Overview of chemical
trends.
An analysis of future trends of particular relevance to the chemical and pharmaceutical industry.
Overview of the analyzed trends
15 trends were identified.
Trends are nothing other than movements of change or processes of transformation, that shape the future world with their far-reaching influences on business, societies, economies, cultures and personal lives. You can find trends at a wide variety of penetration levels. This report showcases 15 selected trends that have already have, or are expected to have, a major impact on operational processes and the world of work in an operational and organizational way in the chemical and pharmaceutical industry. The 15 trends are clustered into three categories.
Digital technologies
Material technologies
Interaction & business processes
The 15 trends explained.*
Trends have a major impact on the development of existing and new skills. In order to better understand trends and link them to HR strategy or development measures, relevant skills are assigned to the trends below.
*Source: HRForecast data crawled from public sources (regions: Germany, EU, USA & China). Period analyzed: 01/2018 – 12/2019. Only data from the chemical & pharmaceutical industry was analyzed.
Digital technologies
Linked skills:
- Databases & database management
- Cloud computing (AWS, Azure etc.)
- Programming languages
- Data mining & intelligence
- Data warehousing
- Data infrastructure
- Data mining
Linked skills:
- Blockchain protocols
- Cryptography
- Distributed ledger technology
- Smart contract
- Real time payment protocols
Linked skills:
- Cloud security
- Data security
- Encryption
- Embedded security
- Fraud detection
- Network security
- IoT security
- DevOps
Linked skills:
- Modelling & forecasting
- Programming languages (e.g: Python, R, Matlab etc.)
- Data analytics tools
- Data visualization tools
- Data strategy & data models
- Data processing & integration
Linked skills:
- Deep learning & neural networks
- Advanced statistics
- Predictive analytics/ preventive maintenance
- Computer vision
- Edge computing
- Simulation and modelling
The processing of complex, large and dynamic amounts of data with modern data processing technologies.
Practical example:
Development of a database of all sorts of materials from experimental and computational chemistry.
Systems in which a record of transactions are maintained across several computers that are linked in a peer-to-peer network.
Practical example:
Blockchains help track, trace, and deliver processed chemicals and ensure quality or detect counterfeit chemicals with transaction data.
The practice of defending computers, servers, mobile devices, electronic systems, networks, and data from malicious attacks.
Practical example:
Cybersecurity aims to protect production assets, valuable chemical formula information and customer data bases from unauthorized access.
An inter-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structured and unstructured data.
Practical example:
Analyzing diverse data sources, such as experiments, photos and images to predict the chemical structure of a material.
The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making.
Practical example:
Simulating chemical reactions during the search for new materials, decreasing the time to develop by reducing the number of laboratory trials needed.
Material technologies
Linked skills:
- Computer-aided design
- Additive Manufacturing
- Extrusion based additive manufacturing
- 3D Printing technologies (stereolithography, powder bed and inkjet head)
Alternative feedstock
Linked skills:
- Bio and renewable feedstock
- Waste management
- Urban mining
- Plastic recycling
- Fischer-Tropsch synthesis
- Pyrolysis
- Coal to liquid fuels
- Syngas
Batteries
Linked skills:
- Battery management system
- Uninterruptible power supply
- Energy storage
- Hydrogen fuel cell
- Lithium polymer battery
- Lithium-ion battery
Biotechnology
Linked skills:
- Bioanalytics / bioinformatics
- Computational biology
- Personalized medicine
- Spectroscopic methods
- Upstream/ downstream process
- Continuous biomanufacturing
- Process modelling/ dev./ optimization
- Molecular characterization
- Nano medicine
Material sciences
Linked skills:
- New/ smart materials
- Coating and surface treatments
- Plastics engineering/ biodegradable plastics
- Liquid crystals/ OLED
- Lightweight materials
- Fibers strength
- Nanomaterials
- Polymers
Definition:
3D printing, also known as additive manufacturing, is the construction of a three-dimensional object from a CAD model or a digital 3D model.
Practical example:
In case of an asset failure due to a damaged part, the replacement part can be conveniently printed on spot and installed in time. In-house printing of spare parts reduces inventory costs and increases efficiency.
Alternative feedstock
The transition from petroleum and natural gas feedstocks to alternative, largely bio-based feedstocks.
Practical example:
Biomass as a renewable energy source, derived from plants and organisms that can be converted into fuel for energy, chemicals and polymers.
Batteries
Containers consisting of one or more cells, in which chemical energy is converted into electricity and used as a source of power.
Practical example:
Battery materials, such as advanced cathode active materials improve battery perfor- mance in terms of energy density and efficiency, which translates into a safer battery.
Biotechnology
A broad area of biology, involving the use of living systems and organisms to develop or make products.
Practical example:
The development of mRNA vaccination materials.
Material sciences
The interdisciplinary field of materials science, also commonly termed materials science and engineering is the design and discovery of new materials, particularly solids.
Practical example:
Nanomaterials – materials with a structure at the nanoscale and unique optical, electronic, thermo-physical or mechanical properties.
Interaction & business processes
Agile principles
Linked skills:
- Agile project management
- Scrum
- Scrum based software (Jira, Confluence)
- Agile organization
- Certified ScrumMaster (CSM)
- Continuous integration
- Agile software development
- Design thinking
- Lean & Six Sigma
Automation & robotics
Linked skills:
- Robotic process automation (RPA)
- Process optimization/ automation
- Control room dashboard
- Automation platforms (Blue Prism, UiPath)
- Digital simulator/ simulation methods
- Cyber physical systems
- PLC software
- Test automation
Digital sales
Linked skills:
- Writing, content marketing
- Customer data analytics
- UI/UX design
- Email marketing
- Marketing automation
- Search engine optimization
- Data science and machine learning
- Customer journey
- Web analytics
Internet of things & connectivity
Linked skills:
- Internet of things (IoT)
- RFID, smart sensors
- Networking and connectivity
- Embedded systems
- Wireless technologies (5G)
Virtual and augmented reality
Linked skills:
- Cross-platform game engine
- 3D computer graphics application
- Virtual reality devices
- Programming & scripting languages
- Unity framework
- Digital twin / factory simulation platform
Agile principles
Definition:
Agile principles aim at being able to act flexibly and, moreover, proactively, anticipatively and proactively in an organization in order to introduce necessary changes.
Practical example:
Agility emerged as project management methodology and then migrated into organizational and cultural transformations. In chemical product development, common agile methods are Scrum and Kanban.
Automation & robotics
The usage or introduction of automatic equipment in a process.
Practical example:
Cobots (collaborative robots) can be utilized to optimize the collaboration between humans and robots in a shared space, e.g. while aiding a chemical technician in hazardous laboratory experiments.
Digital sales
The ability to scale relationship building using social and digital channels.
Practical example:
Implementation of e-commerce platforms and marketing automation tools to attract clients via multiple channels, to scale client touchpoints, decrease costs and improve supply chain efficiency.
Internet of things & connectivity
Describes the network of physical objects—“things”—that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the Internet.
Practical example:
Sensors collecting data of production machines enable scalable data analytics that lead to optimized and predictive maintenance of machines, saving costs, increasing efficiency and allowing remote monitoring.
Virtual and augmented reality
Technology that superimposes a computer-generated image on a user’s view of the real world, thus providing a composite view.
Practical example:
Provides maintenance technicians the capability to provide effective training and feedback to staff remotely in real-time through hardware and software.
Drivers of change in the chemical and pharmaceutical industry
One in four job postings requires skills in the area of data science & analytics.*
The 15 analyzed trends are in different maturity stages, hence the impact on the industry varies greatly. Three phases can be defined (emerging, maturity, adoption phase).
High-impact trends – adoption phase: wide, large-scale adoption across the whole value chain with high impact on the industry today.
Low impact trends – emerging phase: still in an embryonal stage and therefore no concrete benefits yet in terms of agility, scalability and flexibility. Low impact on the industry today but close monitoring of trends recommended as they might emerge to impact the jobs in the industry in the short to mid-term future.
*Source: HRForecast data crawled from public sources (regions: Germany, EU, USA & China). Period analyzed: 01/2018 – 12/2019. Only data from the chemical & pharmaceutical industry was analyzed.
Share of the trends on all job postings
The trend with the highest impact on the industry is ‘data science & analytics’. Around one in four job postings requires skills in this area. The three other trends that have a very high impact on the industry are biotechnology, digital sales and machine learning/AI. Ca. one of six job postings requires skills in each of these 3 areas.
Drivers of change in the chemical and pharmaceutical industry
Impact of the trends on the functional areas.
Name of functional area.
List of relevant trends in functional area.
Additional explanations.
*Source: HRForecast data crawled from public sources (regions: Germany, EU, USA & China). Period analyzed: 01/2018 – 12/2019. Only data from the chemical industry was analyzed.
- Biotechnologies
- Material sciences
- Alternative feedstock
- Batteries
- 3D printing
- Big Data
Large data repositories provide intuitive access to data and free up R&D time for more creativity and productivity.
- Blockchain
Implementation of blockchain platforms to ease, validate and accelerate contract management and material negotiation processes .
- Automation & robotics
- Batteries
- 3D printing
- Agile principles
- IoT & connectivity
Collecting and processing data from sensors and utilizing robots to automate the production workflows.
- IoT & connectivity
- Big data technologies
- Data science & analytics
- Machine learning / A.I.
- Cybersecurity
- VR and AR
Algorithms that process continuous streams of sensor data to analyse, predict, and visualize data for improved maintenance of production lines, also known as predictive maintenance.
- Blockchain
Continuous collection and analysis of internal data (e.g. historic material consumption) and external data (e.g. tariff regulations, business cycles) to predict future material demands and bottlenecks for the supply chain.
- Digital sales
- Big data technologies
- Data science & analytics
Prediction of customer behavior to recommend appropriate products to customers through e-commerce platforms based on the customer’s buying pattern and requirements.
- Digital sales
- Big data technologies
- Data science & analytics
- Machine learning / artificial intelligence
Machine Learning techniques used to improve customer segmentation and predictive analytics to qualify and prioritize leads based on the segmentation.
- Big data technologies
- Data science & analytics
- Machine learning / artificial intelligence
- Cybersecurity
- Agile principles
The development of innovative, data-driven business models is transforming the IT function from a support function towards an essential value-driver in the value-chain of the organization.
- Data science & analytics
- Agile principles
- Automation & robotics
Replacing repetitive, manually performed processes with automation technologies.
- Big data technologies
- Data science & analytics
Analysis of employee data in connection with business data to better understand the workforce, e.g. detecting skill gaps, modelling future workforce supply or understanding drivers for churn.
