The absence of food traceability has led to severe problems like a product recall, consumer dissatisfaction, and contamination insecurities in the past. We consider a five-level supply chain for sausage where at each level, the output product is manufactured by combining/mixing correct proportions of the raw materials from the previous stages. The demand for the final product is uncertain. Using stochastic models, we improve the supply chain's traceability and optimize dispersion among the sausage material batches. We also derive theoretical results in terms of proving a relatively complete recourse structure in the proposed model. Furthermore, to provide insights regarding the supply chain network's transparency, we integrate the Blockchain framework in our model data storage. Using a simple case study, where an attacker tries to alter or delete data inside the Blockchain, we quantitatively measure this immutability of this decentralized data. The case study reveals that decentralized databases could make data accessible to peers (retailers, suppliers, manufacturers) while mitigating data tampering by Blockchain technology. This ensures transparency among the peers and the privacy and security of the data present in the decentralized database of different supply chain networks.

缺乏食品可追溯性已导致严重的问题，例如过去的产品召回，消费者不满意以及污染不安全。我们考虑了香肠的五级供应链，其中在每个级别上，输出产品都是通过混合/混合来自前一阶段的正确比例的原材料来制造的。最终产品的需求不确定。使用随机模型，我们改善了供应链的可追溯性，并优化了香肠原料批次之间的分散。我们还从证明所提出的模型中相对完整的追索权结构的角度得出理论结果。此外，为了提供有关供应链网络透明度的见解，我们将区块链框架集成到模型数据存储中。通过一个简单的案例研究，在攻击者试图更改或删除区块链内部数据的情况下，我们定量地测量了这种分散数据的不变性。案例研究表明，去中心化数据库可以使同行（零售商，供应商，制造商）可以访问数据，同时减轻区块链技术对数据的篡改。这确保了对等方之间的透明性以及不同供应链网络的分散数据库中存在的数据的隐私和安全性。