automotive domain. The project addresses two new challenges of quickly adapting the operation strategy to the market and handling production disruption in a knowledge-based scenario. Knowledge here means ontologies for event definition and diagnostic reasoning. According to the authors, even though the increasing complexity of manufacturing systems will make them more costly and error-prone, the reduction of the time to market of the products justifies their evolution.

A subject index, which I found inadequate, completes the volume.

The book addresses readers with an interest in production organization and a basic knowledge of informatics. Computer science readers may consult the book to better understand the current driving themes in the manufacturing world. It is intended for researchers, with some possible student material for business schools.

In conclusion, this book offers discussions on a wide range of open issues in manufacturing systems. Most of the underlying work is being done in research centers. In fact, only four of the chapters have a co-author from industry. The value of the book is not in proposing new methods, but in proposing solutions to real organizational problems. It presents opinions, some of which are contradictory, since many choices remain open--for example, the choice between the simulation of stochastic events and knowledge-based actions, or between symbolic or probabilistic virtual models of a plant. Those themes are still debated in research and academia, where the presented prototype systems are developed. The unanswered question here is: Why are they not already in everyday use?