Bigdata analysis in supply chain managmentKushal Shah
big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.
supply chain industry need this type of data to survive in every situations.
Digital Supply Chain - Insights on Driving the Digital Supply Chain Transform...Lora Cecere
Executive Summary
It started with the internet, and the drum beat continues. Mobile. Social. Cloud. Digital Products. Telematics. The Internet of Things. The list of enablers is endless.
Over the last decade, digital marketing departments quickly took advantage of new technologies to power marketing capabilities. As a result, companies have new products and services; but, over the last decade there has been little change in supply chain processes.
There is a great divide in organizations today. There are digital teams in marketing while there are traditional supply chain processes in operations. Many supply chain leaders are asking how they digitize their supply chain practices. This report is designed to help. Here we share a five-step process to get started, and we provide insights from recent research on how to transform manufacturing processes.
What Is Digital Business?
Digitization transforms businesses. A digital business model uses new forms of technology to create new forms of revenue and business value. It is about the use of combinations of technologies to sense changes in real-time and shape a meaningful output.
Digital business is about much, much more than the redefinition of business processes for B2B and B2C. While e-business strategies are foundational, and necessary, it is about more than e-business. In today’s supply chain, while B2C models are well defined and new supply chain models have embraced and redefined e-commerce delivery, B2B processes lag B2C. Today, only 9% of B2B commerce business flows through business networks. There are no digital B2B officers. Companies have been slow to adopt new forms of B2B.
Information sharing is a major challenge in SCM due to the geographical spread of partners and monumental paper work involved across countries and regions. Digitisation impacts the flow of goods, funds and information. It is at the threshold of introducing the Smart Factory where all flows are automated. How relevant are these technologies for India? What can be the Smart Approach for India in sequencing the adoption of these technologies? We present a suggested approach here.
Bigdata analysis in supply chain managmentKushal Shah
big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.
supply chain industry need this type of data to survive in every situations.
Digital Supply Chain - Insights on Driving the Digital Supply Chain Transform...Lora Cecere
Executive Summary
It started with the internet, and the drum beat continues. Mobile. Social. Cloud. Digital Products. Telematics. The Internet of Things. The list of enablers is endless.
Over the last decade, digital marketing departments quickly took advantage of new technologies to power marketing capabilities. As a result, companies have new products and services; but, over the last decade there has been little change in supply chain processes.
There is a great divide in organizations today. There are digital teams in marketing while there are traditional supply chain processes in operations. Many supply chain leaders are asking how they digitize their supply chain practices. This report is designed to help. Here we share a five-step process to get started, and we provide insights from recent research on how to transform manufacturing processes.
What Is Digital Business?
Digitization transforms businesses. A digital business model uses new forms of technology to create new forms of revenue and business value. It is about the use of combinations of technologies to sense changes in real-time and shape a meaningful output.
Digital business is about much, much more than the redefinition of business processes for B2B and B2C. While e-business strategies are foundational, and necessary, it is about more than e-business. In today’s supply chain, while B2C models are well defined and new supply chain models have embraced and redefined e-commerce delivery, B2B processes lag B2C. Today, only 9% of B2B commerce business flows through business networks. There are no digital B2B officers. Companies have been slow to adopt new forms of B2B.
Information sharing is a major challenge in SCM due to the geographical spread of partners and monumental paper work involved across countries and regions. Digitisation impacts the flow of goods, funds and information. It is at the threshold of introducing the Smart Factory where all flows are automated. How relevant are these technologies for India? What can be the Smart Approach for India in sequencing the adoption of these technologies? We present a suggested approach here.
Digitization will reinvent the world economy with individuals, businesses, and societies becoming
interconnected in real time. This new digital economy is more collaborative, intelligent,
responsive, and efficient, with dramatic increases in productivity and economic value.
The digital economy will transform the way we live and work, how business runs, and how society
functions – and it will do this in a timeframe that is much shorter than any other major economic
transition in history.
Big Data & Analytics to Improve Supply Chain and Business PerformanceBristlecone SCC
Prof. David Simchi Levi, Engineering Systems Professor at MIT and Chairman of OPS Rules spoke at Bristlecone Pulse 2017 about delivering customer value through digitization, analytics and automation.
Digital Supply Chain: the start of a new eraBluecrux
95% of Supply Chain leaders struggle with how to drive improvement in their Supply Chain and only 5% are making progress.
In this presentation, you'll get a market overview of emerging technologies and approaches you can apply to drive value. Are you ready to become part of that 5%?
Presented by Lora Cecere, Founder Supply Chain Insights on Supply Chain 4.0 : ready to operate in the digital era? (29 Nov, 2018)
Digital Supply Chain Management - Supply Chain 4.0 - Supply Chain Management ...Danar Mustafa
Digital Supply Chain Management - Supply Chain 4.0 - Supply Chain Management in Industry 4.0
How to increase operational efficiency leveraging digital technologies in Supply Chain Management
https://digitalstrategy-ai.com/
What are big data in the contacts of energy & utilities, and how/where can the utilities find value in the data. In this C-level presentation we discussed the three prime areas: grid operations, smart metering and asset & workforce management. A section on cognitive computing for utilities have been omitted from the presentation due to confidentiality - but I tell you - it is mind-blowing perspectives on how IBM Watson will help utilities plan and optimize their operations in the near future!
See more on http://www.ibmbigdatahub.com/industry/energy-utilities
To share the learnings I had from the course –
Supply Chain Analytics Essentials
by
Dr.Yao Zhao,
Professor in Supply Chain Management
Rutgers Business School
(Rutgers the State University of New Jersey)
Offered through Coursera.
Thanks to TamilNadu Skill Development Corporation
Challenges. Two years and counting. Many issues. And we must recognize we are in a time of continuous supply chain disruption. What you should be doing today and going forward with your supply chain management and its logistics.
A lesson from the pandemic is the strategic and critical importance of supply chain management. With that goes the need for supply chain resilience. Both inside and outside four walls. It started out about technology, but as the pandemic continued, logistics and logistics infrastructure is showing as a key for supply chain resilience. View these for content that may assist you to make your supply chain resilient.
Artificial intelligence transforming the phase of supply chain managementRahul R
Professionals associated with logistics and supply chain are always on their heels to shape the operational chain innovatively that address the challenges more efficiently and minimizes the risk that caused otherwise.
When the professionals hunt for new possibilities, technology is always there for help! Although the concept of Artificial Intelligence is six decades old, it is well on its course to take over the lives of people slowly by making it easy and efficient.
Update on the progress of supply chain leaders on progress on the Supply Chain Effective Frontier (balancing growth, profitability, cycles and complexity). Philippe Lambotte, SVP of Merck, recommends a seat at the table, focus on supply chain strategy, eliminate the white noise, and stay the course.
Blockchain in Supply Chain Management By Prashant Prashant Pandey
A blockchain is a distributed, digital ledger. The ledger records transactions in a series of blocks. It exists in multiple copies spread over multiple computers, which are also called nodes. The ledger is secure because each new block of transactions is linked back to previous blocks in a way that makes tampering practically impossible Supply chain management (SCM) is the active management of supply chain activities to maximize customer value and achieve a sustainable competitive advantage
Digitization will reinvent the world economy with individuals, businesses, and societies becoming
interconnected in real time. This new digital economy is more collaborative, intelligent,
responsive, and efficient, with dramatic increases in productivity and economic value.
The digital economy will transform the way we live and work, how business runs, and how society
functions – and it will do this in a timeframe that is much shorter than any other major economic
transition in history.
Big Data & Analytics to Improve Supply Chain and Business PerformanceBristlecone SCC
Prof. David Simchi Levi, Engineering Systems Professor at MIT and Chairman of OPS Rules spoke at Bristlecone Pulse 2017 about delivering customer value through digitization, analytics and automation.
Digital Supply Chain: the start of a new eraBluecrux
95% of Supply Chain leaders struggle with how to drive improvement in their Supply Chain and only 5% are making progress.
In this presentation, you'll get a market overview of emerging technologies and approaches you can apply to drive value. Are you ready to become part of that 5%?
Presented by Lora Cecere, Founder Supply Chain Insights on Supply Chain 4.0 : ready to operate in the digital era? (29 Nov, 2018)
Digital Supply Chain Management - Supply Chain 4.0 - Supply Chain Management ...Danar Mustafa
Digital Supply Chain Management - Supply Chain 4.0 - Supply Chain Management in Industry 4.0
How to increase operational efficiency leveraging digital technologies in Supply Chain Management
https://digitalstrategy-ai.com/
What are big data in the contacts of energy & utilities, and how/where can the utilities find value in the data. In this C-level presentation we discussed the three prime areas: grid operations, smart metering and asset & workforce management. A section on cognitive computing for utilities have been omitted from the presentation due to confidentiality - but I tell you - it is mind-blowing perspectives on how IBM Watson will help utilities plan and optimize their operations in the near future!
See more on http://www.ibmbigdatahub.com/industry/energy-utilities
To share the learnings I had from the course –
Supply Chain Analytics Essentials
by
Dr.Yao Zhao,
Professor in Supply Chain Management
Rutgers Business School
(Rutgers the State University of New Jersey)
Offered through Coursera.
Thanks to TamilNadu Skill Development Corporation
Challenges. Two years and counting. Many issues. And we must recognize we are in a time of continuous supply chain disruption. What you should be doing today and going forward with your supply chain management and its logistics.
A lesson from the pandemic is the strategic and critical importance of supply chain management. With that goes the need for supply chain resilience. Both inside and outside four walls. It started out about technology, but as the pandemic continued, logistics and logistics infrastructure is showing as a key for supply chain resilience. View these for content that may assist you to make your supply chain resilient.
Artificial intelligence transforming the phase of supply chain managementRahul R
Professionals associated with logistics and supply chain are always on their heels to shape the operational chain innovatively that address the challenges more efficiently and minimizes the risk that caused otherwise.
When the professionals hunt for new possibilities, technology is always there for help! Although the concept of Artificial Intelligence is six decades old, it is well on its course to take over the lives of people slowly by making it easy and efficient.
Update on the progress of supply chain leaders on progress on the Supply Chain Effective Frontier (balancing growth, profitability, cycles and complexity). Philippe Lambotte, SVP of Merck, recommends a seat at the table, focus on supply chain strategy, eliminate the white noise, and stay the course.
Blockchain in Supply Chain Management By Prashant Prashant Pandey
A blockchain is a distributed, digital ledger. The ledger records transactions in a series of blocks. It exists in multiple copies spread over multiple computers, which are also called nodes. The ledger is secure because each new block of transactions is linked back to previous blocks in a way that makes tampering practically impossible Supply chain management (SCM) is the active management of supply chain activities to maximize customer value and achieve a sustainable competitive advantage
Introduction to Big Data: Definition, Characteristic Features, Big Data Applications, Big Data vs Traditional Data, Risks of Big Data, Structure of Big Data, Challenges of Conventional Systems, Web Data, Evolution of Analytic Scalability, Evolution of Analytic Processes, Tools and methods, Analysis vs Reporting, Modern Data Analytic Tools
Big data refers to the vast amount of structured and unstructured data that inundates organizations on a daily basis. This data comes from various sources such as social media, sensors, digital transactions, mobile devices, and more.
1.Introduction
2.Overview
3.Why Big Data
4.Application of Big Data
5.Risks of Big Data
6.Benefits & Impact of Big Data
7.Conclusion
‘Big Data’ is similar to ‘small data’, but bigger in size
But having data bigger it requires different approaches:
Techniques, tools and architecture
An aim to solve new problems or old problems in a better
way
Big Data generates value from the storage and processing
of very large quantities of digital information that cannot be
analyzed with traditional computing techniques.
Data Analytics has become a powerful tool to drive corporates and businesses. check out this 6 Reasons to Use Data Analytics. Visit: https://www.raybiztech.com/blog/data-analytics/6-reasons-to-use-data-analytics
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
2. 1. Introduction
-Big Data Definition
-History of Big Data
2. How Big Data is Shaping the Supply Chains of Tomorrow
3. Big vs. Small Data in Supply Chain
4. Big Data Application in Supply Chain Operations
5. Value-Creating Big Data Sources in Supply Chains
6. How Can an Organization Become Big-Data Enabled in Supply Chai
n Management
7. The Future of Big Data
8. Conclusion
Content
2
3. Introduction
3
Big data is expected to add big value to enterprises in the real world.
Due to the rapid growth of such data, decision makers, planners, and policy makers
need to be able to gain valuable insights from such varied and rapidly changing data,
ranging from daily transactions to customer interactions and also social network
data. Inevitably, some of the difficulties related to big data include capture, storage,
search, sharing, analytics, and visualizing. It can be overcome when the system is
completely mature and can be applied according to current requirements.
Big data is increasingly used to optimize business processes and everyday
operations. With the size of the big data and the capacity of the data that it
encompasses, it carries in itself the potential that will help companies doing
businesses. Companies can use that data to make improvements and generate
efficiencies and making a better intelligent and data driven decisions. It is a driving
force behind new business opportunities.
4. Definition
4
BIGDATA A term that
describes the large
volume of data –
both structured and
unstructured – that
inundates a
business on a day-
to-day basis. But it's
not the amount
of data that's
important. It's what
organizations do
with the data that
matters.
BIGDATA
“A high-volume,
high-velocity
and/or high-variety
information assets
that demand cost-
effective, innovative
forms of information
processing that
enable enhanced
insight, decision
making, and
process
automation.“(Gartne
r,2012 )
BIGDATA
It refers to a process
that is used when
traditional data
mining and handling
techniques cannot
uncover the insights
and meaning of the
underlying data. Data
that is unstructured or
time sensitive or
simply very large
cannot be processed
by relational database
engines. This type of
data requires a
different processing
approach called big
data, which uses
massive parallelism on
readily-available
hardware.
5. History of Big Data
5
1970: A Relational
Model of Data for Large
Shared Data Banks –
Invented by Codd
1985:Decomposition
Storage Model -
Copeland
1989:Shared Nothing
Architecture
2004: Google –Map
Reduce
2005: C-Store
(Vertica),layers WS/RS
2007:Materialization
Optimizations in
Columbar Stores &
Hadoop
Implementation
2005-2007:Star-
Schema Benchmark +
Hadoop
2008:Attempts to
backport columnar
advances to row
storage, not very
effective
Today : Big Data
6. .
Concepts
6
The First 3Vs of big data
are Volume, Variety and Velocity. Yet,
to meet the needs of today's
technological acceleration, other
factors must also be taken into account
by adding 3 more Vs, there are
Veracity, Value and Visualization….
7. Concepts
7
Big Data upgrade
Those who use Big Data often use the Three Vs model to describe it. The three Vs represent:
Volume
Variety
Velocity
To meet the latest needs, at least three more Vs must be added, which is
Veracity - It is about making sure that the Big Data is accurate
Value - It refers to the relative value of the Big Data process
outcomes
Visualization - It is an important way for architects, planners, and
policy experts to communicate with the public
*Visualization example: Access to government data, an interactive data visualizations that invite public participation
8. Big Data Shaping The Supply Chains
8
Supply Chain
Management
Procurement
Inventory
control
logistics
Product lifecycle
management
Preferential
pricing & lead
times
Demand
management
BIG Data in Supply Chain Management
9. Big Data Shaping The Supply Chains
9
Trends in Smart
Manufacturing and
Supply Chain
Cloud, IoT
Driven
Analytics
Demand Driven
Supply Chain Using
BIG DATA
Distributed
Manufacturing
3D
printing/Additiv
e
Manufacturing
BIG Data in Supply Chain Management
10. Big Data Shaping The Supply Chains
10
1
• Providing supplier networks with
more data accuracy, clarity and
insights that leading to contextual
intelligence across supply chains
2
• Scale , scope and depth of data
supply chains is more
accelerating, providing ample data
sets to drive contextual
intelligence
3
• As a catalyst for a greater
collaboration by enabling more
complex supplier networks that
focus on knowledge sharing and
as the value add rather than just a
transactions
4
• Advanced analytics forecasting,
demand planning, sourcing,
production and distribution
• Real time issues can be resolved, better
customer-supplier relationships
• Optimizing inventory control, assets used
efficiently and supply chain security
11. Big Data Shaping The Supply Chains
Big Data can deliver value along the manufacturing value chain in
terms of cost, revenue, and working capital.
Big Data enables well-informed decisions in real time
Reduces wasted resources
Predicts risk of downtime
Predicts needs for maintenance or repair
Detects the presence of safety issues earlier
Improving supply chain management
Notices defects in work products,
Predicts workloads
Forecasts staffing needs
11
12. Big Data vs. Small Data in Supply Chain
12
Small data
hard to comprehend, access,
organize and analyze.
Are used to make crucial decisions for
expansion in business
easy to understand, access and
analyze.
13. Big Data vs. Small Data in Supply Chain
13
Big data
Small data
• Massive ,
impersonal
• Interesting
• General
• Historical
• Pulled
• Targeted ,
personal
• Actionable
• Customised
• Real time
• Pushed
14. Big Data Application in Supply Chain Operations
14
Traceability and recalls are by
nature data-intensive, making
big data’s contribution
potentially significant.
Enabling more complex
supplier networks that focus
on knowledge sharing and
collaboration as the value-add
over just completing
transactions
Big data and advanced
analytics are being integrated
into optimization tools,
demand forecasting, integrated
business planning and
supplier collaboration & risk
analytics at a quickening pace.
Using geoanalytics based on
big data to merge and optimize
delivery networks.
Companies are achieving
significant results using big
data analytics to improve
supply chain performance and
gain greater contextual
intelligence
Greater contextual intelligence
of how supply chain tactics,
strategies and operations are
influencing financial
objectives.
Big data is providing supplier networks with greater data accuracy, clarity, and
insights, leading to more contextual intelligence shared across supply chains.
16. Value-Creating Big Data Sources In Supply Chains
RFID and GPS big data can help in real-time inventory positioning and warehousing.
Point of sale (POS) data is one of the main enablers of demand forecasting and
customer behaviour analysis.
Supplier big data can help manufacturers monitor supplier performance, and
manage risk and capacity.
Manufacturing big data and telemetry will help identify production bottlenecks and
impending machine failures, thus eliminating disruptive machine breakdowns.
16
The immediate opportunities for supply chain leaders to exploit the billions of
gigabytes of data being produced every day are potentially game changing. The
vast majority of this data is unstructured but the technology and tools are now
available to analyse and drive real-time decision making like never before.
20. 20
The Advantages of Big Data in Supply Chain Management
Get better
diagnostic
information
Get a clearer
“crystal ball” for
the future
Manage
external factors
that are beyond
your control
Reduce demand
variability and
cycle times
Make more
profitable supply
chain demand
forecasts
Prepare for the
‘SNEW’ wave
22. 22
Machine Learning
will be the next Big
Thing in Big Data
Privacy Will Be the
Biggest Challenge
Chief Data Officer: A
New Position Will
Emerge
Data Scientists Will
Be In High Demand
Businesses Will Buy
Algorithms, Instead
of Software
Investments in Big
Data Technologies
Will Skyrocket
Prescriptive
Analytics Will
Become an Integral
Part of BI Software
Big Data Will Help
You Break
Productivity Records
Big Data Will Be
Replaced By Fast
and Actionable
Data
The Future of Big Data
23. Observing what is happening in the world, given the developments in information
and mobile technology, there is no doubt that we find ourselves in the era of Big Data.
Anticipating sales volumes, customer preferences for products and optimizing work
schedules are a few examples where proper analysis of big data has the power to help business
succeed.
It is critical that supply chain management and logistics decision makers take note of
the fact that as data and analytics transform organizations, and the landscape within which they
operate .
For a successful organization, it is necessary to take a whole range of steps and
actions.
23
Conclusion
24. Boyd, D. & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technol
ogical, and scholarly phenomenon. Information, Communication and Society. 15 (5) pp.662-679.
Chen, H., Chiang, R & Storey, V. (2012) Business intelligence and analytics: From big data to big
impact. MIS Quarterly 36 (4) pp.1165-1188.
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. & Kruschwitz, N. (2010). Big data, analytics and
the path from insights to value. MIT Sloan Management Review.
Tan, K., Zhan, Y., Ji, G., Ye, F. & Chang, C. (2015) Harvesting big data to enhance supply chain in
novation capabilities: An analytic infrastructure based on deduction graph. International Journal of
Economics. 165 (2015) pp.223-233.
Waller, M. & Fawcett, S. (2013). Data science, predictive analytics, and big data: A revolution that
will transform supply chain design and management. Journal of Business Logistics. 34 (2) pp.77-
84.
Wang, G., Gunasekaran, A., Ngai E. & Papadopoulos T. (2016). Big data analytics in logistics and
supply chain management. Certain investigations for research and applications. International Jour
nal of Production Economics. 176 pp.98-110.
KPMG, Supply Chain Big Data Series Part 1,2,3 & 4 (2017)
24
References