The project is performed in collaboration between three Norwegian research institutes and industrial actors representing all parts of the supply chain, from fresh food and packaging producers to retailers. This paper describes a pilot case study involving the supply chain of fresh salad, in which an important focus is on how greenhouse gas emission reductions can be achieved and measured, as part of an overall increased supply chain efficiency.
Food and supply chains
Since fresh food is highly perishable, efficient production planning and supply chain management is crucial to minimise waste. In the Norwegian grocery sector today, production planning and supply chain control is often based on historic demand and event information such as past sales and forecasts, leading a time gap between sudden events and corrective actions . Thus, there is substantial improvement potential with respect to responsiveness, stock turnover, and lead-times. In addition, ineffective information exchange and lack of visibility in the supply chain may lead to poor forecast quality and additional loops for adjustment and operational control . Further, actors are often reluctant to share information with others in the supply chain for fear that it might disrupt the supplier-customer power balance.
These issues lead to less efficient supply chains where production is not based on demand, total costs are higher than necessary, and delivery service for customers poorer. However, just as important is the waste produced and its associated environmental problems. The amount of edible food waste is substantial, with most of it occurring in households, and a considerable amount at the retailers’ . A British study shows that 61% of household food waste is avoidable and could have been eaten if it had been managed better . A substantial part of this waste is can be linked to lead times that are too long and the associated short shelf life.
The most significant environmental effects related to food waste are not the loads related to waste treatment, but rather the inherent upstream effects related to cultivation of crops and livestock, processing of these, production of additives etc. . When food is wasted, all activities upstream and their related emissions are in vain. In other words: Avoiding waste through better utilisation of food means avoiding unnecessary GHG emissions.
Introducing demand-driven production and replenishment will enable reduction of lead times and increased delivery performance, thus leaving more time for consumers to consume the product until it reaches its expiry date.
Estimating impacts in a case study
In the Smart Vareflyt project three research institutions (SINTEF, Ostfold Research and the RFID Innovation Centre) are working closely with a number of leading fresh food manufacturers, packaging manufacturers, wholesalers and retailers in the Norwegian grocery industry. The objective is to collaboratively develop the supply chain control models that are necessary to support the application of RFID technology. Concepts from supply chain management are used to redefine collaboration among the actors . focusing on developing:
- New control concepts, principles and algorithms
- Unified supply chain control models as opposed to individual actor control
- Suitable collaborative models and contract types
Parallel to this innovation process, work is being done to estimate the potential effects of the implementation of RFID enabled demand-driven supply chains; and to further down the line enable the actors to register and monitor these changes. These effect estimations (i.e. indicators) will cover efficiency both in terms of logistics and the environment.
For the environmental effects, the focus lies on two main topics both having strong correlation to climate gas emissions, namely resource efficiency and distribution efficiency:
- Resource efficiency:
- The food waste
- Packaging, especially regarding Returnable Transport Items
- Distribution efficiency
Based on a theoretical framework, a number of indicators for capturing such effects will be integrated into an effect measurement system and methodology.
The environmental effects framework is based on a life cycle approach for improvement of environmental profile for producing, packaging and distribution of food products: ‘Environmental Value Chain Assessment’  and climate gas accounting . This will constitute the basis for the indicator system and enable a presentation of results on the salad case level, i.e. estimated impacts on environmental indicators resulting from changes in the supply chain.
Results and Conclusions
Through the Smart Vareflyt project companies were given time to learn about the new enabling technology and its possibilities and limitations through practical implementation exercises. In the later phases, focus has been shifted to the benefits of sharing information across the supply chain. The actors involved are demonstrating a growing readiness to increase collaboration and integration with supply chain partners.
The selected effect indicators are modelled in a specific supply chain case. Changes in GHG emissions will be estimated for a range of causes, such as reduced product loss and a variation in transport intensity.
At present, the hypothesis is that GHG reductions may be achieved through the proposed changes in the supply chain. The connection between the commercial and environmental gains makes the case convincing and increases the probability of enduring emissions reductions. There is, however, a danger that the changes will result in increased transportation and one therefore needs to ensure that the GHG reductions through reductions in food waste are not offset by GHG increases from increased transport.
 Dreyer et al. (2008): Real-Time Supply Chain Planning and Control – A Case Study from the Norwegian Food Industry, Proceedings of APMS 2008, Helsinki, Finland
 Hanssen & Olsen (2008): Mapping of food waste – pre-project for NorgesGruppen, Øsfoldforskning,
 Ventour, (2008): The food we waste. WRAP.
 Rubach & Hanssen (2002): Nordic Methods for documentation of packaging optimisation. Proposal for a Nordic method – Benchmarking System. Østfoldforskning
 Sinden. (2008): PAS 2050:2008. Specification for the assessment of the life cycle greenhouse gas emissions of goods and services, BSI