A third of all raw materials and food produced across the globe spoils before getting to their targeted consumers or end-users. According to the United Nations, this amounts to approximately 1.3 billion metric tons of food wastage and approximately 40% of this wastage occurs during transportation across supply chains. In a time of increasing inflation, solving the logistics challenges attached to the food and beverages manufacturing industry has become a priority.
Insight into the causation factors for food waste provides a starting point for successfully tackling waste across the supply chain. Today, several reasons have been provided and they include:
- Variable harvesting conditions
- Temperature variations within transportation assets
- Delays across the supply chain and
- Inadequate monitoring processes to track supply chain movements
The integration of digital transformation solutions across today’s logistics and supply chain systems introduces Industry 4.0 concepts to managing supply chains. These technology solutions provide food and beverages manufacturers with the tools to implement solutions such as real-time monitoring, condition monitoring, optimized scheduling, and data-driven decision-making to eliminate waste. In this post, insight into the application of digital technology to developing smart supply chain processes will be provided.
Capturing Data across the Supply Chain
Leveraging automation across the supply chain starts by capturing the data required for automated decision-making. The integration of Industrial IoT (IIoT) within logistics lines and transport carriers provides excellent insight that can solve a few of the wastage causation factors highlighted above. IIoT sensors can capture temperature variations within transportation assets to help supply chain managers lower or increase temperatures when required. Edge sensors such as RFIDs and barcode trackers also provide geo-location support to help with planning.
An example of the tracking capabilities of IIoT is Bosch’s use of its IoT framework to track the health status of raw materials transferred from Puerto Rico to Hamburg. In this case, banana’s where the materials and to ensure they arrive at their destinations in optimal condition, specific transporting temperatures and their shelf-life must be known. The deployment of IIoT devices within cases of bananas allowed the enterprise to transport this perishable commodity in its optimal condition to the shop floor.
Communicating across the Supply Chain
Data collection is the first step to implementing a smart supply chain or Logistics 4.0. In most cases, the captured data must be analyzed and results communicated in real-time to make decisions. In situations where edge devices are used, decentralized decision-making ensures a level of automation. Going back to the IIoT use case in tracking the health status of bananas, the optimal transportation temperature for this raw material is 11 degrees Fahrenheit. If the internal temperature falls below this threshold, the device can trigger a notification to increase the heat from the refrigeration systems – but this isn’t always the case.
In scenarios where interconnected decisions, that affect other production plans, have to be made more complex analysis is required. In these scenarios, captured data is sent to a centralized platform such as an MES, simulation or a digital twin platform for further analysis. To transfer data across these digital technologies and communicate results, a communication network is required. To ease communication across the supply chain, more traditional wireless networks are used due to their reliability and saturation. IIoT devices traveling through remote locations are more likely to access 3G networks than 5G. The collected data may then be transferred using more advanced networking options when communication networks are more stable.
Analyzing Supply Chain Data
Data capture and transfer across the supply chain provide analytical software with the tools they need to work with. Once the data is captured and accessible, then the heavy-lifting of analyzing supply chains, evaluating logistics, improving production scheduling, and implementing remote monitoring strategies becomes possible.
A variety of analytical software platforms provide manufacturers, warehouses, and logistics enterprises with the tools for implementing a smart supply chain. These tools include:
- Simulation Modeling – For evaluations, testing, and developing optimization strategies to improve supply chain performance.
- Digital Twin Technology – for remotely monitoring supply chain performance in real-time, evaluations, and optimizing decision-making.
- Manufacturing enterprise systems – for inventory management, resource management, and resource allocation.
- ERPs – for managing customer expectations with regards to meeting delivery timelines.
An example of the application of analytical tools to develop a smart supply chain was the use of Simio to capture the variances in transporting agricultural raw materials across major maritime choke points. The study utilized historical transportation data of the Panama Canal, Suez Canal, and the Strait of Gibraltar to evaluate how their closure affects the global supply chain of agricultural produce. Results of the evaluation highlighted the significant food shortages that are likely to occur and their effect on shipping costs, as well as, alternate routes to alleviate shortages. You can find the comprehensive results here.
Taking Advantage of Logistics 4.0
The inclusion of digital technologies to capture and analyze data across the supply chain creates a smart environment that ensures accountability across the entire chain. This smart supply chain provides insight concerning the location of raw materials or produces, evaluates their condition, and monitors logistical progress from loading a container to its final delivery.