Journal of Graphic Era University

Blockchain Technology Enabled Traceability Framework: Food Supply Chain Perspective

2023-09-07T08:34:53+00:00

In the global market, consumers have expectations of good quality food products. To provide flawless food products, traceability has become dominant in a complex food supply chain (SC) network. Stakeholders of the food SC hesitate to share their data with each other, as they have different business strategies. Blockchain Technology (BCT) brings all the parties of food SC on single platform to improve interorganizational trust. The implementation of BCT in food SC offers the traceability of products. This paper proposes a framework for traceability of products in food SC using BCT that offers the consumers to trace the origin of the food products. Blockchain enhances the food traceability, minimize the data tampering, and improves data transparency in food SC. BCT enabled food SC framework has been tested using case study of rice SC and found the enhancement in traceability.

Improved Demand Forecasting of a Retail Store Using a Hybrid Machine Learning Model

2023-08-02T19:07:19+00:00

Accurate demand forecasting is a competitive advantage for all supply chain components, including retailers. Approaches like naïve, moving average, weighted average, and exponential smoothing are commonly used to forecast demand. However, these simple approaches may lead to higher inventory and lost sales costs when the trend in demand is non-linear. Additionally, price strongly influences demand, and we can’t neglect the impact of price on demand. Similarly, the demand for a stock keeping unit (SKU) depends on the price of the competitor for the same SKU and the price of the competitive SKU. We thus propose a demand prediction model that considers historical demand data and the SKU price to forecast the demand. Our approach uses different machine-learning regressor algorithms and identifies the best machine-learning algorithm for the SKU with the lowest forecasting error. We further extend the forecasting model by training a hybrid model from the best two regression algorithms individually for each SKU. Forecasting error minimisation is the driving criterion for our literature. We evaluated the approach on 1000 SKUs, and the result showed that the Random Forest is the best-performing regressor algorithm with the lowest mean absolute percentage error (MAPE) of 8%. Furthermore, the hybrid model resulted in a lower inventory and lost sales cost with a MAPE of 7.74%. Overall, our proposed hybrid demand forecasting model can help retailers make informed decisions about inventory management, leading to improved operational efficiency and profitability.

Data-Driven Retail Excellence: Machine Learning for Demand Forecasting and Price Optimization

2023-08-02T19:08:28+00:00

Demand forecasting and price optimization are critical aspects of profitability for retailers in a supply chain. Retailers need to adopt innovative strategies to optimize pricing and increase profitability. This research paper proposes a price optimization approach for retailers using machine learning. The approach involves using linear regression to forecast demand incorporating price as an input, followed by price optimization taking into account inventory and perishability costs. The feasibility of using linear regression for price optimization for Stock Keeping Units (SKUs) is assessed using a feasibility index. The linear regression can predict the demand more accurately (23% Mean Absoulute Percentage Error (MAPE)) compared to exponential smoothing with optimised smoothing constant (47.09% MAPE) for 1000 SKUs. Also, the feasibility index can segregate the SKUs with an accuracy of 99%. The machine learning-based demand forecasting can assist retailers in accurately predicting customer demand and improving pricing decisions, while the feasibility index enables retailers to identify SKUs that require alternative pricing strategies.

Shortest Path of a Random Graph and its Application

2024-03-06T09:59:24+00:00

The goal of this work is to provide an effective method for determining the shortest path in random graphs, which are complicated networks with random connectivity patterns. We have developed an algorithm that can identify the shortest path for both weighted and unweighted random graphs to accomplish our objective. As connectivity in these types of structures is changing, the algorithm adjusts to different edge weights and node configurations to provide fast and precise shortest path searching. The study shows that the suggested method performs more successfully in finding the shortest path throughout random graphs using comprehensive computations. Many networks, including social networks, granular networks, road traffic networks, etc., include nodes that can connect to one another and create random graphs in the present-day computational era. The outcomes demonstrate how flexible it is, which makes it a useful tool for practical uses in domains where random graph structures are common, like transportation networks, communication systems, and social networks. For illustration, we have taken into consideration an actual case study of communication road networks here. We have determined the shortest path of the road networks using our proposed algorithm, and the results have been presented. Better decision-making across a range of areas is made possible by this study, which advances effective algorithms designed for complicated and unpredictable network environments.

Some Studies on Cutting Force Generation in Machining of Mg-Ca1.0 Alloy

2024-03-29T19:12:37+00:00

Magnesium alloy has qualities that are similar to those of bone, it is now commonly acknowledged as a biodegradable material. Mg-Ca1.0 is a recently developed biodegradable substance that doesn’t cause any harmful substances to be produced in the body. It is frequently utilized in fixation screws and supporting plates for bone implants. To far, very little research has been published on the machining of biodegradable Magnesium Calcium alloy. This investigation’s goal is to evaluate the cutting forces produced during a face milling procedure. The experimental work used a one-factor-at-a-time strategy to assess how process factors affect performance attributes. The current study uses a CVD diamond-coated insert to examine how the depth of cut, feed rate, and cutting speed affect the radial (F_{$_{x}$}), tangential (F_{$_{y}$}), and axial (F_{$_{z}$}) cutting forces during face milling in a dry machining environment.