3. 1. Human Resource Management
2. Water Management
3. Manufacturing Industry
4. Human Resource Management
• Of all the departments in an organization, the Human Resource (HR) department may have the least
popular reputation.
• This has two reasons. First of all, they are like a doctor: you’d rather never need one. When the HR
manager calls you and asks to come by their office, it’s likely that there’s something bad about to
happen. You may get reprimanded, put on notice, or even fired. If it was good news, like getting a
promotion, your manager would tell you. Not HR.
• Second, HR is regarded as soft. Fluffy-duddy. Old-fashioned. A lot of the work in HR is based on ‘gut
feeling’. We’re doing things a certain way because we’ve always done it that way. HR doesn’t have a
reputation of bringing in the big bucks or playing a numbers game like sales. HR also struggles to
quantify and measure its success, as marketing and finance do.
• HR analytics changes all of this. A lot of the challenges we just described can be resolved by
becoming more data-driven and analytical savvy.
5. Water Management
New digital technologies can introduce detailed measurement and near real-time monitoring
and reporting of water extraction, treatment, distribution, use and reuse, with the potential to
distinguish between different water qualities, sources, quantities and users. New governance
and decision support systems, backed by powerful digital solutions, will support the rational use
of multiple waters, based on the true value of water and new economic models, with minimised
impact to natural water bodies. As a matter of urgent priority, it is highly needed to define and
deploy a group of actions for the development of Digital Water Services in the single market.
These actions will provide clear signals to water stakeholders, operators and society on the way
forward with long-term targets as well as a concrete, broad and ambitious set of activities. Such
acts at EU level will drive investments and create a level playing field, removing obstacles
stemming from European legislation or inadequate enforcement, deepening the single market,
and ensuring favourable conditions for innovation and the involvement of all the stakeholders. 6
| P a g e The action plan for digital water services is based on gaps identified by the ICT4Water
cluster (ANNEXII) and is in line with The Digital Single Market Strategy main pillars
6. Manufacturing Industry
Dvanced big data analytics is a hot topic for the manufacturing industry. Manufacturers are generating
vast amounts of data through their systems, but are they using it to optimise overall operations?
First, let’s answer a basic question: What’s the added value of data analysis? It’s all about uncovering
critical information to enable smart operations and drive the business. Whether you look at your shop
floor, your supply chain or procurement, advanced analytics helps you identify patterns and
dependencies within your systems. By doing that you can make right decisions or optimise the whole
process. Typical use cases for manufacturing are:
•Predictive maintenance. Knowing when a part is going to break reduces downtime and waste. By
analysing factors that drive the wear of your devices, you gain transparency on the real lifetime of your
products.
•Automatic quality testing. Automating this task saves time and helps avoid human errors. Instead of
using manual checks, quality can be tested incorporating data from special test devices, X-ray scans,
photography, etc.
•Product optimisation. Understanding what drives the quality of your production avoids waste and
improves the overall equipment effectiveness (OEE). Advanced analytics identifies parameters that
cause variable levels of quality or efficiency.
•Supply chain optimisation. Anticipating the right time to produce orders or plan shipping dates enables
on-time delivery and resolves storage issues. Analysing the duration of individual processes and the
complex interdependencies among them provides information about transportation times and the impact
of disruptions.
7. Data analytics, machine learning and artificial intelligence (AI) in manufacturing aren’t just hype. If done
properly, they enable cost savings and process optimisation. Using them requires a professional
approach.Many analytics projects fail because stakeholders underestimate the degree of complexity
involved. To avoid such situations, manufacturers should address these areas:
•Analytics strategy. This is the DNA of your system and the main orientation point for the following areas. A
clear overall roadmap on where you are with all your different efforts will help you define your goals and
govern all the necessary steps.
•Gradual and agile approach. The following two areas go hand in hand. Perform them in multiple, step-by-
step cycles based on an agile minimum viable product (MVP) approach. Gradual investments thus show
tangible results and gradually overcome larger barriers:
• Utilities and pipelines. Infrastructure is key. You need the platform and hardware to process the data
as well as smart pipelines for gathering and storing the data centrally.
• AI experiments. Rapid prototyping and agile AI experimentation provide insights and determine the
right approach for optimising your system and operations. Many organizations expect that there are
ready solutions for their every need, but plug-and-play solutions are rare for most industries and
applied AI solutions and applications are still under heavy research. That’s why it’s essential to have
an experimental mind-set. The experimental stage is all about quickly identifying the most effective
analytics approach for your company, whether it’s an existing solution, a completely self-built system
or something in between.
•Operationalised AI. Finally, you need to operationalise the successful experiments. The entire workload
needs to be transferred from an experiment to a stable, maintained and enterprise-wide solution. This
could mean integrating it into a business application, or have it running as a micro-service in a modern
