Vaggelis Giannikas

Product intelligence in industrial control: Theory and practice

This paper explores the evolving industrial control paradigm of product intelligence. The approach seeks to give a customer greater control over the processing of an order – by integrating technologies which allow for greater tracking of the order and methodologies which allow the customer [via the order] to dynamically influence the way the order is produced, stored or transported. The paper examines developments from four distinct perspectives: conceptual developments, theoretical issues, practical deployment and business opportunities. In each area, existing work is reviewed and open challenges for research are identified. The paper concludes by identifying four key obstacles to be overcome in order to successfully deploy product intelligence in an industrial application.

Organizational adoption of open source software

Organizations and individuals can use open source software (OSS) for free, they can study its internal workings, and they can even fix it or modify it to make it suit their particular needs. These attributes make OSS an enticing technological choice for a company. Unfortunately, because most enterprises view technology as a proprietary differentiating element of their operation, little is known about the extent of OSS adoption in industry and the key drivers behind adoption decisions. In this article we examine factors and behaviors associated with the adoption of OSS and provide empirical findings through data gathered from the US Fortune-1000 companies. The data come from each companys web browsing and serving activities, gathered by sifting through more than 278 million web server log records and analyzing the results of thousands of network probes. We show that the adoption of OSS in large US companies is significant and is increasing over time through a low-churn transition, advancing from applications to platforms. Its adoption is a pragmatic decision influenced by network effects. It is likelier in larger organizations and those with many less productive employees, and is associated with IT and knowledge-intensive work and operating efficiencies.

An algorithm for dynamic order-picking in warehouse operations

Warehousing has been traditionally viewed as a non-value-adding activity but in recent years a number of new developments have meant that supply chain logistics have become critical to profitability. This paper focuses specifically on order-picking which is a key factor affecting warehouse performance. Order picking is the operation of retrieving goods from specified storage locations based on customer orders. Today’s warehouses face challenges for greater responsiveness to customer orders that require more flexibility than conventional strategies can offer. Hence, dynamic order-picking strategies that allow for changes of pick-lists during a pick cycle have attracted attention recently. In this paper we introduce an interventionist routing algorithm for optimising the dynamic order-picking routes. The algorithm is tested using a set of simulations based on an industrial case example. The results indicate that under a range of conditions, the proposed interventionist routing algorithm can outperform both static and heuristic dynamic order-picking routing algorithms.

Intelligent logistics

HighlightsWe propose a conceptual model for customer orientation in intelligent logistics.We provide a set of structured requirements for a successful customer-oriented intelligent logistics system.We illustrate the model and structured requirements in the context using example from our current research. The role of logistics in effective supply chain management is increasingly critical, and researchers and practitioners have recently focused their attention in designing more intelligent systems to address todays challenges. In this paper, we focus on one such challenge concerning improving the role of the customer in logistics operations. In particular, we identify specific developments in the systems governing core logistics operations, which will enhance the customer experience. This paper proposes a conceptual model for customer orientation in intelligent logistics and describes a number of specific developments the authors are involved in.

Product Intelligence in Intermodal Transportation: The Dynamic Routing Problem

Intermodalism has been recognised as a promising way to efficiently reduce logistics costs and travel times of shipments in today’s freight industry. Further, it has the potential to add resilience to the transportation network by providing options in the case of disruption. However, intermodalism still faces some operational challenges such as the routing of shipments in the intermodal network and the control of these shipments during their transit. In this paper we focus on the dynamic version of the routing problem in intermodal transportation that is the control of a shipment while it moves in the transportation network. We present an approach (based on the so-called “intelligent product” notion) for efficient dynamic routing and discuss its applicability and differences with current practice.

Product Intelligence in Warehouse Management: A Case Study

The need for more flexible, adaptable and customer-oriented warehouse operations has been increasingly identified as an important issue by todays warehouse companies due to the rapidly changing preferences of the customers that use their services. Motivated by manufacturing and other logistics operations, in this paper we argue on the potential application of product intelligence in warehouse operations as an approach that can help warehouse companies address these issues. We discuss the opportunities of such an approach using a real example of a third-party-logistics warehouse company and we present the benefits it can bring in their warehouse management systems.

Using Customer-related Data to Enhance E-grocery Home Delivery

Purpose: The development of e-grocery allows people to purchase food online and benefit from home delivery service. Nevertheless, a high rate of failed deliveries due to the customer’s absence causes significant loss of logistics efficiency, especially for perishable food. This paper proposes an innovative approach to use customer-related data to optimize e-grocery home delivery. The approach estimates the absence probability of a customer by mining electricity consumption data, in order to improve the success rate of delivery and optimize transportation. Design/methodology/approach: The methodological approach consists of two stages: a data mining stage that estimates absence probabilities, and an optimization stage to optimize transportation. Findings: Computational experiments reveal that the proposed approach could reduce the total travel distance by 3% to 20%, and theoretically increase the success rate of first-round delivery approximately by18%-26%. Research limitations/implications: The proposed approach combines two attractive research streams on data mining and transportation planning to provide a solution for e-commerce logistics. Practical implications: This study gives an insight to e-grocery retailers and carriers on how to use customer-related data to improve home delivery effectiveness and efficiency. Social implications: The proposed approach can be used to reduce environmental footprint generated by freight distribution in a city, and to improve customers’ experience on online shopping. Originality/value: Being an experimental study, this work demonstrates the effectiveness of data-driven innovative solutions to e-grocery home delivery problem. The paper provides also a methodological approach to this line of research.

Adaptive storage location assignment for warehouses using intelligent products

Due to rapidly changing customer preferences, order-picking has become a bottleneck for the efficiency of the order fulfilment process and in turn a burden to the customer satisfaction of warehouse companies. Improved storage location assignment of newly delivered products is one effective method for improving the picking performance. However, most of the available storage policies provide static solutions that do not deal with frequent changes in order demand characteristics. This study aims to identify a potential solution by developing a distributed, adaptive strategy for the storage location assignment problem and follows the product intelligence paradigm for its implementation. The efficiency of such a strategy in real industrial systems is explored via a simulation study using data from a local e-commerce fulfilment warehouse.

The Role of Distributed Intelligence in Warehouse Management Systems

Third-party-logistics warehouses have been gaining popularity in recent years mainly due to their ability to buffer the material flow along a supply chain and consolidate the products from various suppliers, which has a major impact on supply chain efficiency. The issues and challenges in warehouse management are similar in many ways to those faced in manufacturing control, resilience and adaptability being two such issues. Since distributed intelligence (DI) approaches have been extensively studied to address such issues in manufacturing control, this paper examines the possible adoption of DI approaches in warehouse management systems (WMS). Further, the paper discusses the challenges in warehouse management and compares these challenges with the characteristics of manufacturing problems that DI approach is suited to.

Travel behaviour applied in freight transportation using intelligent products

The route planning problem for an order in freight transportation involves the selection of the best route for its transportation given a set of options that the network can offer. In its adaptive (or dynamic) version, the problem deals with the planning of a new route for an order while it is actually in transit typically because part or all of its pre-selected route is blocked or disrupted. In the intelligent product approach we are proposing, an order would be capable of identifying and evaluating such new routes in an automated manner and choosing the most preferable one without the intervention of humans. Because such approaches seek to mirror (and then automate) human decision making, in this paper we seek to identify new ways for dynamic route planning in industrial logistics inspired by the way people make similar decisions about their journey when they travel in multi-modal networks. We propose a new simulation game as a methodological tool for capturing their travel behaviour and we use it in this study. The results show that a simulation game can be used for capturing strategies and tactics of travellers and that intelligent products can provide a proper platform for the usage of such strategies in freight logistics.