Farming-as-a-Service (FAAS) for a Sustainable Agricultural Ecosystem in India

2022 ◽  
pp. 85-123
Author(s):  
Chandrani Singh ◽  
Sunil Hanmant Khilari ◽  
Archana Nandanan Nair
Keyword(s):  
Decision Making ◽  
Food Security ◽  
Real Time ◽  
Cost Optimization ◽  
Data Driven ◽  
Farming Practices ◽  
Real Time Monitoring ◽  
Agricultural Ecosystem ◽  
Go Model ◽  
Start Ups

Agriculture being the prime means of livelihood, there is a basic need of re-inventing the farming best practices, combined with tech-driven innovations in this segment to ensure sustainability and eliminate poverty and hunger. In this chapter, the authors focus on introducing relevant technology-enabled services that will ensure economic sustainability, enhance food security through data-driven decision making by various stakeholders like farmers,agri-business and agri-tech start-ups, farmpreneurs, government, agronomists, and IT suppliers. The analyzed information will be used as a vantage by farmers to select precision farming practices to aid productivity to empower personnel to provide timely assistance and industries to implement real-time monitoring using sensors and devices. The chapter will help formulate concepts, methods, practices, benefits, and introducing several case scenarios to effectively propagate the service mode of farming that will imbibe pay-as-you go model ensuring cost optimization and operational ease.

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

scholarly journals Data-driven decision support under concept drift in streamed big data

2019 ◽  
Vol 6 (1) ◽  
pp. 157-163 ◽  
Author(s):  
Jie Lu ◽  
Anjin Liu ◽  
Yiliao Song ◽  
Guangquan Zhang
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Real Time ◽  
Concept Drift ◽  
High Volume ◽  
Streaming Data ◽  
Data Driven ◽  
Research Directions ◽  
Decision Outcomes ◽  
Past Data

Abstract Data-driven decision-making ($$\mathrm {D^3}$$D3M) is often confronted by the problem of uncertainty or unknown dynamics in streaming data. To provide real-time accurate decision solutions, the systems have to promptly address changes in data distribution in streaming data—a phenomenon known as concept drift. Past data patterns may not be relevant to new data when a data stream experiences significant drift, thus to continue using models based on past data will lead to poor prediction and poor decision outcomes. This position paper discusses the basic framework and prevailing techniques in streaming type big data and concept drift for $$\mathrm {D^3}$$D3M. The study first establishes a technical framework for real-time $$\mathrm {D^3}$$D3M under concept drift and details the characteristics of high-volume streaming data. The main methodologies and approaches for detecting concept drift and supporting $$\mathrm {D^3}$$D3M are highlighted and presented. Lastly, further research directions, related methods and procedures for using streaming data to support decision-making in concept drift environments are identified. We hope the observations in this paper could support researchers and professionals to better understand the fundamentals and research directions of $$\mathrm {D^3}$$D3M in streamed big data environments.

scholarly journals DEVELOPMENT OF FOOD SECURITY INFORMATION SYSTEM BASED ON BUSINESS INTELLIGENCE IN FOOD SECURITY AGENCY, MINISTRY OF AGRICULTURE, INDONESIA

2014 ◽  
Vol 8 (2) ◽  
pp. 76
Author(s):  
Manise Hendrawaty ◽  
Harisno Harisno
Keyword(s):  
Decision Making ◽  
Information System ◽  
Food Security ◽  
Real Time ◽  
Balanced Scorecard ◽  
Business Intelligence ◽  
Business Environment ◽  
Security Information ◽  
Real Time Information ◽  
Time Information

Food is the main basic need of human, because of that fulfillment of human need of food has to be fulfilled. So it can fulfill that need, then government institution, Food Security Agency (BKP) is formed so it can monitor fulfillment of food need of society. The goals of this writing are to develop food security information system that provides dashboard facility based on business intelligence, to develop food security information system that can give fast, precise and real time information about food security, to develop decision-making support system for chairman in food security institution. Data is obtained from questionnaires to 51 respondents that are chairmen in Food Security Agency. Data is analyzed with SWOT analysis method for business environment and IT balanced scorecard (IT BSC) for IS/IT environment. The result of analysis of food security information system in Food Security Agency can help chairman in decision-making by presenting information about dashboard that gives fast, precise and real time information. It can be concluded that development of information is successfully done.

Empowering human resource functions with data-driven decision-making in start-ups: a narrative inquiry approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Deepkumar Varma ◽  
Pankaj Dutta
Keyword(s):  
Decision Making ◽  
Narrative Inquiry ◽  
Technology Implementation ◽  
Data Driven ◽  
Structured Interviews ◽  
New Paradigm ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Start Up ◽  
Start Ups

Purpose Across industries, firms want to adopt data-driven decision-making (DDDM) in various organizational functions. Although DDDM is not a new paradigm, little is known about how to effectively implement DDDM and which problem areas to focus on in these functions. This study aims to enable start-ups to use DDDM in human resources (HR) by studying five HR domains using a narrative inquiry technique and aims to guide managers and HR practitioners in start-ups to enable data-driven decisions in HR. Design/methodology/approach This study adopts the narrative inquiry technique by conducting semi-structured interviews with HR practitioners and senior members handling HR functions in start-ups. Interview memos are thematically analyzed to identify repeated ideas, concepts or elements that become apparent. Findings The study findings indicate that start-ups need to have canned operational reports with right attributes in each of these HR domains, which members should use when performing HR tasks. Few metrics, like cost-to-hire in recruitment, distinctly surfaced relatively higher in importance that each start-up, should compute and use in decision-making. Practical implications Managers, HR practitioners and information technology implementation teams will be able to consume the findings to effectively design or evaluate HR processes or systems that empower decision-making in a start-up. Originality/value Start-ups have a fast-paced culture where creativity, relationships and nimbleness are valued. Prevalent decision models of larger organizations are not suitable in start-ups’ environments. This study, being cognizant of these nuances, takes a fresh approach to guide start-ups adopt DDDM in HR and identify key problem areas where decision-making should be enabled through data.

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