Empowering Farmers through Future Price Information: A Case Study of Price Forecasting of Brinjal in Eastern Uttar Pradesh

2020 ◽  
pp. 479-488
Keyword(s):  
Supply Chain ◽  
Economies Of Scale ◽  
Uttar Pradesh ◽  
Future Price ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Price Information ◽  
Marketing Activities ◽  
Eastern Uttar Pradesh

The study developed ARIMA forecasting model for brinjal prices for the markets of Eastern Uttar Pradesh. It was observed that the ARIMA (1,0,1) with non-zero mean was suitable for both Lucknow and Allahabad markets. ARIMA (2,0,0) (0,1,0) (52), ARIMA (1,1,0) (1,1,0) (52), ARIMA (1,1,2), ARIMA (2,0,0) (1,0,0) (52), ARIMA (3,1,1) were suitable for Delhi, Varanasi, Kolkata, Gorakhpur, and Kanpur markets, respectively, based on lowest AIC values. The farmers and other supply chain actors of Eastern Uttar Pradesh could plan their production and marketing activities looking into the price scenario projected for major markets in the study. The highest price of brinjal was likely to prevail in the Kolkata market. To exploit distant markets, the farmers need to organize themselves into groups to exploit economies of scale.

scholarly journals SERVICE CENTRES IN RURAL DEVELOPMENT : A CASE STUDY OF JAUNPUR DISTRICT, UTTAR PRADESH

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Dinesh Kumar ◽  
Dinesh Kumar ◽  
Dinesh Kumar
Keyword(s):  
Uttar Pradesh ◽  
Secondary Data ◽  
Spatial Arrangement ◽  
Population Index ◽  
Service Centre ◽  
Block Level ◽  
Centrality Score ◽  
Eastern Uttar Pradesh ◽  
Fifth Order

This paper attempts to deal with the identifying the service centers and calculation of the spatial arrangement with complementary area of service centres in Jaunpur district Jaunpur district of Uttar Pradesh. The study area is situated in Eastern Uttar Pradesh of the Middle Ganga Plain. The study is exclusively based on secondary data collected at block level from different offices. The centrality score has been calculated on the basis of three type of indices like functional centrality index, working population index and tertiary population index. There are 31 function or services selected judicially from five sectors (administrative, agricultural and financial, educational, health and transport and communication) to measure the centrality of service centre. The thissen polygon and berry breaking point method has been used for measure the complementary area. Total 88 service centres have been identified as first, second, third, fourth and fifth order service centre. The number of I, II, III, IV, and V order centres accounts for 43, 24, 16, 4, and 1 respectively.

Empirical testing of a model on supply chain management adoption in India using the case study method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Srichandan Sahu ◽  
Kambhampati Venkata Satya Surya Narayana Rao
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Theoretical Model ◽  
Policy Implications ◽  
Chain Management ◽  
Content Type ◽  
Marketing Activities ◽  
Multiple Case ◽  
Innovative Culture

Purpose The purpose of this study is to empirically test a theoretical model on supply chain management (SCM) adoption in India. Design/methodology/approach The present study used a multiple case research method to study the phenomenon. The findings are based on analysis of the SCM adoption processes in three large manufacturing organizations from the aluminium, steel and fertilizer industries. Findings The present study tested four propositions. Three of the propositions were empirically validated and one proposition was revised. The key findings are: one, a lack of recognition by an organization of higher advantages because of SCM adoption as compared to the costs leads to SCM non-adoption. Two, a lack of organizational readiness factors such as a collaborative and innovative culture, higher absorptive capacity and slack resources leads to the non-adoption of SCM. Three, a lack of institutional pressure and marketing activities of the SCM vendors on an organization lead to the non-adoption of SCM. Originality/value The major contribution of the present study is that it has empirically validated the theoretical model for SCM adoption in India. The findings of the present study have both theoretical and practical implications. Theoretically, a model of SCM adoption was validated. The study provides managerial connotations for SCM vendors, consultants, practitioners and policy implications for policymakers.

scholarly journals Value Chain Finance to Dairy Sector-A Case Study of Eastern Uttar Pradesh

2021 ◽  
Author(s):  
Sarvesh Kumar
Keyword(s):  
Value Chain ◽  
Business Models ◽  
Uttar Pradesh ◽  
Value Addition ◽  
Financial Flows ◽  
Production And Distribution ◽  
A Chain ◽  
Financial Arrangements ◽  
Eastern Uttar Pradesh

Background: Dairy sector has highly fragmented supply base and unique ecosystem of delivery resulting that a chain of value addition actors involved in its production and distribution. Value chain financing approach provides opportunities to develop equitable business models that better link all actors in the value chain. Accordingly, this study has carried out to assess the diversity of financial arrangements and the actors involved in dairy value chain in the Eastern Uttar Pradesh. The study also brings about the relatively prominent components/actors of the dairy value chain that could be emphasised while financing dairy value chain. Method: The value chain actors including milk producers have identified purposively and interviewed with well-constructed scheduled. The study has analysed on the data collected from 64 milk producers in 8 villages, 3 inputs suppliers, 8 milk collectors/assemblers, 4 milk transporters, 1 milk processor and 12 distributers for the year 2019-20. Result: The study observed that there is vast network of financing institutions have engaged in the financing of dairy value chain in the study area. Financing agencies have identified the set of activities associated with milk value chain and determine the structure of finance accordingly, in order to minimize costs, to maximize efficiency and to reduce risk. However, there are several informal mechanism of value chain financings also existed parallel to institutional finance due to informal sources are willing to lend money more easily without collateral. Relationships between actors in the value chain facilitate informal financial flows directly to his client actors is also observed in the study. The study has further inferred that among all the actors involved in milk value chains, the processor, producer and distributer have added greater value addition in comparison to other actors in the value chain. 

Data-Driven Inventory Management in the Healthcare Supply Chain

2020 ◽  
pp. 1390-1403
Author(s):  
Shuojiang Xu ◽  
Kim Hua Tan
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Inventory Management ◽  
Data Driven ◽  
Forecasting Model ◽  
Market Demand ◽  
Improved Model ◽  
Future Work ◽  
Statistic Approach

From 21st century, enterprises combine supply chain management with big data to improve their products and services level. In China healthcare industry, supply chain decisions are made based on experience, due to the environment complexities, such as changing policies and license delay. A flexible and dynamic big data driven analysis approach for supply chain decisions is urgently required. This report demonstrates a case study on CRT forecasting model of inventory data to predict the market demand based on pervious transaction data. First a basic statistic approach has been applied to represent the superficial patterns and suggest some decisions. After that a CRT model has been built based on the several independent variables. And there is also a comparison between CRT and CHAID models to choose a better one to further build an improved model. Finally some limitations and future work have been proposed.

scholarly journals SALES FORECAST FOR AGGREGATE PLANNING: CASE STUDY OF AN INDUSTRIAL PRODUCTS COMPANY IN MEXICO

2021 ◽  
Vol 8 (4) ◽  
pp. 381-392
Author(s):  
Ignacio Alvarez Placencia ◽  
Diana Sánchez-Partida ◽  
José-Luis Martínez-Flores ◽  
Patricia Cano-Olivos
Keyword(s):  
Supply Chain ◽  
Forecasting Model ◽  
Aggregate Level ◽  
Industrial Products ◽  
Sales Forecast ◽  
Demand Pattern ◽  
Best Execution ◽  
The Cost ◽  
Significant Effort

This case study presents the analysis through the use of sales estimation tools for planning demand for aggregate level as a finished product in a leading industrial products company in the market in Mexico. First, it aligned the demand plan and the supply plan, recommending the best execution scenario to increase operational efficiency and reduce the cost of operating the supply chain to increase the company's productivity and stay competitive. Then, after analysing the behaviour of the demand for selected products, the authors determined as the main affectation the inadequate precision of the method forecasting and the lack of an aggregate forecasting strategy that allows reducing the variation. Due to this, the most significant effort was concentrated on determining a better-forecasting model and the decision to aggregate the demand based on three relevant criteria: the demand pattern based on the Soft, Intermittent, Erratic or Irregular quadrant, the best method of the forecast for each product and the time in quarters. As a result, a reduction between 20% and 46% in the forecast variation can be obtained from the above.

scholarly journals RNNLBL : A Recurrent Neural Network and Log Bilinear based Efficient Stock Forecasting Model

2020 ◽  
Vol 9 (4) ◽  
pp. 1676-1682 ◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Stock Price ◽  
Future Price ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Short Term ◽  
Stock Price Forecasting ◽  
Non Linear

Recent years have seen the wide use of Time series forecasting (TSF) for predicting the future price stock, modeling and analyzing of finance time series helps in guiding the trades and investors decision. Moreover considering the stock as the dynamic environment, it is pronounced as the non-linearity of time series which affects the stock price forecast immediately. Hence, in this research work we propose intelligent TSF model, which helps in forecasting the early prediction of stock prices. The proposed stock price forecasting model employed both short-term (i.e. recent behavior fluctuation) using log bilinear (LBL) model and long-term (i.e., historical) behavior using recurrent neural network (RNN) based LSTM (long short term memory )model. Subsequently, this model is mainly helpful for the home brokers since they do not possess enough knowledge about the stock market. Proposed RNNLBL hybrid model shows the satisfying forecasting performance, these results in overall profit for the investors and trades. Furthermore, proposed model possesses a promising forecasting in case of the non-linear time series since the pattern of non-linear pattern are highly improbable to capture through these state-of-art stock price forecasting models.

scholarly journals Forecasting Model Selection of Curly Red Chili Price at Retail Level

2019 ◽  
Vol 2 (1) ◽  
pp. 1-12
Author(s):  
Ketut Sukiyono ◽  
Miftahul Janah
Keyword(s):  
Moving Average ◽  
Price Policy ◽  
Exponential Smoothing ◽  
Future Price ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Monthly Data ◽  
Forecasting Models ◽  
Double Exponential ◽  
Double Exponential Smoothing

Chilli is one of strategic commodity in Indonesia due to its contribution to inflation level. For this reason, future price information is very importance for designing price policy. Future price merely can be provided by conducting a price forecasting. Various forecasting models can be applied for this purpose; the problem is which the best model for forecasting is. This study aims to select the most accurate forecasting model of curly red chili prices at the retail level. The data used are monthly data, from 2011 - 2017. Five forecasting models are applied and estimated including Moving Average, Single Exponential Smoothing, Double Exponential Smoothing, Decomposition, and ARIMA. The best model is selected based on the smallest MAPE, MSE and MAD values. The results show that the most accurate forecasting model is ARIMA (1,1,9).

Data-Driven Inventory Management in the Healthcare Supply Chain

2017 ◽  
pp. 75-91
Author(s):  
Shuojiang Xu ◽  
Kim Hua Tan
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Inventory Management ◽  
Data Driven ◽  
Forecasting Model ◽  
Market Demand ◽  
Improved Model ◽  
Future Work ◽  
Statistic Approach

From 21st century, enterprises combine supply chain management with big data to improve their products and services level. In China healthcare industry, supply chain decisions are made based on experience, due to the environment complexities, such as changing policies and license delay. A flexible and dynamic big data driven analysis approach for supply chain decisions is urgently required. This report demonstrates a case study on CRT forecasting model of inventory data to predict the market demand based on pervious transaction data. First a basic statistic approach has been applied to represent the superficial patterns and suggest some decisions. After that a CRT model has been built based on the several independent variables. And there is also a comparison between CRT and CHAID models to choose a better one to further build an improved model. Finally some limitations and future work have been proposed.

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