#ArtificialIntelligence Archives - Page 2 of 2

How AI is likely to transform the Consumer Packaged Goods (CPG) Industry

Posted on 16/06/201917/06/2021 by Tushar

The future is Artificial Intelligence (AI), and various industries are investing heavily in creating self-evolving AI applications. The CPG industry (consumer packaged goods) has till now been somewhat dormant and is looking at opportunities to improve efficiency and reduce expenses. Amazon, Microsoft and Facebook have been among the front runners in this domain. Amazon, for instance, spends more than 10% of its yearly revenues on Tech research, whereas top CPG companies are still at 1-2%. This is changing and changing fast, more and more CPG players recognize the hidden power and are putting in robust efforts in the area.

Expansion of AI Application in the CPG Industry

Overall, the possibilities of AI application in the CPG industry is infinite. However, presently the state of AI application is still at a stage of infancy, lagging far behind other sectors such as retail and technology. Even though investment in AI from CPG companies has considerably increased, most companies are still working on identifying the critical applications with high business impact. In 2015, CPG firms, on average, spent 0.66% of revenue on AI application. This percentage is expected to keep increasing until a precise evaluation of their AI maturity is established. This article will discuss the areas in which CPG companies can expect to find successful applications of AI.

Consumer Feedback

Receiving feedback from customers at a massive scale usually involves leveraging natural language processing (NLP) programs for sentiment evaluation. Fundamentally, NLP focuses on teaching a machine to infer the gist of raw text. It is tremendously valuable but more complicated and resource-demanding than processing structured data. Structured data is greatly systematized and easily cognized by machine language. For instance, an AI program will be easily able to compute names, credit card numbers, geo-locations, stock data, etc. Analyzing customer sentiment, on the other hand, requires much more resources. The analysis is only the first step. A comprehensive AI-powered system must also be able to integrate ways to convey this analysis to the company’s customer feedback manager in plain and simple terms so that essential modifications can be made.

For instance, Hitachi devised a way to analyze customer feedback in a bid to reduce food wastage. They conducted a test in a hospital where trolleys mounted with cameras were used to collect trays from patients. The camera clicked images of the leftovers, and machine learning was used to detect the patterns of leftovers. In future servings, these wasted food items were not included in the patients’ meals.

Supply Chain

Another heavily researched field of AI application in the CPG industry is forecasting consumer demands. The data of wasted and sold food items in the past can help businesses efficiently forecast market demands. At the retail level, supermarkets will be able to stock precise amounts of food, considerably reducing wastage and avoiding stock shortages. CPG companies can easily supervise product locations and stock availability using AI tools that will eliminate the need for manual labor and boost effectiveness in logistics.

For example, when a major apparel company was faced with exceeding supply chain expenses, their products were not being able to reach potential customers, making lost sales a critical problem. Even a 1% recovery could provide a substantial increase in its yearly revenue. The company implemented AI to examine its products and discover how in-demand they were in the eyes of the customer. This application of AI was able to forecast precise classifications by store and by item. The AI program was able to predict which store would sell which item, ranking each item in terms of expected demand. Based on this analysis, the company was able to cut down on excess inventory and provide its customers with improved product-availability.

Marketing

A recent Nielsen survey revealed that over seventy per cent of CPG investment in marketing fail to breakeven. The major problem that CPG companies have when it comes to marketing is that they fail to integrate forecasting and planning in order to find the best promotional solutions. AI has the capability to introduce a data-driven method for CPG industry marketers. Powered by historical data, they can easily detect which marketing avenues are expected to generate maximum returns. An efficient program will be able to forecast and make recommendations on whether an in-store marketing tactic like – ‘buy two, get one free offer’ is the most effective for a specific product or brand, or if television advertisements will give the desired results.

AI programs can evaluate thousands of scenarios, incorporating even the littlest amounts of data before providing the ideal suggestion on which promotional channel will deliver the best results. Providing marketers with vital data like this is the only way for CPG companies to implement operationalized marketing projects on a large-scale yet cost-effective manner.

Pitfalls to avoid

CPG companies find themselves in a perfect place to make the most out of the AI boom. The technology is widely accepted as useful, and there are several verified methodologies shaped by other industries. By analyzing companies that are already reaping the benefits of AI application, the key takeaways include –

AI as a means, not an end – CPGs can apply AI to various aspects of their business operations and get augmented results. However, this is only possible when AI application is treated as a means to help workers, not eliminate them. For instance, when applying AI in marketing, any substantial discoveries or predictions should be provided to the experts in the marketing team so that they can then make even more informed decisions.
Streamlined Approach – CPG companies should avoid incorporating AI into every aspect of the business. Launching ten initiatives at once will more than likely result in those projects being stuck in the development phase for the next ten years. Companies must narrow down on one or two aspects of their business in order to have a better chance of delivering mass-scale results.

CPG company heads must stop viewing AI investment as “research projects” and welcome it as a way of carrying out day to day business tasks. Accepting the use of data-driven models in departments where employee intuition has always led operations can be a challenging and combative change. There is lot that CPG companies can achieve by using AI applications to support business but choosing the right initiative may be the key to its successful implementation.

The Rise of AI in Art: Ushering in a New Era of Creative Machines

Posted on 06/02/201919/06/2021 by Tushar

The Rise of AI in Art: Ushering a New Era of Machines with Creative Behaviors Just a few years ago, AI seemed like some futuristic tech straight out of a Sci-Fi movie. But the tables have turned now. We probably experience AI-based tech more often than we think.We come across at least one instance of AI in our daily life – be it a product recommendation algorithm on an online shopping platform or text auto-correction in our smart phones.Recent developments in AI, have begun to question the very characteristic of human nature which makes us unique, i.e., creativity. AI is already creating a myriad of visual art, poetry, music, and likewise, which bear an eerie resemblance to real human art.The Creative Leap of AI

Can we really imagine intelligent machines rivaling human creativity? Somehow, it’s still difficult to imagine a mathematical algorithm to be creative, isn’t it? But, not anymore.What we’re witnessing is that AI can be creative, even artistic. Recognizing and sorting images is one thing, but how about creating those from scratch?

Intelligent AI systems are now capable of creating artwork using certain algorithms. Much like in the creative world, where there is no set of rules to be followed, similarly to create AI art there are no particular rules. Thousands of images are analyzed and then the algorithm generates a new image. In the same way as we accessorize our paintings and those get better, AI too includes stylistic processes to generate images. More importantly, AI art does not replicate what humans do; rather it replicates the actual human thought process and enhances human creativity – a process called as “co-creativity”.

Interestingly, Creative adversarial networks (CANs) – a set of machine learning frameworks – maximize deviation from established styles. Human artists are directing the code with a desired visual outcome in mind and some really interesting artwork is being made. Quite artsy, isn’t it?

Collaboration is the key

Since AI has officially entered the world of art, it seems we’re getting overwhelmed and been thinking AI to be a threat to the art world. Here’s the catch – AI works best with human collaboration. Basically, AI uses algorithms that are fed to it. The more you feed, the better and easier it gets. Seemingly, AI works best when there’s an amalgamation between human creativity and modern machines. It’s not the technology alone that makes the difference but rather the knowledge and creativity of humans. It means AI is never going to replace humans in art as there is a requirement for real creativity, the real human emotions.

Impact of AI algorithms in Art: Overcoming the Limitations of Human Creativity

Creativity is attainable. But, human creativity has its limitations. This is where AI comes to your rescue and solves your problems. AI emulates and enhances our creative thought process in art and business as well. AI art or art created with neural networks has recently surged up with being hotshot “AI artists” on the rise. With an algorithm named AICAN, a solo exhibition was held in New York having each of its portraits sold for $6000 to $18,000.

Another example is Unsecured Futures, a solo exhibition to showcase artwork – drawing, painting, sculpture and video art – by Ai-Da, the first ultra-realistic humanoid robot artist. The brainchild of Gallery Director Aidan Meller, Ai-Da is capable of using her eyes and pencil for drawing people from life using AI-based algorithms. This exhibition actually questions the human relationship with technology but interestingly, it was a grand success that earned Ai-Da around 1 million pounds worth of artwork.

Applications of AI have found their way into the music industry too. AI-based algorithms are being used by musicians in their live performances, studio production in the form of various plug-ins and software. Moreover, some of the current AI technologies have successfully composed entire songs.

The best example would be Bach’s music, which integrates math into its music following a structured pattern, and can be easily replicated by AI. Facebook AI Research (FAIR), whose research team has created high-fidelity music with neural network is another such example.

The song “Drowned in the sun”, written by AI Magenta and launched by Google is also a work of art. Google’s latest Poem Portraits is another such example of how far the field of AI has come in the past few years. New AI tool – Deep Nostalgia – animates the faces in old photos to make them look alive.

Recently, GPT3 – the third generation of the language predicting deep learning algorithm took the world by storm by generating some of the most human-like conversations such as poems, stories, articles, etc.

Could AI be the Future of Art?

Humans have been raising the bar from drawing machines to generating arts using AI. Needless to say, AI has transformed our society and has changed the way we interact with technology. Though its impact upon us is greater, still there will always be negative consequences associated with it. But it would be hasty on our part to predict that AI will take over our life.

When GPT3 was announced to the world, it received a mixed response; one of utter amazement as well as deep concern. The age of the Industrial revolution witnessed machines replacing humans as a better alternative.

Now, this raises the question – will we be replaced by AI algorithms in the same manner? Are the algorithms the better alternatives? And if so, will it be for the greater good of mankind? These are some genuine reasons for concern.

Algorithms are a product of the human thought process. AI is not as artificial as we might deem it to be. AI algorithms merely implement our thought processes on a computer. Hence, all that an AI can create is a product of collaboration among humans. We feed in the data that other humans have generated.

Art by AI algorithms is a reflection of the global creativity of mankind. They are an ideal representation of what we, as individuals, have put into the world.

On this World Art Day, let’s rejoice in the artistic results of a synergistic collaboration between humans and intelligent machine systems.

The Supply Chain AI Hype and the Importance of a Digitized Supply Chain Control Tower

Posted on 07/01/201917/06/2021 by Tushar

The hype around Artificial Intelligence is far from fizzling out anytime soon. Digitalization and big data have completely penetrated the supply chain industry and are ubiquitous in nature. This article discusses one of the more interesting trends in the current supply chain analytics space – The Control Tower.

The concept of Air Control Towers and the Evolution of Digital Control Towers in Supply Chain

One may wonder if supply chain control towers have any correlation with air traffic controllers? To be honest, yes, there is!

An air traffic control tower (ATC), is a service provided by on-ground staff (controllers), who direct aircraft on the ground and through controlled airspace; they can provide advisory services to aircraft in non-controlled airspace. The primary purpose of ATC worldwide is to prevent collisions, organize and expedite the flow of air traffic, and provide information and other support for pilots (wiki). In short, the tower helps Improve flow, reduce emergency like situations through tactical interventions and provide inputs for right decision making. In fact, the ATC’s can now be enabled for an ‘auto-pilot’ mode wherein complex decisions are taken without human interventions. Only in cases where there is an absence of reliable data to make a trade-off, is where the humans intervene.

The digital control towers aim at keeping a bird’s eye view on the events occurring within the supply chain ecosystem (controlled and uncontrolled space), with the modus operandi being very similar to a generic air traffic controller. With the help of this consolidated view generated by the digital control towers in supply chain one can gain powerful insights about the current happenings within the organization. These insights help in improving flow across the organization, reducing urgencies and providing insights and tactical support to supply chain managers to make effective decisions. In fact, in the longer run, very similar to the ATC’s of today, the Supply chain control towers should have the capability to make complex decisions when there is adequate reliable data.

Significance of Digital Control Towers in Supply Chain

Corporations today want to leverage the useful applications of the supply chain control tower. Organizations have copious amounts of data across their supply chain and related functions. Over the past few years, they have managed to build business intelligence and analytics solutions to drive decision-making but at a node level. Extracting valuable insights using the right sets of data, lying across various nodes in an organization while also utilizing market intelligence, to deliver real-time visibility and provide meaningful insights that can drive decisions that are optimal cross organization, is the need of the hour. E.g., with the expected slow-down in sales on specific SKUs, a client may wonder if their manufacturing plant need to continue producing to plan OR does it make sense to course correct and lose capacity?

While an ATC is designed to minimize errors by incorporating huge factors of safety and commonly understood rules of engagement between various players (airlines, pilots, other ATCs), supply chain digital control towers have the luxury to experiment under statistical variability. E.g., Try different stock norms and check the impact on service levels, see whether a reduced Order-to-delivery promise induces better productivity and hence improved customer service levels and so on. This ability of a supply chain to experiment, try and fail or succeed quickly, at nominal cost can help build a virtuous loop of innovation with in a supply chain and drive a cultural change.

Most organization today recognize the impact a control tower can have on their organization. For a global organization, it is probably one of those platforms that will steer the supply chains of the future. Many organizations have tried implementing a control tower, but there have been very few examples of success. More often than not, organization fall short of implementing a “gold-standard” control tower capable of – real time visibility, predictive alerting, identifying bottle-necks to supply chains and providing insights that can drive decision; instead they end up implementing a large set of dashboards, that showcase different KPIs important to the various nodes in a supply chain.

This possibly is because of challenges that are faced when implementing an initiative as large as a control tower.

How does a Digital Supply Chain Tower work?

The SCCT should help an organization in making 3 key decisions – a) Ensure smooth flow-paths across the supply chain, b) Identify or predict bottlenecks / constraints to flow, and c) Derive efficiency/utilization improvement opportunities in the current network.

Hence, some of the key functionalities that are required would be:

End to end data connectivity: Ability to go beyond creating reports and tools that are not unidimensional but are able to work with data from different nodes in a supply chain is important.

Visibility: SCCT should provide visibility of key supply chain KPIs (simple and complex KPIs). They should showcase the right metrics, while also be able to project the impact of a decision on the metric real-time.

Analytics: Supply chain control towers are equipped with and boast of analytical tools and applications. With the help of these tools, supply chain managers can easily run what-ifs, and take calculated decisions. They can, easily harness the power of predictive analysis to detect ‘tripping points’, identify triggering alerts, as well as conduct root cause analysis of the data to arrive at solutions and address challenges.

Execution: The real benefit of the SCCT lies in the way the control tower communicates with the executive and the operational teams across the supply chain and allied functions. Hence this an important aspect of SCCT adoption within an organization

Key Challenges to Implementing a Supply Chain Control Tower

The supply chain control tower, unlike a typical analytics project, entails involvement from multiple functions and geographies across the supply chain (like the involvement of multiple VPs/SVPs in large enterprises).

Implementing SCCT would mean working with a team having – a) Different priorities, b) Very different data maturity and data quality, and c) Different products and software (some archaic and some new-age).

Some of the key challenges that appear during the construction and execution of SCCTs include –

When the SCCT implementation is picked up as a priority exercise by a single function within a supply chain without getting the other key function buy-in early into the transformation, there is a high chance the implementation will hit multiple road blocks.
Many a times, people tend to implement the most complex piece OR the piece of SCCT that seems most interesting. This may lead to no tangible results for an extended period, thus leading to lack of enthusiasm from fringe teams.
Data maturity: Different functions may have different levels of data maturity (availability, quality etc.). Inability to assess and map this aspect will tend to escalate timelines and cost.
Sometimes the implementing partner makes the mistake of selling the SCCT, not as a strategic tool that can transform business functions but rather as another software that will improve business efficiencies. This will lead to wasted effort that implementation will get driven in completely wrong direction.
There are number of proven analytics tools and products that exist with the client. Integrating these existing tools/products in the SCCT roadmap, may cause issues during implementation but will help adoption.

A typical issue of not successfully overcoming these challenges is that companies go down the path of SCCT implementation (visibility, predictive analytics, decision tools etc.) but end up implementing an end-to-end KPI dashboard. Though the dashboard may still bring in benefits, it causes disillusionment amongst the client project team in terms of SCCT capabilities.

Some of the ways to mitigate these challenges and move towards a successful implementation include –

Treat SCCT implementation as a strategic initiative and not as an IT implementation. Hence, it is critical to have someone high in the business team (CSO level) bless the initiative.
When prioritizing sprints, give equal weights to simple but quick wins – this motivates the client’s project team.
Always assess the current tools and products in client environment, i.e., prioritize integration over innovation.
Continuous engagement with all functions (even if there is nothing happening in a specific function) is important and should be made into a practice.

Tag: #ArtificialIntelligence

How AI is likely to transform the Consumer Packaged Goods (CPG) Industry