The purpose of this paper is to present a framework for intelligent goods and illustrate how it can be applied when modeling intelligent goods as agents. It includes a specification of different levels of capability connected to the goods, which is based on a number of capability dimensions. Additionally, three specific intelligent goods services related to transport are presented. We show how these services can be modeled as agents and how they relate to the intelligent goods framework. A discussion of different physical locations of service information and processing is also included.

This paper presents and evaluates a new method for how emissions from freight transport routes with single or several points of loading and unloading, can be allocated to individual consignments. The method, called Dedicated Distance Proportional Allocation (DDPA), has been developed based on a literature review, discussions with logistics providers, and analysis. DDPA is designed to have low data processing requirements and be easy to explain to actors involved. Furthermore, it supports several levels of information availability, and accounts for any set of vehicle-limiting factors, as well as prepositioning/repositioning. DDPA has been evaluated in simulations with different levels of information availability, together with three existent allocation methods: the Equal profit method (EPM), the CEN EN16258:2011 standard and the Greenhouse gas (GHG) protocol. The simulations show that the GHG protocol under-allocates the total amount of emissions, on average. EPM and DDPA achieve equal relative savings, whereas for CEN EN16258:2011 and the GHG protocol, relative savings vary, on average. When DDPA is used with low level of information availability, an error is introduced which can be reduced by applying compensation factors. Since DDPA accepts low information availability, the Intelligent Products concept can be applied for computing and storing emissions allocations, at the time of unloading. The results from this study can be used for further development and implementation of consignment allocation methods. Furthermore, by combining DDPA with other environmental load approaches for other parts of a product’s life cycle, a complete life cycle assessment of the product’s environmental impact can be obtained.

The durability of perishable food varies due to different storage and handling conditions during the supply chain as well as final consumer activities. If the durability of the individual products can be estimated, dynamic expiry dates may be developed and used to prevent food waste, ensure quality, and improve supply chain activities etc. Depending on the system architecture used for such a service, different qualities can be obtained in terms of usability, accuracy, security etc. This paper presents a novel approach for how to identify and select the most suitable system architectures of a dynamic expiry date service. The approach is illustrated by focusing on one of the potential user groups, the supply chain managers. The approach consists of three steps: (i) identify the potential architectures, (ii) filter out the least relevant candidates by applying a specified set of principles, and (iii) perform an analytic hierarchy process (AHP) based on a set of quality attributes.

Purpose: To investigate the possibilities, risks and requirements of a dynamic shelf life service – a technological innovation focusing on minimizing food waste in supply chains (SC). Design/methodology/approach: Semi-structured, open-ended interviews with SC actors have been used to identify the requirements, possibilities and risks with a dynamic shelf life service. Field tests have been conducted to investigate practical implications and effects of small temperature variations on shelf life. The field tests involved sensors based on Bluetooth Low Energy (BLE) and Radio Frequency Identification (RFID). Findings: The results show that a DSLP service holds great potentials. The field tests revealed that shelf life predictions are sensitive to small temperature differences along the cold chain. Results from the interviews confirm the importance of accuracy. The interviews also emphasize the importance of sharing costs among the involved actors. Research limitations/implications: Key aspects from 11 SC actors concerning dynamic shelf life prediction in cold chains are provided. The field tests involve a SC from production to household. Practical implications: Implementation of a dynamic shelf life service can increase visibility and information flow within SCs. The system can also be integrated with companies’ business systems creating new business opportunities and reducing manual work by automatically alerting quality fluctuations of food products. Original/value: Quantitative and qualitative data from 11 SC actors are provided. This information, together with the experiences reported from the field tests, have the potential to help replacing dysfunctional date labelling systems and reduce food waste.