This proceedings volume includes 51 papers on logistics that deal with tagging, planning, routing, and scheduling in the supply chain. The drivers for an increase of complexity in the supply chain start with factors that are actually outside of the supply chain proper. These are shorter product life cycles caused by rapid innovation and fast-changing product demand from the market due to increased competition. These market pressures force the manufacturers to specialize in their core competencies while at the same time require outsourcing of secondary tasks at all costs. Outsourcing requires the manufacturer to become an integrator of multiple companies into the supply chain, which in turn leads to an increase in complexity.

The logistics of yesteryear dealt simply with the movement of materials in the supply chain at large, whereas today just-in-time production and delivery, shipping algorithms, up-to-the minute monitoring, and inventory management contribute to the new dynamics in logistics. With the advent of predictive analytics and the Internet of Things (IoT), the dynamics of logistics have increased exponentially. Add to these developments new paradigms gleaned from animal behavior and sophisticated modeling. As a result, supply chains with their underlying dynamics have become nearly unmanageable without sophisticated tools, which, as an additional benefit, can help companies carve out a significant competitive advantage.

The volume includes two invited keynotes (Part 1, “Future of Manufacturing Utilizing Tagging Technology” and “Fault Detection in Dynamic Vehicle Routing”) and 49 refereed papers whose scope is “the identification, analysis, and description of the dynamics of logistic processes and networks” (page v). The papers are grouped into parts according to areas of specialization in logistics research: Part 2, “Transport Logistics and Dynamic Routing”; Part 3, “Production Logistics”; Part 4, “Modeling and Simulation”; Part 5, “Identification Technologies”; Part 6, “Mathematical Modeling”; Part 7, “Information, Communication, Risk, and Failure”; Part 8, “Autonomous Control”; Part 9, “Global Supply Chains”; and Part 10, “Future Internet of Things.”

These proceedings provide a snapshot of the panoply of research endeavors that have become part of the new dynamics in logistics. The audience for this book, researchers and practitioners working in logistics and supply chain management, may find a different degree of relevance and interest in the individual contributions based on their areas of expertise, but will find valuable information on very specific topics in logistics and solid starting points for further research.

The key areas in logistics are tagging, planning, routing, and scheduling. Investigating these aspects of logistics and their interplay is pervasive in the proceedings. Bennett, in her opening paper on the future of manufacturing (pages 3-11), discusses the challenge of using real-time data to tailor manufacturing to just-in-time fulfillment of orders. As more stakeholders are added to the supply chain, the manufacturer becomes a broker of services to maneuver the goods in the supply chain. She sees radio frequency identification (RFID) implementations in dynamic environments as being challenging for technological reasons, but needed from product inception to product disposal. Her discussion of decision algorithms includes taking into account external factors such as the weather to be considered for process flow.

This idea of overlaying dynamic algorithms onto static ones is discussed further in the paper on “Fault Detection in Dynamic Vehicle Routing Operations” by Novaes et al. (pages 13-32). In a network of sensors, unexpected events such as traffic congestion, weather, and road conditions should be handled in real time. The authors employ a mathematical model to simulate 20,000 replications of trucks moving parts to an assembly factory with 20 percent of traffic disruptions during working days. They propose an integrated logistics service consisting of individual agents that monitor congestion, weather, and accidents. Agents depend on sensors and can communicate with each other to find an ad hoc solution. Novaes et al. show convincingly how mathematical modeling can lead to streamlining vehicle routing under adverse conditions.

While a traveling salesman problem can be solved by an algorithm that finds the optimal static routing between multiple delivery stops, the agent-based model adds an optimal dynamic routing dimension to the operations. Such a multiagent-based approach is also discussed in the paper by Wu et al. dealing with intermodal transport (pages 35-47) and with reference to an intelligent product approach by Giannikas (pages 59-69) that I think is isomorphic to the agent-based approach since it gives autonomy to the individual entity, that is, product or object, and makes decentralized decisions on its own. The ideas of smart object, intelligent product, and so on are the results of developments that led to the Internet of Things (IoT).

With regard to the IoT, Windelband et al. (pages 545-556) not only give a historical perspective (IoT is a confluence of complementary technologies), but also propose a taxonomy of the depth of diffusion as it relates to IoT. Windelband et al. introduce the notion of levels each characterized by the amount of technology employed: Level 0 has no technology, Level 1 uses Auto-ID identification, Level 2 employs a sensor network, and Level 3 consist of embedded systems with decision-making capabilities. They then characterize each level using five different criteria: energy supply, connectivity, information processing, level of aggregation, and location of intelligence (page 550). Subsequently, they survey logistics companies based on the four levels and the diffusion of the five criteria and arrive at an assessment that most companies are at Level 1. While such an assessment would surely change over time, they make two interesting observations based on their research: IoT technologies start out getting used inside of a company first, and the level of adoption of IoT leads to a more meaningful assignment of skilled workers since the lower-level tasks get automated and the workers have to advance their skill sets accordingly to remain relevant. IoT has severe ramifications on the workforce since they are required to be one step ahead of their obsolescence.

The discussion above is but one example of how the proceedings allow for a multitiered discussion of a subject area, starting from practical considerations about what components companies should consider to be part of their logistics challenge to the mathematical verification of an algorithm (as shown by Knollmann and Windt) that can quantify due date reliability with regard to the lead time syndrome (pages 313-325). Topics are presented in a “threaded” fashion; that is, one subject area is investigated from practical, theoretical, technological, and mathematical viewpoints in multiple papers to afford the reader a 360-degree view of a topic.

The proceedings succeed not only in providing the reader with a starting point for more in-depth research in specific areas, but also in painting a broader picture of the dynamism in logistics, especially in three burgeoning research areas for the future: next-generation RFID, predictive analysis, and IoT. Unfortunately, there is no index for the entire book, making it hard to cross-reference topics that are discussed in various chapters (for example, an important subject such as RFID requires the person interested in that subject/keyword to comb through the table of contents to find mention of it in various parts of the book). However, this shortcoming may also have piqued someone’s interest with regard to other areas of the diverse field of logistics.