Now that it has become possible to monitor the conditions of berries during transit, this PhD project aims to explore how decision-makers in the supply chain may use this real-time data to make in-transit decisions that preserve the quality of the berries.
Over the last decade, food supply chain stakeholders have been implementing real-time data collection, enabling them to record, verify and track a product’s entire history through the length of the supply chain.
The current challenge for the Australian food industry is to switch from gathering and analysing data of past events into collecting real-time data and delivering insights on what is happening in real-time in food supply chains.
Sensors and the Internet of Things (IoT) could play an important and relevant role in Australian berry supply chains (ABSC). During transit, real-time data could be sent at regular intervals to ensure the highest shelf life possible, preserving quality and ensuring food safety.
As berries and other perishable produce transit through the different stages of the supply chain, their quality could be compromised. Preserving the freshness of berries during transit, as they traverse large distances across different Australian states and territories, is of great importance to all stakeholders in the supply chain.
Australian agrifood technology solution providers are looking for more efficient ways to analyse real-time data to improve customer service levels and achieve higher revenues. The goal is to obtain real-time condensed information, analysed and readable that could enhance business operations. Using real-time data to preserve the quality of the berries could increase businesses performance and profits.
The Australian Government and Australian food regulators seek to transform and digitalize current perishable food supply chains. Australian perishable food stakeholders are investing millions of dollars in technologies that can reduce the amount of delivery time wasted during transit. Now, real-time monitoring of the goods flow is possible from early stages in the supply chain, such as farms, when sensors are placed in the pallets to provide constant data about the location, temperature, humidity and light exposure.
Sensors and the Internet of Things could play an important and relevant role in Australian berry supply chains (ABSC). During transit, real-time data could be sent at regular intervals to ensure the highest shelf life possible, preserving quality and ensuring food safety. Food safety becomes critical as it can represent a threat to human health and reputational damage for suppliers.
As a result of produce damage and spoilage due to inadequate conditions during transit, consumers are not willing to buy highly perishable produce, berries for example, if they do not look fresh. Among all the products that belong to the cold chain category, berries are considered highly perishable and are known for having a very short shelf life. Berry quality decreases when the necessary conditions are not fulfilled properly during harvest, storage, and transit along the whole fresh supply chain.
Preserving the freshness of berries during transit, as they traverse large distances across different Australian states and territories, is of great importance to all stakeholders in the supply chain.
The research question being explored here is this:
How can decision-makers use real-time data about transit conditions and location to make in-transit interventions that help preserve the quality of berries in Australian supply chains?
Identify financially feasible opportunities for in-transit interventions that use real-time data to help preserve the quality of berries in Australian supply chains.
- Identify opportunities of making in-transit interventions that help preserve the quality of berries during transit.
- Develop a model to simulate a typical Australian berry supply chain, including the impact of diverse in-transit conditions and interventions on the quality of berries and the supply chain costs.
- Evaluate (using the model developed in Objective 2) the magnitude of the potential benefits and costs of the in-transit interventions (identified in Objective 1), in order to identify the most promising ones.
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