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IoT: Biggest Revolution in Retail

If the IoT represents a huge opportunity for almost every facet of the business, this is particularly true for supply chain specialists, operations and analysis. The leaders of e-commerce and traditional commerce see an opportunity of competitive advantage in IoT.

 

Even though I’ve already wrote about IoT in my previous posts, let me give you again a quick definition of it. In 1999, Kevin Ashton (MIT Auto-ID Center) describes the Internet of Things as a network of interconnected objects that generates data without any human intervention. Today, Gartner describes the IoT as “the network of physical objects containing embedded technology to communicate, detect or interact with their internal states or the external environment.”

 

estimates for IoT revenue by region in 2020

For some IoT is only a new name of an old concept, the only thing which has recently changed in this existing concept, is the evolution of Cloud technology. According to a recent survey by Gartner, IoT is one of the fastest-growing technological trend. Estimation says that by 2020, the number of connected objects will be multiplied by 26 to $ 30 billion. Main reason behind IoT success is the development of solutions based in clouds; which allows to actually have access to the data generated by the connected objects.

 

The growth of IoT relies on three levers: reduction in integrated chips costs, technologies supported by a cloud platform and powered by analyzing Big Data and finally the Machine Learning. A case study of IBM named “The smarter supply chain of the future” revels that in near future the entire supply chain will be connected – not just customers, suppliers and IT systems in general, but also parts, products and other smart objects used to monitor the supply chain. Extensive connectivity will enable worldwide networks of supply chains to plan and make decisions together.

 

The main objective of such connective supply chain is to gain better visibility and to reduce the impact of volatility in all stages of the chain and get better returns by being more agile product flow. Several developments are already underway in the IoT and are revolutionizing the retail supply chain at various levels:

 

– At the client side: integration of end consumer in the IoT. The main objective of this step is to collect customer data to create customized product, personalized offers while simplifying the purchasing process. Devices such as health trackers, connected watches etc. continuously collect the data from consumers, prescribers. The collected data represents a great opportunity of positioning product/services. For example, from a person’s browsing history, its culinary tastes and influences on social networks, information on a nutrition bar can be offered to him. Recommendations may also be appropriate if the person enrolled in a sports club or acquired a fitness tracker and so on.

 
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– As for retailers: Beyond the preparation of the assortment by merchants, there are smart shelves and organization of sales outlet. Moreover, we are witnessing a rapidly changing purchasing behavior so with smart shelves a retailer’s system can analyze inventory, capacity and shipment information sent by suppliers. Via the predicted system retailers and suppliers can avoid costly out-of-stocks or missed sales.

To take the example of nutrition bar, time spent in front of a specific category of products (yogurt lightened for example) can be an early indicator to change suggestions or promotions. In addition, the integration of the retail IoT can allow the line to automatically trigger orders. The whole environment can be configured to access a library of planograms, to store inventory data and related warehouses to automatically run restocking. As the elements of this environment are already used independently, we can predict that we are at the dawn of IoT in retail.

 

If the store are at a less advanced stage in the application of IoT, transportation and warehousing are well connected. The integration of RFID shows a first generation data-oriented machine. Integrated tracking systems have long been used in transport and warehouse systems. RFID tagging of pallets has to have better visibility on the status of stocks and the location. The convergence of demand signals and increased visibility on the state of stocks and their location results in scenarios such as the anticipated shipment for which Amazon has filed a patent. Increasing integration of IoT can lead to efficient use of robots for material handling and delivery by drones. These innovations are challenging the effectiveness of existing systems by optimizing the machine learning an effective alternative.

 

Even with all the benefits it promises to offer companies, IoT is still a gamble, with big risks and unsolved problems. For any organization that decided to embark on the IoT, a number of questions remain open whether in technology, integration with file distribution systems to traditional ERP API to communicate with sensors and application languages ​​(Python, ShinyR, et AL.)

 

There are several interfaces that work well in specific areas, but it needs more standardized platforms. Industry experts have launched PaaS (Platform as a Service) to integrate this growing IoT technology. Despite these challenges, the technology seems a surmountable obstacle. Only the legislation on collected data is a real problem so far. Even the customer acceptance remains a challenge. In 2013, Nordstorm had to backtrack on his program which was to track customer movements by the Wi-Fi use on smartphones and via video analysis due to customers demand.

 

Finally, the important thing to remember is that the IoT is a revolutionary technology. A lot of expert retailers, e-commerce players and technology solutions providers will rethink and adapt the model and evolve in processes designed for organizations wishing to adopt the IoT. Retailers that take the lead in this space stand to gain an important advantage in an already competitive environment. Early adopters will be positioned to more quickly deliver IoT-enabled capabilities that can increase revenue, reduce costs and drive a differentiated brand experience. The IoT will be a disruptive force in retail operations.

 

 

Sources:

The Smarter Supply Chain Of The Future

The CEO Perspective: IOT for Retail Top Priorities to build a Successful Strategy

Machine Learning: An Artificial Intelligence approach

I’ve heard a lot of people saying that Machine Learning is nothing else than a synonymous of Artificial Intelligence but that’s not true at all. The reality is that Machine Learning is just one approach to AI (in fact it’s called the statistic approach).

 

Let me first give a definition of Machine Learning. It’s a type of artificial intelligence that gives computers the ability to learn to do stuff via different algorithms. On the other hand Artificial Intelligence is used to develop computer programs that perform tasks that are normally performed by human by giving machines (robots) the ability to seem like they have human intelligence.

 

If you are wondering what it means for a machine to be intelligent, it’s clear that “learning” is the KEY issue. Stuffing a lot of knowledge into a machine is simply not enough to make it intelligent. So before going far in the article, you must know that in the field of Artificial Intelligence, there are 2 main approaches about how to program a machine so it can perform human tasks. We’ve a Statistical Approach (also known as probabilistic) and Deterministic Approach. None of these two approach are superior to the other, they are just used in different cases.

 

The Machine Learning (=Statistic AI) is based on, yes you’ve guessed right, statistics. It’s a process where the AI system gather, organize, analyze and interpret numerical information from data. More and more industries are applying AL to process improvement in the design and manufacture of their products.

 

There’ll be around 5 to 20 billion connected devices within 3 years and so many capture points will be used to make live decisions, to recommend, provide real-time information and detect weak signals or plan of predictive maintenance. Whether it’s at the level of business uses, the sectors of industry and services (health, distribution, automotive, public sector …) or even the use of Business Intelligence, everything is changing! With the Machine Learning and voice recognition technology based on AI, even the Big Data technology might be quickly overtaken by real-time information.

 

In a preview of an upcoming e-book, “AI & Machine Learning”, UMANIS talks about The Data, machinery and men. In the e-book they have elaborated problems and expectations that different companies are facing in the technological era.

 

Based on the responses of 58 participants who responded to the survey “AI & Machine Learning”, here below you’ll find identified trends and indicators.

 

  • 44% of companies believes that AI and Machine Learning have become essential and latest trend in various fields including education, healthcare, the environment and business sector,
  • One company on two is curious about the technological innovations in order to understand the collection of data (via machine)
  • 1/3 of companies are currently on standby on AL & Machine Learning topics,
  • 21% of IT decision makers were informed about Cortana suites (Microsoft) and Watson (IBM)
  • 36% want to go further on this type of technology,
  • 88% are planning to launch an AL project within more than 6 months,
  • 50% of respondents are unaware of the purpose of these technologies in the company.

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TOP 5 issues:

  • Detect abnormalities
  • Using machine learning to optimize the automation
  • Integrating a Learning Machine module into an existing SI
  • Remodeling of the real-time Data architecture to gather big volumes with high computing power
  • Finding a permanent solution of storage and backup of the collected data

 

There’s no doubt that machine learning area is booming. It can be applied to high volumes of data to obtain a more detailed understanding of the implementation process and to improve decision making in manufacturing, retail, healthcare, life sciences, tourism, hospitality, financial services and energy. The machine learning systems can make precise result predictions based on data from training or past experiences. By gathering relevant information for making more accurate decisions, machine learning systems can help manufacturers improve their operations and competitiveness.

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