AI training is based on a lot of data (big data). If the training is performed without the involvement of ready-made models, then this is called machine learning.

To solve problems with machine learning methods, knowledge from such areas as mathematical statistics and analysis, optimization methods, probability theory, numerical methods, graph theory, etc. is involved.

Machine learning is inductive learning, that is, learning based on empirical patterns and data. There is a certain sample – training sample. It is a set of situations (objects) and corresponding results (responses, reactions). An algorithm capable of producing a realistic response for any input signal (object) should be built. The performance of such an algorithm is measured using the quality functional.

In industry, machine learning is often used for pattern recognition. For example, an artificial neural network is used for this. The essence of learning is to find the coefficients of connections between neurons. Most importantly, a neural network is able to identify complex relationships between inputs and outputs, as well as make generalizations. So the neural network is able to finish what is missing, that is, restore the image.

There are several types of common tasks that are solved using machine learning in manufacturing:

1) Optimization of the production process,

2) Prediction of equipment failure.

I give a few examples for each item in the framework of the neural network training method.

Optimization of production processes is possible with the help of computer vision (pattern recognition). For example, the selection of objects moving along the conveyor. For example, a large stone can clog a crushing machine if it is not removed in time.

Predicting equipment failure can bring great benefits. Timely replacement of equipment will allow not to stop business processes. For example, the forecast for the replacement of a piece of pipeline, which is based on external and internal factors of exploitation. External factors are temperature, depth of burial, acidity and humidity of the environment, and so on. Internal factors are pumping volumes, composition, pressure, and so on.

However, for the successful digitalization of production, the customer, the owner of the production, should ensure the solution of some problems.

The main tasks that the customer must solve

1) Data collection. Sensors in modern production must be digital for this, information must be accumulated and complete.

2) Evaluation of factors. Only the owners of production or employees can set priorities correctly.

by Iren Kolov,

Analyst at CounselSenses

The post Artificial Intelligence and Neural Networks on Guard of Production appeared first on Digital Strategic Model.

Eneco Group wanted to decrease the cost of service while maintaining operational efficiency since Dutch Energy Regulator sets price limitations on expenses per connection. Eneco Group restructured and simplified its organization into process-streams to attain this goal and used digital tools.

As part of its mission to develop, build, and maintain grids and connections at the lowest feasible costs, Eneco Group employed QPR Suite to achieve operational excellence. The objective of Eneco Group was to maintain and grow the distribution grid while reducing the number of minutes of downtime per customer connection. For this, Joulz devised a two-pronged method for implementing a control system. The first was the implementation of an ERP system to automate operational procedures. The deployment of a business support management system to drive and monitor process excellence was the second step.

To support the process streams, Joulz chose SAP as its Enterprise Resource Planning (ERP) solution. From order through delivery and payment, SAP processes all installation, maintenance, and support operations effortlessly. Although ERP systems improve productivity, it was necessary to exercise caution when aligning automated operations with Joulz’ administrative and field operating tasks. To guarantee that the SAP system was properly implemented, test-drives against defined business processes were required. Once the system was up and running, it was critical that the process’s performance be constantly monitored in order to allow for operator intervention as necessary.

Going digital not only increased efficiency and reduced costs, but it also had strategic implications. Energy retailers may enter new growth sectors by using their scale, existing brands, and big customer bases. As a result of giving consumers its smart-energy solution, Eneco has boosted revenues and reduced attrition rates. Customers receive information on their energy usage, boiler performance, weather, and energy-saving ideas in exchange for a monthly membership fee.

The economic benefits of digitization led in a structural reduction of 75K euros per year on software and maintenance, as well as a total savings of 15 million euros.

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McKinsey, a management consulting firm, determined four elements of digital strategy that stand out in the most successful digital companies.

Increased agility of strategy

Perhaps the most important element of all, adaptability of the digital model makes a great difference: the quicker business creates, executes and adjusts its strategy, the greater is economic advantage. At least, that’s what comes from the study where McKinsey compared the frequency with which companies followed 11 operational practices of digital strategy. Respondents in the top revenue companies carried out each one more frequently than their peers.

Investments in digital

The most successful companies are taking advantage of new software ecosystems and innovation their operating models basing of those ecosystems. New digital platforms allowed to create new marketplace, share and analyze data of the unseen scale. Additionally, new ecosystems are providing the best understanding of customers’ needs.

Increased usage of M&A

Merge & Acquisitions is another stand-out element for top performers. As such, they’re investing more in different types of M&A activities. It’s not just about better financial capabilities to do so: top companies are spending more than twice as much on M&A as a share of annual revenue, comparing to less successful companies.

Investment in digital talent

While ecosystems and digital technologies are very important, people are still the important source of success. Even more – qualified digital talents are even more important. The ability to find them – and to use them at the right place at the right time – is the final element that distinguish top performing companies from others. Again, it’s not always about rough resources to attract employees, but about better allocation of digital talents across the organization. This allows to set up the best alignment between resources and strategies.

1 – https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/a-winning-operating-model-for-digital-strategy

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Earlier, Nike centered much of its strategy around the 2012 launch of FuelBand, a digital-fitness tracker. Release of Apple Watch pretty much killed this idea for good, but then they realized that data collected by software in the process was more valuable to Nike than developing the hardware itself. The Nike+ fitness app system for smart phones and watches now has more than 170 million members. Nike+ membership subscription was central to creating a more seamless “physical to digital” retail experience and lead to increased transactions and higher sales among Nike+ members.

Additionally, Nike has acquired multiple AI and data analytic startups to improve the understanding of consumer’s shopping behavior. Also, Nike has found a unique way to use AI: scanning customer’s feet in store and make the best style and size of shoe for that customer.

Going forward, Nike saw the technology a key component in increasing the “speed to market” . Introducing improved digital tools to product creation teams red lead to faster design cycles. Moreover, a couple of years ago, Nike partnered with various 3D printing companies and launched the first 3D-printed textile upper in performance footwear. The benefit was obvious: Nike was able to prototype 16 times faster than in any previous manufacturing method. All these new digital tools allowed Nike to leverage on collected consumer data to create the product based on client’s preferences.

All in all, instead of relying on other vendors, Nike improved its connection with customers through membership opportunities, stronger digital marketing and powerful data analytics. An end-to-end focus on consumer data better allowed Nike to connect with customers and recommend the right products. The improved digital focus gave Nike a faster product development cycle to quickly respond on market changes. All measures these proved to be vital at the height of pandemic.

Just like many other companies in the consumer and fashion industry, Nike was heavily affected by COVID-19 lockdown. Nike closed almost all stores in the USA, when the virus started to spread. The situation was similar for all other countries as well. Even for stores that remained opened, the foot traffic has declined significantly, which led to a drop in revenue and a net loss in the company’s 2nd quarter.

In response to the pandemic, Nike doubled down on efforts to grow online operations, strengthening its own e-commerce platforms and expanding app initiatives to drive digital sales. The result: Nike’s stocks now are worth 35% more than before the pandemic. All of it would be impossible if the company hadn’t started to go digital years ago.

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A new CEO, Satya Nadella, took over the Microsoft in 2014. Nadella was (and is) a great supporter of digital transformation. His very first idea was to shift the company towards cloud networking systems. It moved the focus away from traditional software to a more agile system for both personal and enterprise. Instead of neglecting global partnerships like they always did, Microsoft has changed their course to build relationships with other IT companies – as such, Office applications has appeared on competitor’s operational systems, which greatly improved the accessibility and convenience for end users. Subscription services became the main way to sell all the software, from aforementioned Office pack to videogames. Public view also changed, as Microsoft went from being seen as an stagnant sluggish behemoth to cutting-edge agile business.

The results of this transformation was impressive. In early 2014, stocks were selling around $40 per share. Today, they’re worth around $210. Over the 2014-2019 period, revenue increased from $93.5 to $122 billion. Microsoft became just the third company to get a $1 trillion market cap, which certainly wasn’t possible to imagine at early 2014.

Nowadays, Microsoft actively gives everyone the benefits of their experience with digitalization. Every company can work with Microsoft to start transformation of business. For the beginning, Microsoft has laid out four universal pillars of digital transformation, which can be used everywhere by everyone:

1. Empowering of employees. The company must provide to employees the opportunity to work from anywhere, on any device. Usage of cloud tech can greatly enhance overall productivity, as staff can continue their daily tasks even literally on the go, outside of their office.
2. Optimizing of operations. Optimization of all internal processes must start with IT department. This will help to reduce costs and safeguard business data of the company.
3. Engaging customers. People are expecting personalized interactions with business, as a result of ever-increasing technological advancement. If leveraged correctly, rightly-built connections can help to boost sales and build customer relationships. Customer engagement can be improved via Big Data and such.
4. Transforming the product. Modern business landscape requires innovation and agility, especially when it comes to transforming the data businesses possess into powerful assets.

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