This document provides a case study about Gino SA, a burner manufacturer founded in 1931 and headquartered in Paris. It discusses Gino's expansion into the Chinese market in 1995 by establishing an office in Beijing and partnering with three distributors in different regions of China. It then describes how Gino achieved success in China by lowering domestic product prices. A key distributor, Jinghua, had a major customer, Feima, but was not pursuing other opportunities. Feima requested to purchase directly from Gino. This caused issues with Jinghua. The document recommends that Gino pursue the Feima contract directly while compensating Jinghua to maintain the important distribution relationship. It also suggests Gino
Channel & distribution system of nestle india ltdGopal Kumar
An In depth Study on Sales & Distribution Management practices at Nestle India Ltd. Finding were counter analysis,distribution system and order taking.Suggestion was to have an efficient distribution system according to the counters
Channel & distribution system of nestle india ltdGopal Kumar
An In depth Study on Sales & Distribution Management practices at Nestle India Ltd. Finding were counter analysis,distribution system and order taking.Suggestion was to have an efficient distribution system according to the counters
This presentation is based on a Harvard Business Review article "Make the most of a polarizing brand". It also briefly explores the application of such strategies in the Indian Market.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
3. INTRODUCTION OF GINO
Founded in 1931
Headquarters in Paris, France
Product Line:
BURNERS
DOMESTIC
COMMERCIAL
INDUSTRIAL
4. Set up Beijing office in 1995.
Marketing Manager:
DAVID ZHOU
Three new distributors in China:
Wayip Trading Co based in South China city
Guangzhou
Fung’s Co based in Central Coastal city
Shanghai
Jinghua mechanical engineering company based in
Northern city Beijing
5. MARKET POSITION IN CHINA
GINO achieved edge among its competitors
in domestic line by lowering its prices and
maintaining profit margin.
GINO also started doing better in
commercial line
GINO started investing in R&D on new
industrial models
6. GINO MARKET ASSOCIATIONS WITH
FEIMA
Feima was a leading factory in Northern China and
typical of the twenty largest OEMs(Other
Equipment Manufactures.
In 1999 Feima bought from Jinghua 350 sets of
Domestic burners,50 sets of Commercial burners
and three sets of Industrial burners.
7. EMERGING ISSUES:
Jinghua had been placing much emphasis on
Fiema and was missing other major windows of
opportunity
Feima asked permission to buy burners directly
from Gino rather than from Jinghua
Given the size and potential of the business and
after receiving consent of Jinghua David entered
into dicussions with Feima.
8. But as project progressed Jinghua had been opposing
the deal.
Feima told Zhou it would not increase the purchase
unless it been given OEM status.
Analysis
There was lack of service and technical support to the
new customers.
Lost sales of atleast 50 units on part of Jinghua.
9. DECISION
Gino should start with Feima OEM business
for a contract basis in order to increase its
sales.
As in the distribution contract Gino has right
to do business with Feima.
Gino must provide better compensation as in
the contract to Jinghua for gaining its
confidence as Jinghua is its major distributor.
10. Also for other distributors Gino
should start other OEM ventures
to increase business and market
stability
11. THANK YOU
Created by Aditi Jain ,LSR
New Delhi, under the marketing
internship of professor Sameer Mathur