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Application of Artificial Intelligence in Automation of Supply Chain Management



A well-functioning supply chain is a key to success for every business entity. Having an accurate projection on inventory offers a substantial competitive advantage. There are many internal factors like product introductions, distribution network expansion; and external factors such as weather, extreme seasonality, and changes in customer perception or media coverage that affects the performance of the supply chain. In recent years Artificial Intelligence (AI) has been proved to become an extension of our brain, expanding our cognitive abilities to levels that we never thought would be possible. Though many believe AI will replace humans, it is not true, rather it will help us to unleash our true strategic and creative potential. AI consists of a set of computational technologies developed to sense, learn, reason, and act appropriately. With the technological advancement in mobile computing, the capacity to store huge data on the internet, cloud-based machine learning and information processing algorithms etc. AI has been integrated into many sectors of business and been proved to reduce costs, increase revenue, and enhance asset utilization. AI is helping businesses to get almost 100% accurate projection and forecast the customer demand, optimizing their R&D and increase manufacturing with lower cost and higher quality, helping them in the promotion (identifying target customers, demography, defining the price, and designing the right message, etc.) and providing their customers a better experience. These four areas of value creation are extremely important for gaining competitive advantage. Supply-chain leaders use AI-powered technologies to a) make efficient designs to eliminate waste b) real-time monitoring and error-free production and c) facilitate lower process cycle times. These processes are crucial in bringing Innovation faster to the market.
Journal of Strategic Innovation and Sustainability Vol. 14(3) 2019 43
Application of Artificial Intelligence in Automation of
Supply Chain Management
Rupa Dash
University of Pittsburgh
Mark McMurtrey
University of Central Arkansas
Carl Rebman
University of San Diego
Upendra K. Kar
University of Pittsburgh
A well-functioning supply chain is a key to success for every business entity. Having an accurate
projection on inventory offers a substantial competitive advantage. There are many internal factors like
product introductions, distribution network expansion; and external factors such as weather, extreme
seasonality, and changes in customer perception or media coverage that affects the performance of the
supply chain. In recent years Artificial Intelligence (AI) has been proved to become an extension of our
brain, expanding our cognitive abilities to levels that we never thought would be possible. Though many
believe AI will replace humans, it is not true, rather it will help us to unleash our true strategic and creative
potential. AI consists of a set of computational technologies developed to sense, learn, reason, and act
appropriately. With the technological advancement in mobile computing, the capacity to store huge data
on the internet, cloud-based machine learning and information processing algorithms etc. AI has been
integrated into many sectors of business and been proved to reduce costs, increase revenue, and enhance
asset utilization. AI is helping businesses to get almost 100% accurate projection and forecast the customer
demand, optimizing their R&D and increase manufacturing with lower cost and higher quality, helping
them in the promotion (identifying target customers, demography, defining the price, and designing the
right message, etc.) and providing their customers a better experience. These four areas of value creation
are extremely important for gaining competitive advantage. Supply-chain leaders use AI-powered
technologies to a) make efficient designs to eliminate waste b) real-time monitoring and error-free
production and c) facilitate lower process cycle times. These processes are crucial in bringing Innovation
faster to the market.
44 Journal of Strategic Innovation and Sustainability Vol. 14(3) 2019
Supply chain management (SCM) is one of the most challenging fields which emphasizes interactions
among different sectors, primarily marketing, logistics, and production. Therefore, success in SCM lies in
the overall success of any business. However, with the consistent changes in business practices like lean
management and just-in-time philosophy both in production and logistics, globalization, adverse events
i.e. frequent natural disaster, political instability, etc. SCM always need to develop an adequate solution to
mitigate such challenges. In recent years technologies like Artificial Intelligence (AI) is been proved
immensely valuable to SCM.
As the name suggests AI defined as the ability of a computer to independently solve problems that
they have not been explicitly programmed to address. The field of AI came to existence in 1956, in a
workshop organized by John McCarthy (McCarthy Et al., 2006). In successive years the pioneering work
of McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, Arthur Samuel, Oliver
Selfridge, Ray Solomonoff, Allen Newell, and Herbert Simon, etc. galvanized the field of “artificial
intelligence” (Solomonoff, 1985). In his article “Computing Machinery and Intelligence” Alan Turing
proposed the possibility of designing computers which can learn automatically (Turing, 1950). After
“Shakey” a wheeled robot that was built at SRI, the field of mobile robotics gained International
attention1. However, in the late ’90s with the technological progress in designing computing power to
store and process large dataset, the internet having the capacity to gather large amounts of data, and
statistical techniques that, by design, can derive solutions from these data sets, allowed AI to emerge as
one of the powerful technologies of the century (Kar et al, 2018). In the last two decades Technologies
like Cognitive Computing, Computer Vision, Context-aware Computing, Natural Language Processing,
Predictive Analytics, Machine Learning, Reinforcement Learning, Supervised Learning, Unsupervised
Learning, and Deep Learning, etc. have enabled computer’s “thoughts” by providing a conceptual
framework for processing input and making decisions based on that data (Kar et al, 2018).
The modern machines enabled with AI platform are capable to gather information from its
surroundings; using logic and probability choose to act with the highest likelihood of success. These
machines are made to learn, and act intelligently based on the big-data sets and recognize objects or
sounds with considerable precision (Mnih et al, 2015, Esteva et al., 2017). With the technological
advancement in mobile computing, storage of huge data on the Internet and cloud-based machine learning
and information processing algorithms, etc. applications and benefits of AI technologies are growing
exponentially (Kar et al, 2018). Machines powered by AI performing many tasks—such as recognizing
complex patterns, synthesizing information, drawing conclusions, and forecasting—that not long ago
were assumed to require human cognition (Zhang et al.,1999, Bughin et al 2017). The best example
would be Netflix and Amazon. Both companies use AI to personalize recommendations to millions of
subscribers worldwide. From self-driving cars to implantable medical devices to electronic trading to a
robot control of remote sensing are few other examples. Using deep learning algorithms, powered by
advances in computation these machines have even exceeded human performance, particularly in visual
tasks like playing Atari games (Perez et al., 2014), strategic board games like Go (Silver D et al., 2016)
and object recognition (Esteva et al., 2017). AI, which enables machines to exhibit human-like cognition,
therefore wherever a process uses digital data, AI can be applied to use that data more effectively to
improve the functioning of most digital operations, products, and services (Hall DW et al., 2017).
Applications of AI has helped businesses gain a competitive advantage in a) getting almost 100%
accurate projection and forecast the customer demand, b) optimizing their R&D, therefore, increase in
manufacturing with lower cost and higher quality c) helping them in the promotion (identifying target
customers, demography, defining the price, and designing the right message, etc. d) providing their
customers a better experience (has been explained in great detail in a later section). AI already in use in
various business practices including medicine, law, finance, accounting, tax, audit, architecture,
consulting, customer service, manufacturing, and transport, etc. (Hall DW et al., 2017). In this article, we
have highlighted the recent trends and applications of AI in supply chain management, particularly in
Journal of Strategic Innovation and Sustainability Vol. 14(3) 2019 45
context to the retail and manufacturing industry. The examples given are the only representative in the
respective areas.
Application of AI in Value Creation
Computer-based forecasting/demand planning is not new. It is based on a series of algorithms designed
which takes various data sets like shipment data, product life cycle data, ordering pattern, manufacturing
data, etc. over a period of time to forecast. In contrast, the AI enabled system knows the best possible
combinations of algorithms and data sets to consider having an accurate prediction. More importantly, AI
is helping businesses to a) get almost 100% accurate projection and forecast the customer demand, b)
optimizing their R&D, therefore, increase in manufacturing with lower cost and higher quality c) helping
them in the promotion (identifying target customers, demography, defining the price, and designing the
right message, etc. d) providing their customers a better experience. These four areas of value creation are
extremely important for gaining competitive advantage.
AI Helps to Forecast Demand and Optimization
AI is been effectively used in projection and forecasting. Organizations are always keen to balance
both supply and demand. Therefore, a better forecast is needed for its supply chain and manufacturing. As
AI can process, analyze (automatically) and more importantly, predict data, it provides accurate and
reliable forecasting demand, which allows businesses to optimize their sourcing in terms of purchases and
orders processing therefore reducing costs related to transportation, warehousing and supply chain
administration, etc. In addition, as it discerns trends and patterns which help to design better retailing and
manufacturing strategies. For example; businesses use this tool in several ways, such as stock only the
specific quantities (as accurate as each independent unit/product) of specific products they will sell and
minimizing waste. Similarly; getting accurate sales trends they can order more soon-to-be-popular items.
As these demand forecasts are so accurate they do not lose the sale because of product unavailability.
National Grid in the United Kingdom uses the platform “DeepMind” developed by Google which predicts
the best supply and demand variations accurately even considering variables like weather-related
exogenous inputs (Yao 2017). Machine learning approaches not only incorporate historical sales data and
the setup of the supply chains but also rely on near-real-time data regarding variables such as advertising
campaigns, prices, and local weather forecasts (Bughin et al 2017). Otto a German online retailer
manages to reduce 90% of their inventory using such application. The AI forecasts are so reliable that
Otto building its inventory in anticipation of the orders, more interestingly totally relying on AI without
any human intervention (Burgess, 2018). AI is also been used in R&D departments, to quickly assess
whether a prototype would be likely to succeed or fail in the market—and if so why. More importantly, it
delivers more efficient designs by eliminating waste in the design process. By doing so AI has played an
important role in smart manufacturing. (Kusiak A, 2018).
AI Helps in the Production
AI has played a significant role in production because a) better optimization of assets and processes, b)
designing best teams i.e. people and robots, c) improvement in quality and reliability i.e. error-free, and d)
prevention of downtime for maintenance. Automation process has taken a big stride because of AI
technologies. Robotics one of the advanced branches of AI has taken a central role in the production (Bughin
et al, 2017). Advances in technologies in object recognition and semantic segmentation has transformed the
behavior of the robots, particularly in context to how they recognize the properties of the materials and objects
they interact with. The new AI-enhanced, camera-equipped robots are trained to recognize empty shelf space.
This leads to a dramatic speed advantage over conventional methods in picking objects (Bughin et al, 2017,
Martin C et al. 2017). Deep learning has also been used to correctly identify an object and its position. This
enables robots to handle objects without requiring the objects to be in fixed, predefined positions. Ocado, the
UK supermarket, use one of the AI platforms in its retailer’s warehouse, where robots steer thousands of
product-filled bins over a maze of conveyor belts and deliver them to human packers just in time to fill
46 Journal of Strategic Innovation and Sustainability Vol. 14(3) 2019
shopping bags (Dale M., 2018). Similarly, other robots whisk the bags to delivery vans whose drivers are
guided to customers’ homes by the best route based on traffic conditions and weather (Bughin et al, 2017).
AI-enhanced logistics robots are also able to integrate disturbances in their movement routines via an
unsupervised learning engine for dynamics. This capability leads to more precise makeovers and overall
improved robustness of processes (Webster, C et al 2019). Collaborative robots can increase productivity by
up to 20 percent (Bughin et al, 2017, Martin C et al. 2017). AI enabled semiconductor chip-production process
is a good example of how AI helps in production. The cycle times from the first processing of the wafer to the
final chip are typically several weeks to months and include various intermediate quality- testing processes.
Testing costs and yield losses in semiconductor production can constitute up to 30 percent of the total
production cost. Semiconductor manufacturers are using AI engines to identify root causes of yield losses that
can be avoided by changing production processes. Enhanced applications are designed to monitor and adjust
subprocesses in real time (Bughin et al, 2017, Martin C et al. 2017). AI techniques help not only determining
the optimized product operating conditions or process conditions but also to significantly reduce defects in
manufacturing. Similarly; in asset-heavy businesses, where complex systems running with minimal downtime,
AI provides the perfect solution. Utility companies use AI for maintenance of their extensive electrical grids.
Using data from sensors, drones, and other hardware, machine learning applications helps grid operators avoid
decommissioning assets before their useful lives have ended, while simultaneously enabling them to perform
more frequent remote inspections and maintenance to keep assets working well (Bughin et al, 2017). Using AI
one European power distribution company reduced its cash costs as high as 30% over five years by replacing
power transformers. AI is also enabling the “preventive maintenance” as well. Therefore, in a production unit
where multiple machines are used, it will indicate the possible failure (Bughin et al, 2017, Martin C et al.
AI helps in Promotion and Pricing
Digital content has already become the norm and businesses employ multiple channels to reach their
customers. About 25 percent of today’s marketing budgets are devoted to digital channels, and almost 80
percent of marketing organizations make technology-oriented capital expenditures—typically hardware
and software—according to a recent Gartner survey (Foo et al 2018, Sterne, 2018). AI-supported
activities include digital advertising buys (programmatic buying), website operation and optimization,
search engine optimization, A/B testing, outbound e-mail marketing, lead filtering and scoring, and many
other marketing tasks (Sterne, 2018).
AI tools like Wordsmith, Articolo and Quill are already being used by the Associated Press and
Forbes to create news, which leads to clicks on their websites (Seligman 2018). These tools use,
templates, fill-in-the-blanks to enter data and keywords to create unique content which gives the readers
the impression that a human has written it. AI is not only able to generate content; it can curate it. Content
curation by AI not only connect the visitors with certain websites but also make recommendations based
on their personal choice. Personalized email marketing campaigns based on preferences and user
behaviors are well known (Sterne, 2018).
The Machine learning applications analyze millions of data about the behavior of consumer i.e. best
frequency, what catches their attention the most and best times and days of the week to contact the user.
A few of the AI-based applications like Boomtrain, Phrases, and Persado is already been shown their
value. Phrases claims, the email it creates surpasses those of a human by over 95%. The cognitive content
of Persado demonstrated to exceed what a human could do 100% of the time (Jaidka et al 2018, Gaggioli
2018). Similarly; Facebook, Amazon, and Google are well known for using AI enabled digital advertising
(Deb et al 2018). The AI platform analyzes the information including interests, demographics, and other
aspects to learn and predict the best audience for their brand. Adext (AI platform) can automate the
handling and optimization of advertisements on various platforms including Google AdWords and
Facebook. More importantly, it detects the most likely buyers and helps them to take the desired action or
conversion. AI has revolutionized Internet searches and search engine optimization (SEO) (Deb et al
2018, Gaggioli 2018). AI devices like Amazon’s Echo, Google’s Home, Apple’s Siri, and Microsoft’s
Cortana make it easy for their customers to perform searches by either saying a voice command or just
pressing a button (Deb et al 2018). RankBrain developed by Google, can interpret the user’s voice
Journal of Strategic Innovation and Sustainability Vol. 14(3) 2019 47
searches and, provides them the best results based on the user’s language and context (Sutton et al 2018).
Therefore, that famous long-tail keywords will be history. Smart marketers will use creative words to
replace with more conversational keywords, which will increase their traffic and customers. An AI
platform like Grid has transformed the webpage designing, the best part is it can customize the website
for each customer and change the content of the website based on the preference of the user. Many brands
already have chatbot powered by AI on their website. They serve clients 24/7, more importantly, as
they’re fast they solve the problems of the customer faster than human. For example; Sephora uses an AI
platform i.e. Visual Artist which identifies facial features and then uses augmented reality to analyze and
suggest customized cosmetic products like lipsticks, eyeshadows, etc. (Kumar et al 2018).
Yield management programs were considered as the best system and been used for pricing airline
seats, hotel rooms, and other perishables for years. However, AI has changed it dramatically. Now every
business interested to know what price is the customer willing to pay? In a 24X7 connected world
consumers continuously redefine value by comparing prices online, even when browsing in a brick-and-
mortar store. The right price at the right time increases customer satisfaction and leads to more sales and
higher profit (Khorram 2019). Defining the optimal price for a product is complicated which is broadly
depends on many factors including the day of the week, season, time of day, weather, channel and device,
competitors’ prices, etc. AI is a good tool to determine the price elasticity for every item and
automatically adjust prices according to the chosen product strategy (Khorram 2019). In the retail
industry, AI is been extensively used to optimize, update, and tailor it to each shopper in real time. AI
program is been exploited which looked for clues about what the shopper will like based on previous
purchases, age, home address, web browsing habits, and mounds of other data. This kind of insights-
based selling, including personalized promotions, optimized assortment, and tailored displays, increase
sales substantially (Mathur 2019, Bughin et al, 2017). Aerospace companies are using AI technologies to
prioritize sales targets and optimize the price of services. For years, they prioritized maintenance, repair,
and overhaul (MRO) sales lead manually, a cumbersome, resource-heavy, and not always an efficient
process. Using AI to improve the accuracy of forecasting MRO work and focusing on the firm’s sales
efforts on the most promising leads can have a significant effect on profitability (Kraus et al 2019, Bughin
et al, 2017).
In recent years, artificial intelligence has enabled pricing solutions to track buying trends and
determine more competitive product prices (Paolanti et al 2018). AI-driven pricing software has been
included in various sectors including consumer goods, fashion, hospitality, and transportation (Meng et al
2018). In the future, businesses will progress from absolute i.e. static pricing to dynamic pricing which
will offer customers different prices based on external factors and their individual buying habits. Dynamic
pricing is based on aggregate available pricing data from various sources i.e. across the web, from
competitors and prices are available in other regions. Dynamic pricing algorithms consider factors such as
competitors’ pricing, consumer behavior, location, time of day, and seasonality to determine how much
shoppers are willing to pay for a product or service. Many machine learning programs have been designed
to collect and analyzing data from a variety of sources like loyalty cards and postal codes, to predict what
the customer is willing to pay and how responsive they might be to special offers. Most importantly, once
the patterns are revealed, they can adjust and determine the best prices for new products that are
appropriate for the customer (Kietzmann et al 2018).
Though dynamic pricing now in its infancy but will grow exponentially. Pace has developed a
program that enables hotel management to design pricing that will match with supply and demand. This
allows hotels to maximize their profits by offering the best price that customers are willing to pay based
on various parameter including demographics, time of year, spending pattern, etc. It will help the hotels to
forecast the demand (De Jesus 2019). Similarly; Shartsis has developed a “Perfect price” program which
enables companies, such as car rental companies, to do dynamic pricing. Traditionally, car rental
companies segment based on time of day, boosting morning prices to match business travelers who are
assumed to be more willing to pay (De Jesus 2019). Perfect Price determines if a certain car shows higher
demand in a specific area and at a specific time of day, resulting in surge pricing while not affecting other
car classes. More importantly, the system needs minimal human oversight. In the future, using Google
48 Journal of Strategic Innovation and Sustainability Vol. 14(3) 2019
Tag Manager (GTM) and Perfect Price many businesses can adjust the product price. Incompetiror,
developed by Intelligence Node is a retail product index that gives the user access to competitors’
catalogs and pricing, allowing the user to use those prices as a benchmark for their own pricing structure
(De Jesus 2019). Moreover, based on the application’s product index, it allows businesses to view and
compare their competitor’s products to determine if theirs are overpriced, about equal, or underpriced.
Similarly, using this information E-commerce business can know exactly which products shoppers want
(De Jesus 2019).
They could access rich market data down to individual SKUs, product attributes, categories, and
brands and gain visibility into product catalogs. There is another program “Wise Athena” which helps
companies determine the best pricing for their products and trade promotion decisions. Wise Athena also
claims to have the ability to automatically select a product’s data attributes or specifications, and it
computes for loss in sales volume, as well as the revenue or market share of a product when the same
company launches a new product. It also computes for the potential change in the product’s demand when
the price for other product changes, as well as competition, leading price, and total sales (De Jesus 2019).
Wise Athena reports that their system updates its machine learning models monthly to maintain or
increase prediction accuracy. Navette PricePoint controls, manages, and measures pricing (De Jesus
2019). It consists of modules that group similar products, optimize local list prices from sales companies,
dealers, and distributors, and takes into account incentives, discounts, and payment terms which help the
businesses optimize prices. Business like Airbus, Tetra Pak, Olympus, Kia, and General Electric
Healthcare are already using this program to design the price (De Jesus 2019). However, the role of AI in
pricing is in its infancy and it will grow exponentially in the future.
AI Helps in the Delivery
Recently more focus has been given to “user experience” i.e. creating richer, more tailored, and more
convenient for the user. Today’s business is all about making every customer feel special and welcome,
which is not an easy task. This used to be difficult and expensive and was often reserved for only the most
lucrative clients. AI technologies like computer vision and machine learning has changed it completely.
For example, a regular supermarket shopper puts a bunch of bananas in his cart, cameras or sensors could
relay the information to an AI application that would have a good idea of what the shopper likes based on
previous purchases. The app could then, via a video screen in the cart, suggest that bananas would be
delicious with a chocolate fondue, which the purchase history suggests the shopper likes, and remind the
shopper of where to find the right ingredients (Mortimer et al 2018, Bughin et al, 2017). Or a runner could
download an app from an athletic shoe company, which would monitor her exercise regimen and
recommend footwear tailored to her routine and running paths she may like. Amazon has built a retail
outlet in Seattle that allows shoppers to take food off the shelves and walk directly out of the store
without stopping at a checkout kiosk to pay (Metz 2018, Bughin et al, 2017). The store called Amazon
Go, relies on computer vision to track shoppers after they swipe into the store and associate them with
products taken from shelves. When shoppers leave, Amazon debits their accounts for the cost of the items
in their bag and emails them a receipt (Metz 2018, Bughin et al, 2017). Delivery through drones is now a
reality. Since Amazon successfully delivered a pilot delivery in rural England in 2016 there a surge in this
area. Google partnered with Chipotle to deliver burritos at Virginia Tech, Dominos Pizza with Flirtey
completed a commercial delivery of pizzas in New Zealand (Druehl et al 2018). UPS has partnered with
drone company Zipline and governmental organizations in Africa to coordinate emergency medical
supplies delivery (such as blood) in Rwanda (Druehl et al 2018). Amazon now routinely gathering data
from drones during home delivery to target future purchases. From healthcare to education to
transportation in every sector, AI is providing the ideal tools for operation management (Druehl et al
AI in Smart Retailing
AI enables the retail and manufacturing businesses in making smarter decisions, with more accurate and
real-time forecasting, improving supply management, defining impactful thematic promotions, and
Journal of Strategic Innovation and Sustainability Vol. 14(3) 2019 49
optimizing assortment and pricing. AI also making operations more efficient, because of robotics and process
optimization which enhances productivity and reduces manual labor costs. Use of interactive Robots in the
warehouse and store are well known. The advancement in enhanced vision is enabled by more powerful
computers, new algorithmic models, and large training data sets. Within the field of computer vision, object
recognition and semantic segmentation—that is, the ability to categorize object type, such as distinguishing a
tool from a component—have recently advanced significantly in their performance (Wen et al 2018, Bughin
et al, 2017). They allow robots to behave appropriately for the context in which they operate, for example by
recognizing the properties of the materials and objects they interact with. They are flexible and autonomous
and capable of safely interacting with the real world and humans (Bughin et al, 2017). Companies like
Swisslog, DHL, etc. are using these technologies efficiently (Wen et al 2018).
However, there are many hurdles yet to be crossed before we get the full potential of AI. The first and
foremost is to gain the confidence of the stakeholders i.e. managers and employees, and those involved in the
regulatory and policy-making boards. As discussed earlier robots are gradually being adapted to perform
packaging and delivery. However; we still do not know how to address technical difficulties.
AI has enabled retailers to increase both the number of customers and the average amount they spend by
creating personal and convenient shopping experiences. Retailers now know more about what their shoppers
want— even before shoppers themselves (Deb et al 2018). AI forecasts from patterns and volumes of data i.e.
previous transactions, weather forecasts, social media trends, shopping patterns, online viewing history, facial
expression analysis, seasonal shopping patterns, etc (Fildes et al 2018, Burgess A 2018). The best examples
are Amazon, Hulu, Netflix etc. Similarly; a European retailer improved its earnings before interest and taxes
(EBIT) by 1 to 2 the percent by using a machine learning algorithm to anticipate fruit and vegetable sales.
The company automatically orders more products based on this forecast to maximize turnover and minimize
waste. A German company i.e. Otto cut surplus stock by 20 percent and reduced product returns by more
than two million items a year, using deep learning to analyze billions of transactions and predict what
customers will buy before they place an order (Burgess A 2018). AI technologies help retailers predict future
store performance when expanding their physical footprints. Now retailers optimize their storage space and
location using AI. Another important aspect of The retail industry is merchandising. AI helping in
merchandising, with opportunities to improve assortment efficiency. Using geospatial and statistical
modeling, they predict and minimize their stock. Amazon has embedded AI at the core of its operations. In
the retailer’s warehouse at Seattle, machine learning algorithms steer thousands of products over a maze of
conveyor belts and deliver them to humans just in time to fill shopping bags. Other robots whisk bags to
delivery vans whose drivers are guided by an AI application that picks the best route based on weather and
traffic conditions (Fildes et al 2018, Burgess A 2018).
AI in Smart Manufacturing
Use of AI has transformed the manufacturing sector, from virtual assistants to advanced robotics, has
enabled the manufacturing companies to produce more with fewer errors to adept demand. Using AI
helped them in rapid growth as they can shorten development cycles, improve engineering efficiency,
prevent faults, increase safety by automating risky activities, reduce inventory costs with better supply
and demand planning, and increase revenue with better sales lead identification and price optimization,
etc. (Patel et al 2018, Bughin et al, 2017). The new concept i.e. “Intelligent manufacturing” is a smart
approach for production where machines are linked to humans i.e. both machine and humans are working
side-by-side with minimal guidance. The best example of intelligent manufacturing is the manufacturing
sector of Siemens2. The employee manages and controls the production of programmable logic circuits
through a virtual factory that replicates the factory floor. Via barcodes, products communicate with the
machines that make them, and the machines communicate among themselves to replenish parts and
identify problems (Bughin et al, 2017). As high as 75% of the production process is fully automated, and
99.99988% of the logic circuits are defect-free. Similarly, AI and 3D printing have revolutionized
customization in manufacturing. Intel has developed Predictive analytics using machine learning a
powerful tool to reduce the time required to solve design problems for semiconductor manufacturers
(Burgess 2018). Motivo, an artificial intelligence startup, managed to compress semiconductor design
50 Journal of Strategic Innovation and Sustainability Vol. 14(3) 2019
processes from years to a few weeks, saving chip makers the cost of iterations and testing3. Using
machine learning, aerospace manufacturing industries has developed productivity tools for engineering
teams i.e. team travel norms, team composition, and supplier communication, etc. Machine learning has
reduced its development costs by unleashing the speed, accuracy, and relevance of products. AI has
allowed manufacturers to integrate production and client feedback in real time to refine the product
design. With suppliers, AI-based tools provide better accountability throughout the supply chain, which
helps aerospace manufacturers as well. For example, manufacturing a jet requires 1000s of parts and
procurement of these parts from around the world is a complex challenge. AI technologies like Virtual
reality in manufacturing link thousands of different parts most importantly, it provides transparency on
supplier machine availability, performance, and downtime, etc. It helps in balancing the supply chain and
optimize inventories in real time (Kraus et al 2018).
Using AI manufacturers are optimizing the key performance indicators and review it in real time. For
example; tailoring of a model using virtual programs help to better predict, identify, and prevent material
and staffing bottlenecks and optimize energy consumption. More importantly, it alerts the engineers
before problems arise and recommend solutions. AI enabled manufacturing not only efficiently assembly
line practices but also cut costs, reduce waste, and speed time to market (O'Reilly et al 2019). Using
machine learning algorithms, collaborative robots, and self-driving vehicles have been proved to improve
warehouse costs and reduce inventory levels as well. The best example is when General Electrics turned
to Kaggle, the platform for predictive modeling and analytics competitions, and invited data scientists to
design new routing and machine learning algorithms for flight planning that optimized fuel consumption
by looking at variables such as weather patterns, wind, and airspace restraints. The winning routing
algorithm showed a 12% improvement in efficiency over actual flight data (Henriques et al 2018).
The key feature of AI enabled manufacturing is collaborative agility, which is the ability to adapt
almost instantly to changes in demand and the evolution of regulation, input prices, technologies, and
other parts of the industry landscape. Now smart Robots are working with humans collaboratively for
mass production of products which is essential for customer-centric products. AI has helped
manufacturing plants around the globe in supply chains, and value chains which are more interconnected
and collaborative (Klumpp 2018). AI is also not far behind in agriculture. The concept of “e-plants in a
box” is a reality which is great for small-scale, low-capital-expenditure, mobile plants that can produce a
limited range of products at a competitive cost. Huxley combines machine learning, computer vision, and
an augmented reality interface to essentially allow anyone to be a master farmer. More importantly, these
e-plants can be transported to markets where demand is temporarily strong and in remote markets where
production must be the local and low cost (Bughin et al, 2017).
The technological advancement in mobile computing, artificial neural networks, robotics, storage of
huge data on the internet, cloud-based machine learning, and information processing algorithms, etc. has
propelled the use of AI in various business sectors. Many businesses are using AI in major parts of their
value chain as AI delivers significant competitive advantages. Most importantly, AI technologies have
helped them eliminating many levels of manual activities including promotions, assortments, and supply
chain. The e-commerce business using AI to predict the trends, optimize warehousing and logistics set
prices, and personalize promotions etc. Some even go one step ahead like anticipating orders and shipping
goods without even waiting for purchase confirmation. Similarly, Smart Manufacturing is now a reality.
However, there are many changes are needed to fully get the benefit of AI, more importantly, the changes
will compel many companies (retail and manufacture ring) to adopt new strategies i.e. plant designs,
reshape their manufacturing footprints, and devise new supply chain models. Also, companies need to
change the way they do business, as there will be a transition from human operators to AI enabled
machines and robots. It is good to note that the trend in global industrial operation driven by AI is
exponentially increasing, which suggests AI has either already or becoming a priority for many
corporations around the world.
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... According to Bansal et al. [1], smart source chain management AI helps minimize errors in the competitive modern world. AI and machine studying are established to modify the appearance of the supply chain business by making it smarter [2]. ...
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Smart supply chain is a must, not a choice, in the age of Industry 4.0 to meet global demands. This paper attempts to classify how Artificial Intelligence (AI) contributes to smart supply chain organization by systematically reviewing the existing literature. The article focuses on addressing the current research gap of artificial intellect in smart source chain management. Additionally, the research paper tries to identify how AI and Microsoft 365 are used in smart supply chain management to improve their effectiveness. Thus, the paper identifies the existing and possible AI techniques and Microsoft dynamics 365 to facilitate the research and practice of supply chain management. The main areas covered in the study on how AI is used in smart supply chain management include AI in intelligent delivery management, implementation of AI in Facebook, AI in smart retailing, and AI in smart manufacturing. In addition, the research demonstrates how smart supply chain utilizes Microsoft 365 by focusing on Supply Chain Managing and Microsoft Energetic forces 365, the Benefits of Microsoft Energetic forces 365 Supply Chain Management, Why Microsoft 365 Should Be Used in Smart Supply Chain Management, and Features of Microsoft 365 in Smart Supply Chain Management. This research paper offers perceptions via orderly examination and synthesis. Moreover, the research provides recommendations on how an intelligent supply chain can be improved using artificial intelligence.
... Utilizing AI in warehouse management has several advantages. Improved performance of warehouse robots; accelerated pace at which shipments are received, identified, sorted, and pulled; increased productivity because the the use of artificial intelligence technologies to simplify and centralize operational activities inside the warehouse (Dash et al., 2019;Pasonen, 2020;Pervaiz, 2020). Predicting consumer trends, analyzing inventory, and planning streamlined transportation procedures like loading, shipping, unloading, and delivery are all made easier and more timely by AI systems' usage of sophisticated neural networks and machine learning (Jayathilaka, 2021a). ...
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Technology has historically played a role in shaping international trade, but the current explosion in Artificial Intelligence has the potential to radically alter global commerce in the years ahead. In this research, we hypothesized that the AI capability of a nation has a major impact on international trade. This study discusses different ways in which technological advancements in the AI domain are improving global trade. We tested the hypothesis using the WDI, Government AI Readiness Index panel dataset of 150 countries for the years 2018-2021. Fixed effect, and Random effect panel models were applied. The results show that the AI capability of a nation has a major positive influence on trade. The findings also show that GDP and exchange rate have significant positive impacts, and inflation and trade restrictions have negative and significant impacts on trade. The findings of this study recommend strengthening the nation's AI capacity to increase its trade volume. AI will stimulate better economic development and open up new avenues for international trade to the extent that it fosters productivity growth. However, governments will need time to adapt and employ new AI technology, since doing so requires significant financial investments, access to skilled people, and a shift in how international companies are operated.
... Tanpa pendidikan dan latihan inovasi pasti terhenti. Melalui Pendidikan dan latihan, strategi pengurusan yang terbaik dapat dijalankan dalam proses produktiviti pengeluaran dan dapat memaksimumkan keuntungan (Dash, McMurtrey, Rebman, & Kar, 2019;Lu, Li, Jiang, & Ding, 2020;Widarni, & Bawono, 2021). Pendidikan dan latihan juga penting bagi menguruskan sumbersumber organisasi iaitu produk dan perkhidmatan. ...
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HDC mensasarkan sebanyak 50,000 syarikat akan berjaya memperolehi pensijilan halal pada tahun 2030. Namun pada masa kini, HDC berdepan dengan cabaran apabila masih banyak syarikat belum mendapat pensijilan halal. Perkembangan ini menunjukkan keperluan strategi yang boleh dijalankan untuk meningkatkan kemahiran sumber manusia dalam pembangunan industri halal Malaysia. Oleh demikian, kajian ini dilaksanakan melalui kaedah kualitatif melalui temu bual dan kajian kepustakaan untuk mengkaji peranan pendidikan dan latihan dalam meningkatkan kemahiran sumber manusia dalam pembangunan industri Halal Malaysia. Temu bual dijalankan dengan Bahagian Hab Halal Jabatan Agama Islam Malaysia dan HDC. Hasil temu bual disokong dengan kajian kepustakaan iaitu menganalisis buku, artikel jurnal akademik, kertas kerja, laman web, statistik, dokumen, dan keratan akhbar. Hasil kajian mendapati Bahagian Hab Halal JAKIM dan HDC memainkan peranan dengan memberi pendidikan dan latihan dalam meningkatkan kemahiran sumber manusia agar pemain-pemain industri halal seperti syarikat-syarikat, agensi-agensi kerajaan, dan pengguna yang terlibat dalam industri halal dapat meningkatkan lagi pengetahuan, prestasi dan kualiti produk halal seperti pemasaran, kreativiti, dan pembungkusan produk. Aspek pendidikan dan latihan yang telah diusahakan dalam membangunkan industri halal Malaysia dilihat dapat membantu meningkatkan kemahiran sumber manusia dalam pembangunan halal dan pengurusan sumber-sumber pengeluaran produk dan perkhidmatan halal dengan lebih baik.
Conference Paper
Capacitance-Resistance model (CRM) has been a useful tool for fast production forecasts for decades. The unique combination of simplicity and physics-based nature in this data-driven approach allowed it to stay as an object of scientific interest and get its own place among other types of models capable of giving predictions on flow rates, such as full-scale 3D reservoir models. However, the model simplicity, assumptions, and limitations does not allow wide application of a conventional CRM in complex field cases. A vast majority of studies on CRM are about overcoming its limitations by introducing new coefficients, modifying the analytical form of the equation, etc. Integrating CRM with rapidly developing artificial intelligence (AI) methods seems to be a logical continuation of model evolution. Recently introduced Physics-Informed Neural Networks (PINN) can preserve CRM's governing equations and coefficients that gives some insights about wells and formations standing out from other popular machine learning and deep learning methods. Moreover, PINN type models give certain flexibility in the choice of architectures – it means that the model architecture can be changed in a way that may assist in solving different problems. Thus, we introduce end-to-end learning of neural networks (NN) while implying some physical constraints. It is intended to overcome one of the major limitations of CRM, which is obtaining predictions for oil and water production rates from total liquid. This way, the additional training of rough approximation fractional flow models that are either not suitable for the case or may require the knowledge of reservoir properties is not needed. In this work, the well-known concept of Capacitance-Resistance models appears in a new form, which allows performing history matching rather rapidly, achieving robustness and forecasting liquid, oil and water production rates simultaneously. To test this new approach, several datasets (both synthetic and real) were used. The results obtained by PINN are compared to those obtained by a conventional CRM. By conventional we mean the analytical solution, which was modified by our research group to take into consideration common real field cases such as shut-in wells, workover operations, etc. by introducing dynamic characteristic coefficients [1].
Conference Paper
Accurately forecasting demand is one of the most undervalued and complex strategies that can significantly impact organizations bottom line. This industrial field study was co-conducted with Sumitomo Corporation's Tubular Division which primarily deals with high-grade Oil Country Tubular Goods (OCTG) globally. The presented solution demonstrates how with the right data set (drilling sequence data, stock data and consumption data), artificial intelligence can be used to build out a model that can quantify and predict future demand accurately thereby reducing cost, working capital and emissions. Multiple multi-layered machine learning models were built to compare and analyze a wide variety of data inputs for bill of materials, operational/project schedules; This includes (a) ‘product movement data’ which describes the changes in demand and supply of a product, (b) ‘product specification data’ which describes the characteristics of a product, and (c) ‘activity specification data’ which describes the characteristics of an activity. The models follow the base temporal map design with different weighting on model inputs. With a temporal map, a sequence of monthly data values (called lags) is used to predict the next monthly value in the sequence. The lags are rolled so that there are six months of data for the model to predict on. All models also use boosted decision-tree-based ensemble machine learning algorithm. It is critical to understand how product movement metrics (actual and safety stock levels, historical forecasts, and consumption patterns), product specification data (lead time, product grade, well function, well category, work center), and external factors (oil price, rig counts, national budget, production targets) can be utilized together to better understand future product demand. Using historical data acquired from drilling operations and supply chain over an eight-year period, multiple machine learning models were trained to predict one year of demand across the most consumed products. Across five years of predictions (2016 to 2019), the models were able to predict with 78% average accuracy for the top 10 products by volume which represents 75% of inventory volume. Across the same time-period, they were able to predict with 73% average accuracy on all 17 products which account for 80% percent of inventory volume. Further iterative updates with additional data led to improvement in results and the model where the model predicted with an improved accuracy of 83% on the top 17 products and an accuracy of 86% on the top 10 products. Moreover, the data can also be used to generate dashboards featuring metrics on material uncertainty / velocity and expected differences between the internally predicted forecasts and actual sales. The results further indicate that, on average, and within a simulated environment (where shipping delays were not considered for instance,) the AI model can maintain a lower inventory than the originally planned stock levels at lowest cost and footprint. This would not only lead to less resource consumption, but also reduce the embodied carbon and emissions within the overall process. This novel study presents the success of a validated tailored AI model for inventory forecast with field data and commercial implementation. Such a tool can be integrated into other value adding digital tools, such as integrated schedule optimization, logistics optimization and management systems to make overall operations more efficient and sustainable with lower costs, inventory, wastage, and reduced emissions.
Conference Paper
Artificial Intelligence (AI) has significant potential to optimize practices, processes, and energy consumption along with maximizing yield, quality, and uptime. This has substantial impact on putting organizations on the path to net-zero, as such optimizations can reduce greenhouse gas emissions by 20% with minimal capital investments. This comprehensive study presents proven industrial case studies that delivered economically strong strategies coupled with sustainability practice and providing strategic insights to identify, manage and/or attenuate the associated impacts. Environomics presented in this study is a novel framework which deals with unifying economic strategies with sustainability practices (through artificial intelligence) for optimal business performance in terms of finances but also environmental impact. This is achieved through a track, trace, and optimize approach for resources (particularly emissions, energy, water, waste, materials,, and safety) This was achieved through a combination of AI methods such as unsupervised machine learning, multi-variate optimization, and the implementation of similarity measures. A few of the inputs included well data (including production data, drilling data, completion data etc.), logistics/supply chain data (scheduling data, production inventory, mobilization data etc.), safety data (near-miss, observations, hazards, disciplines and insights etc.) with associated costs and emission data. Multiple industrial case studies are presented where sustainability metrics are identified through validated AI models to optimize productivity while reducing emissions and inventory. For instance, well profiling can be used to identify historical parameters that have maximized production potential while optimizing for aspects such as cost or emissions. Furthermore, we can identify the optimal completion parameters for a new well which satisfies carbon targets, use well profiles to build an optimized drilling schedule that meets budget or production criteria while still achieving production targets and optimizing drilling rig routes. Thus, the approach can quickly (within run time) solve interrelated environomic challenges in the reservoir studies space and the field development space. Further case studies indicate that the supply chain can have immense optimization impact on scope 3 aspects with results indicating 30-50% asset utilization improvement with respect to fleets (Vessel, Truck, Rigs). With respect to materials, a 10-20% reduction of material inventory levels all improved through AI. As the workforce are also part of the environment it has been observed that identifying unsafe behaviors within a large operation, also leads to enhanced sustainability behaviors. The models indicate potential of overall emission reduction ranging from 12-20%. This led to the comprehensive framework presented in this study to support sustainable practices that are also economically feasible and deployable. The real-time sustainability metrics generated has immense values in terms of decision-making processes and scenario generation in a fraction of the time that is required using traditional approaches. In addition to assessing the scope of impact, a novel multidisciplinary study and framework is presented to analyze environomic strategies to propose a market-oriented approach through the application of artificial intelligence. Furthermore, industrial, and academic case studies have been evaluated to identify, predict, and optimize the crucial parameters within such workflows that are effective in reducing resources utilized and associated emissions.
In developing the highly useful technologies, knowledge from human factors and ergonomics (HF/E) can be of great use, especially to designers charged with the difficult task of dovetailing humans and machines in complex systems built to navigate sometimes chaotic environments. The role of HF/E in A ³ design remains centered around the goal that A ³ self-action is ideal to provide maximum benefit to humans while increasing the likelihood of task success. This chapter is written between “artificial intelligence (AI) winters,” times of decreased funding of AI technologies, indeed at a time of great optimism and investments in A ³ technologies. It argues that design is key to all A ³ technologies, and that the advent of autonomy which has no need for humans is not only unlikely, but likely undesirable. The chapter endeavors to provide tools from the HF/E literature with which to shape the development of A ³ with respect to our knowledge of human factors.
Occupational biomechanics is an interdisciplinary field in which information from both the biological sciences and engineering mechanics is used to quantify the forces present on the body during work. Biomechanics assumes that the body behaves according to the laws of Newtonian mechanics. The approach to a biomechanical assessment is to characterize the human–work system situation through a mathematical representation or model. This chapter focuses on the information required to develop proper biomechanical reasoning when assessing the physical demands of a workplace. It presents a series of key biomechanical concepts that constitute the underpinning of biomechanical reasoning. The National Research Council reviewed the scientific literature in epidemiology, biomechanics, tissue mechanobiology, and workplace intervention strategies and concluded that there is a significant relationship between workplace design and the occurrence of musculoskeletal disorders of the low back and upper extremities. Shoulder pain is believed to be one of the most underrecognized occupationally related musculoskeletal disorders.
A manufacturing system is assumed to be comprising a combination of machines, cells, intra-logistics devices and other peripheral devices, used on the factory floor as well as on logistics. This chapter focuses on AI at the manufacturing system level. At system level, the volume and a variety of relevant data increase, activities are characterized by a higher degree of uncertainty and stochasticity, with several interdependencies among the parameters in non-linear relations. All these inherited attributes make transparency, predictability and adaptability more challenging tasks for AI. More specifically, the chapter examines (i) AI for the design of a manufacturing system that relates the design to process and the machine selection, the system layout as well as the capacity planning, (ii) AI for the operation of manufacturing systems that require planning and control of the material and the information flows and (iii) digital platforms and ICT technologies for the development and deployment AI applications in manufacturing systems. For each category, the scope and theoretical background are provided and then, selected cases of AI applications are discussed.
Disruptive events with damaging consequences afflict supply chains across industries. The survival of the business and its consequent recovery depend on the supply chain's resilience. This exploratory article discusses how technology-driven real-time decision-making in a connected supply chain achieves intended business outcomes of resilience, agility, and visibility. Based on an Integrative Literature Review and adopting a Design Science Research Methodology (DSRM), we propose a distributed approach for real-time inferencing in edge near the data sources for rapid autonomous decisioning and recovery planning under disruption. We develop a framework for building resilience in the supply chain using real-time distributed information sharing in a collaborative partner ecosystem. Visibility across the supply chain is ensured with a Digital Control Tower by making information available to any connected node for synchronized action. The important contribution of this research is building a real-time decision framework for sustainable resilience-building in resource-constrained organizations unable to invest in big data and enterprise systems. A set of design propositions following the CIMO framework is expected to help scholars and practitioners alike. A research agenda is provided for the researchers to take forward hypothesis formulation and empirical validation on the basis of the propositions. (194 words)
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Most people in the future will not need to work, at least in the ways in which we continue to think about work/human labour. In this chapter, we discuss the role of humans in the future economy. We begin with a discussion of the evolution of the integration of robots into the economy. Then, we turn out attention to the economics of robotics and AI, showing how these technological changes alter the economy and how markets and political responses may unfold. Then we discuss how humans can remain competitive in the new economy, developing skills that are needed and how educational institutions will have to change to address the new economic reality. Finally, we conclude, showing that humans will have to see their relationship to the job market differently and there will have to be an appropriate political response to the new economic landscape with changes in taxation and new ways of ensuring economic and political stability.
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This paper deals with utilization of ontologies and conceptual structural models for improving safety data management in civil aviation maintenance, repair and overhaul organizations. It provides an overview of existing safety data management and identifies its shortcomings. Current approach to safety data management is based on global standards and regional regulations, which stem from long-term experience but recent development of advanced technologies and the demand for their versatile application creates new potential for further improvements of aviation safety. One of the technologies which have strong potential for application in aviation are ontologies and conceptual models. Ontologies deal with philosophy-motivated description of reality and conceptual models are ontology and object-oriented tools for building concrete description, the so-called conceptualization. Their value is recognized especially with regard to data management, such as data collection and processing. This is of extreme importance in modern knowledge management systems, such as safety management system, to optimize control mechanisms or to support research and development activities, which are highly sensitive to data quality, such as Safety-II concept. This paper explores the capabilities of the models with regard to current state-of-the-art in aviation maintenance and identifies strong points for domain application. Subsequently, framework for the technology deployment to the industry is outlined. Due to the non-existence of safety management requirements for aviation maintenance organizations and with regard to the current development of aviation safety in other aviation organization types, there exists strong willingness for such framework application and, according to the analysis performed, it represents one of the desired solutions for aviation industry.
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AI techniques combined with recent advancements in the Internet of Things, Web of Things, and Semantic Web-jointly referred to as the Semantic Web-promise to play an important role in Industry 4.0. As part of this vision, the authors present a Semantic Web of Things for Industry 4.0 (SWeTI) platform. Through realistic use case scenarios, they showcase how SweTI technologies can address Industry 4.0s challenges, facilitate cross-sector and cross-domain integration of systems, and develop intelligent and smart services for smart manufacturing.
This paper reviews the research literature on forecasting retail demand. We begin by introducing the forecasting problems that retailers face, from the strategic to the operational, as sales are aggregated over products to stores and to the company overall. Aggregated forecasting supports strategic decisions on location. Product-level forecasts usually relate to operational decisions at the store level. The factors that influence demand, and in particular promotional information, add considerable complexity, so that forecasters potentially face the dimensionality problem of too many variables and too little data. The paper goes on to evaluate evidence on the comparative forecasting accuracy. Although causal models outperform simple benchmarks, adequate evidence on machine learning methods has not yet accumulated. Methods for forecasting new products are examined separately, with little evidence being found on the effectiveness of the various approaches. The paper concludes by describing company forecasting practices, offering conclusions as to both research gaps and the barriers to improved practice.
Facing imminent disruption, many large, established firms have embraced innovation as a way to develop new growth businesses. To succeed in the face of disruptive change requires established firms to master three distinct disciplines: ideation, to generate potential new business ideas; incubation, to validate these ideas in the market; and scaling, to reallocate the assets and capabilities needed to grow the new business. This article illustrates how two successful firms (Amazon and IBM) have developed approaches that address all three disciplines.
CyberSightings is a regular feature in CYBER that covers the news relevant to the Cyberpsychology community, including scientific breakthroughs, latest devices, conferences, book reviews, and general announcements of interest to researchers and clinicians. We welcome input for inclusion in this column, and relevant information and suggestions can e sent [email protected]
In the not so distant future year 2025, there lives a robotic engineer named Jenny. Her house is completely run by a house monitoring robot called GENIE. Jenny's house is equipped with a lot of smart common items, such as a smart refrigerator and smart cupboards. Jenny woke up and was made ready by her house robot, which gave her a bath and prepared her breakfast. Like everyone else in the year 2025, Jenny works from her home office, which is upstairs. She moves upstairs to take an early morning conference call with her robot boss from her home office. She does not have to worry about ordering common household items and food since her entire house is smart and connected to her house monitoring robot. Her smart refrigerator creates a list of items that are getting exhausted, like butter, cheese, jam, etc. The machine learning application inside the smart refrigerator uses sensors and images to check on the number of items which need reordering. In this age, the smart refrigerator talks to Jenny and finds out her preferences for a new product, which she orders for herself. Based on this feedback, the refrigerator remembers and removes the things from the reordering list that Jenny does not like. Her house has smart cupboards that have smart containers for each common product, a separate smart jar for coffee beans, salt, sugar, etc. Smart containers weigh the quantity that is stored in them and flash an alert to the smart cupboard when they reach a perfect reorder threshold weight inside them. These tools aggregate all the orders at the end of the week and send them to the house GENIE. The house GENIE collect the orders from the smart refrigerator the smart cupboard and other such devices and then shows the complete order to Jenny. The house GENIE also finds out from the internet the current offers and discounts that are available for each of the products to be ordered and shows Jenny the cost savings that she can have by selecting them. After the house GENIE reports the order list, it takes voice feedback from Jenny and makes modifications to the order and then places it. In this new modern world, all the retail stores have their own marketing robots that connect to the house monitoring robots and take orders. Drones are used by the retail stores to make home delivery of products. The items arrive at Jenny's home, and the house robot takes delivery after checking all the items through visual inspection—another use of visual machine learning. The biggest advantage for Jenny is that she does not have to be involved in mundane tasks, and the robots carry them out with efficiency. This is how the retail industry is going to change our society in the future..