Lately, there have been many papers, articles, and discussion on digitalization and the digital transformation of the chemical industry. However, the messages and conclusions often seem very, well, abstract. Buzzwords like internet of things, artificial intelligence, big data, autonomous operations, smart automation, smart supply chains, smart you-name-it and so on spread, but without a clear message as to how they affect the business and what, specifically, should be done.
This debate does convey one common message: digitalization is important for future growth and sustainability.
I consider myself a systematic, straightforward and simple person. I’ve always had trouble with abstract definitions and terms, because if you don’t dig deep enough into the subject, you can only assume what the author wanted to say. And you know what people say: “assume” makes an “ass” out of “u” and “me.”
Just as an example, let’s consider the term “internet of things.” What first comes to your mind? To what does this term apply? Now, ask this same question your colleague, a spouse or a friend. Are your answers identical?
What we’ve learned during our latest research into the R&D process in the chemical industry (focused on paints and coatings) is that digitalization doesn’t have to be so abstract. We can be very specific about what we want to digitalize and what we need to do to make it happen.
First, you have to digitize, before you can digitalize!
Recently I came across a very simple but powerful definition of terms related to digital transformation (source: https://workingmouse.com.au/innovation/digitisation-digitalisation-digital-transformation):
- Digitization is the process of converting information from a physical format into a digital one.
- Digitalization is the process of leveraging digitization to improve business processes.
- Digital transformation is the impact caused by the process of digitalization.
Simple, right?
Please note that even the simple scanning of incoming paper documents is considered digitizing, but we’re already well past that baby step. Here, I’m referring to the next level digitization: converting information from a physical format into DATA.
For example, if you scan an incoming invoice, you still need someone to process it. But if you had a software that could parse invoices, from physical (document) format into numbers, you could process invoices automatically. Moreover, if we all agreed on the standard digital structure of invoices (i.e. xml), we could completely digitalize invoice processing and even payment processing (see e-invoicing).
It’s the same with R&D in the chemical industry: if you send a technical/safety data sheet to a customer in physical form (even as a PDF document), someone must process it – manually transcribing the numbers into a spreadsheet, or some other internal database software, for future cross-referencing. But we’re already past that baby step, right? Unfortunately, no. Not yet.
What do we want to digitalize?
Now that we’ve clarified these terms, we can look into the first question: What do we want to digitalize?
To answer this question, we have to understand what it is to digitalize. We could digitalize production, sales and marketing, purchasing, accounting and finance, etc. In other words, business functions.
So, in the chemicals industry, we have several business functions:
- Production
- Research and development
- Purchasing
- Sales and marketing
- Human resource management
- Accounting and finance
- Health, safety & environment / regulatory affairs
- Logistics…
Many of these functions, like purchasing, sales and marketing, HR, finance, etc., are pretty much alike across sectors and industries, and the implementation of solutions in chemical companies could be done without major adaptations (think e-invoicing, recruiting, digital marketing…).
But the core of the chemical industry, R&D and regulatory affairs, are too often stuck in classical workflows: an engineer measures a parameter (i.e. density) of a material, writes it down in their notepad then goes to the office, writes a report (in a Word file) and sends it (by e-mail) to a department in charge of maintaining a product database. They transcribe the data from the report to the database and then they ask their colleagues from the IT department to produce reports (i.e. technical data sheets). The IT team then produces reports in PDF form and uploads them to their web server.
On the other hand, when a customer requires information about a product, they get datasheets by e-mail (or they download them from the web server). After that, someone in the company must process the document – manually transcribe the data from the document into some internal software – before they can use the data in their internal processes.
You see the pattern: analog (manual to notepad) -> semi-digital (manual to word) -> digital (manual to database) -> semi-digital (database to PDF) -> website/e-mail -> customer (manual to database) -> digital (customer’s database).
Have you counted all those manual activities?
Now, think about updates and mistakes that could happen in the process of manual transcription at multiple phases in this process. Think about all the lost time and frustrations arising from tedious, non-value-added paperwork.
What do we need to do to make it happen?
Now remember: you have to digitize, before you can digitalize.
Here are some questions and examples to help you start your digital journey:
- What process do you want to improve (digitalize)?
- What data do you need to run this process?
- How and where do you get the data? How can you get it?
Manual storage management.
(I’ll try to avoid using the buzzwords mentioned in the beginning.)
| Business Function | Digitization
(data generation/collection) |
Digitalization
(using collected data to improve processes) |
Before digital transformation |
| Production | Smart manufacturing equipment constantly monitored by sensors that communicate with each other and collect data in a single database for real-time and future analysis. | Near-zero downtime. Improved production planning. Reduced cost of production. | Production stalls until the problem is solved; unpredicted production costs; delays. |
| Purchasing | Structured order collection (i.e. via web forms, CRM platforms, ERP systems) and automatic order processing. | Improved customer service, faster response times, improved planning. | Lack of overview of orders. Lost orders. Delayed delivery. Bad customer experience. |
| Sales and marketing | Reaching potential customers and generating leads through various digital channels (i.e. Google Adwords, LinkedIn, MailChimp, Facebook, Salesforce, web-forms…) and direct collection of leads in a CRM platform that enables segmentation, lead-generation and follow-up campaigns, tracking KPI… | Improved overview of potential customers, identification of relevant leads, lead qualification, improved customer experience… | Cold calling. Irrelevant leads. Lack of overview of customer behavior. Spam mail. Ineffective sales team. High cost of acquisition. |
| Human resource management | Recruiting via various digital/social channels and collecting data in structured web-form applications.
Employee timesheets connected to time and attendance tracking system, leave management… |
One-click-payroll, real time employee presence, predictions based on collected data. | Manual tracking of attendance. No real-time overview of presence. Manual payroll processing. |
| Accounting and finance | E-invoicing (see example above). | Automatic invoice and payment processing. | Manual invoice and payments processing. |
| Logistics | Storage: collecting detailed data on goods in a structured form (type, size, number of units, materials, contents…); classification of incoming goods; retrieving data from purchasing and shipment orders/systems; in-stock quantity; location of goods; availability of goods; scanning of incoming/outgoing goods…
Transport: collecting data on the location, speed, altitude, path of vehicles and retrieving data from ordering systems. |
Automated warehouses connected to purchasing and ordering systems provide real-time data on product availability à improved planning of purchase/delivery times; predicting the availability of goods…
Transport: reduced fuel consumption, improved path planning, improved customer experience… |
Manual storage management. No real-time overview of products in stock. Slow delivery times. Bad customer experience.
Transport: lack of planning. Inefficient and costly transport. |
| Health and safety | Collecting real-time data on workplace air quality, temperature, light, air pressure, humidity, etc. by various sensors (i.e. static sensors, wearable sensors). | Improving the health and safety of workers, environment protection, faster response to hazardous conditions, elimination of accidents, etc. | Responding to accidents after they happen. Inability to predict accidents. Unhealthy workplace environment. |
Digitizing R&D and regulatory affairs in the paints and coatings industry
What do all of the above examples have in common? Data is collected at the source and, in real-time, stored in a database. In production, sensors measure conditions on the spot, in sales and marketing, potential customers generate data through their behavior and activities on social media and websites, in finance and accounting, a supplier issues an invoice and it immediately appears in a customer’s digital books. No manual processing whatsoever.
In R&D in the chemical industry, product data is measured in a lab and then travels from person to person, hand to hand, while being manually transcribed multiple times before, in the form of documents, it reaches the end user – a formulator. It’s the same with regulatory information, like safety data sheets.
It’s time to take a step forward
To enable digitalization of R&D in the paints and coatings industry and to open the doors to further innovation, growth and a sustainable future, it’s time to take a step forward and start digitizing data on raw materials.
Register in Allchemist digital platform and start your digital journey now.
