(LSJ) Case Study: työhyvinvoinnin kehittäminen

Case Study

Työhyvinvoinnin kehittäminen keinotodellisuudessa

Virtual Reality (VR) keinotodellisuus ja augmented reality lisätty todellisuus tulevat muuttamaan edelleen työn suorittamisen tapoja ja paikkaa. 5G sekä 6G mahdollistavat näiden todellisuuksien hyödyntämisen paikasta riippumatta.

Miten luodaan työn tekemiseen tarkoitetut virtuaaliset (AR/VR) todellisuudet, joissa  työntekijät voivat innostua, luoda ja kehittää yhdessä?

Miten ihmisten jaksamista ja hyvinvointia tuetaan virtuaalisessa etätyössä ja varmistetaan tuottavuuden jatkuva kehittäminen?

Mitä muutoksia tarvitaan toimitiloihin, työnkulkuihin, ja luotuihin virtuaalimaailmoihin?

1. Mitä työnkulkuja voidaan kehittää virtuaalisessa todellisuudessa? 

2. Mihin business caseihin ja milloin voidaan käyttää laajennettua/ lisättyä todellisuutta?

3. Mitä voidaan automatisoida heti ja saavuttaa kilpailuetua, entä ensi vuonna?   

4. Mitä uusia työnkulkuja tarvitaan jotta kilpailukykyä voidaan tehostaa?

5. Miten uusi toimintatapa on huomioitu yrityksen strategiassa?

• Kuka hoitaa nämä liiketoimintaprosessien ja työnkulkujen muutokset?

• Kuka varmistaa että kyvykkyydet ja taidot on päivitetty?

• Miten varmistetaan että toimintaa kehitetään tietoturvallisesti?

• Miten kouluttaa henkilöstö tehokkaasti uuteen ajattelu- ja toimintatapaan?

• Miten menestyä yrityksenä entistä paremmin?

• Miten organisoidaan työn johtamisen muutos ja tuki virtuaaliseen työskentelyyn?

1. Millaista in-house coachingia ja tukea esimiestyöhön tarvitsette?

2. Millaisen etä / lähi toimivan esimiehen tarvitsette ?

3. Millaisia työelämäpalveluita ja henkilöstöetuja tulette tarjoamaan henkilökohtaisten työpisteiden ollessa melkein missä tahansa maailmassa?

4. Miten yrityksen tuotekehitys ja myynti järjestetään uuden toimintatavan aikana?

5. Miten liiketoimintaa kehitetään globaalissa virtuaalisessa kilpailutilanteessa?

Miten työntekijöiden kasvavaa tarvetta skaalataan muutoksessa? 

(LSJ) What robot can do in your business?

Spot-on on the days of new normal

Robots helping with strawberry picking, seasonal workers scenarios

Robots do not get Covid-19. They add social distancing to seasonal work.

In Europe, by yearly basis there is need of 1.000,000 up to 1.200,000 seasonal workers to work associated with the farm, the crop, vegetables, horticulture, fruit and potatoes, berry picking. In the new normal the problems caused by Covid-19 can be greatly helped if not fully solved with the assistance of harvesting robots.

“France is short of about 200,000 workers until the end of May, while Spain has a shortage of 100,000. Italy needs about 250,000 seasonal workers in the next two months, while the UK normally receives 80,000 over the season and Germany 400,000, Finland 16,000.
”

— ft.com

Cloudsourcing resources from Lifetime Group to build and continuously deploy the solutions include Consultants, Cloud engineers, DevOps engineers, Solutions Architects, Developers, Integration Specialists, AI engineers.. ..

The competitive edge for corporate level is tied ensuring the availability of the efficient core teams from Lifetime Group. Build skills solutions with Cloud Ready Teams from the Consortium.

Network and Computing Quality is key for any of these services. In the New Normal, increased company revenue is tied into network quality ie. 5G built-in, edge computing availability, robots and its tooling availability, Cloudsourcing resources from Lifetime Group to build and continuously deploy the solutions in the near-future.

Consumers get very personal assistants

The new normal opportunity give corporations roadmap how to monetize this early opportunity of personal assistant robots.

Securing the premises

Download the task for your personal assistant

Let’s drive to farm and do Strawberry picking - Scenario

In this new normal scenario personal robot is integrated into new electric car from Volkswagen Group. The Assistant offer driverless experience and help maintenance of the electric car by taking care of the charging station operations like connecting the cord, guarding the load process, unconnecting the cord, taking care of tires, fills in the wiper washer tank, and so on. The design of the car needs to be adjust for the capability of the robot.

The driverless car mode is adjusted for the driver personality so careful driver has more careful driverless mode driving where sporty personality is served differently.

The family goes for a ride in the countryside. Next to a farm, next to the strawberry field they see hand picking sign for 1 hour for 10 €.

Consumers want to subscribe to their own personal assistants today. The want to own new skills for their personal assistant. The edge computing connected to cloud computing is another technology we need to mention here.

Let your Spot drive the car to strawberry field. Add and activate the crop picking software to your personal assistance robot through 5G. Add Basket and hand picker tools from your VW. Push button on your car and let your assistant go pick basket full of fresh Strawberries. Choose from the data pipeline exacly how sweet strawberries you want to be hand-picked or spot-picked in this case.

What strawberries are ready to pick and which should be left alone for tomorrow?

How much strawberries are needed dictate the chosen container for strawberries

The conditions on the field and the demand for network quality, the robot load station can reside in the car.

Secure social distancing and improving the picking process quality.

Spot 2.0 robot from Boston Dynamics connected with 5G delivers product capable support for diaconia work for elderly, driver associated personal assistant for car life cycle/ driveless. The business opportunities in the consumer related are many.

IoT categories

Edge Computing

5G connectivity

(LSJ) What is Data Science Platform?

When building teams

Market Definition/Description

Data Science Platform definition

A cohesive software application that offers a mixture of basic building blocks essential for creating all kinds of data science solution, and for incorporating those solutions into business processes, surrounding infrastructure and products.

Artificial intelligence (AI) is hyped:

Hype about AI is at its peak, but AI must be distinguished from data science and ML. Of course, data science is the core discipline for the development of AI, and ML is a core development of AI, but this is not the whole story. ML is about creating and training models; AI is about using those models to infer conclusions under certain conditions. AI is on a different level of aggregation to data science and ML. AI is at the application level.

Data Science and ML models must be combined to work together with other capabilities, such as a UI and workflow management, to constitute an AI Application.

The Report finds variety of audiences to data science and ML platforms (modified):

• Citizen data scientists who are accessing (open) data and building data science and ML models. They come from roles such as business analyst, line of business (LOB) analyst, data engineer, and application developer.

• LOB data science teams who address initiatives in areas such as digital marketing, risk management, Customer Engagement Management.

• Corporate data science teams who have strong executive sponsorship, and can take a cross-functional approach. They do model building and end-to-end process for building and deploying data science and ML models.

• Star (Mavericks) Data Scientists. The Python, R, Apache Spark Gurus.

(Gartner Report Magic Quadrant for Data Science and Machine Learning Platforms, published 28 January 2019 ; by Analysts Carlie Idoine, Peter Krensky, Erick Bretheneoux, Alexander Linden).