Roadmap to Industry 4.0

Author: Lauri Antalainen

In manufacturing, the foundation of digital transformation is data, more precisely – how data is created and used for decision making. While it is clear that suitable equipment is a necessity in order to apply Industry 4.0, it’s important to understand that just having suitable hardware and software is not nearly enough. Thus, in order to develop a roadmap to Industry 4.0, it is essential to have data creation and usage in operational decision making prioritized and analyzed. The road to Industry 4.0 can be thought of as depicted in the following figure.

At first, everything is simple

(1) At the most basic level, there is no digital data; therefore, all the decisions made are based on intuition. The CEO spends most of their working hours in the production hall, working with equipment, and monitoring the production.

(2) As the company grows and the CEO can’t be on the production floor all the time, a spreadsheet-based solution will usually be introduced. At first, the spreadsheets are mainly used for planning the short-term production pipeline. The decisions are more often than not still based on intuition.

Complexity creates the need for more thorough resource planning

(3) As the complexity in the company grows, so do the spreadsheets. Now there are many of them, for different planning and analysis purposes. The data in the spreadsheets are used to calculate product manufacturing costs and create simple BoM-s. The decisions are influenced by the data and statistics in the spreadsheets.

(4) At a certain point, the spreadsheet system limits begin to manifest in quality problems – deadlines are repeatedly missed and the number of scrap increases. There is no effective overview of what is going on in the work centers and when one or another product will be finished. Therefore, processes are optimized and an ERP/MRP system is implemented. A correctly implemented ERP system gives a good overview of things happening on the production floor and helps in estimating delivery times. In addition, ERP starts suggesting when to order raw materials or when to produce certain products.

Integration and automation

(5) The company continues to grow and while the production planning and manufacturing functions reasonably well, problems appear in other areas. These problems can manifest in different aspects of the business, such as warehouse management – finding the right products or semi-finished products from the warehouse starts taking more and more time and effort. The quantities in the customer orders have grown and mistakes on order assembly result in customer complaints. Therefore, additional solutions to digitalize warehouse management and other parts of the business are implemented. These systems are now integrated with each other and ERP, enabling automation in several aspects of the business. All actions are taken by human operators, but decision support tools present options and influence decision making.

(6) Additional solutions are implemented to gather and use real-time data. The data gathered from various sources is all real-time and, based on this data, AI-based solutions are developed and implemented to automate even more parts of the business. Several decisions (e.g. production plan) and actions are performed autonomously with human supervision and authorization.

The Factory is now Industry 4.0 ready

(7) All of the systems are integrated into one data realm where all the data is created and used in real-time. AI is implemented for making all the important decisions. Decisions and actions are performed autonomously with human supervision. High-impact decisions allow human operators to intervene and override.

It takes time and effort to apply Industry 4.0 principles and it is usually difficult to skip steps in this process. As mentioned earlier, the advances in data creation and usage need to be accompanied by investments into suitable equipment and hardware. Approaching this journey as described above gives good guidelines on prioritizing potential investments in the pursuit of Industry 4.0.

Digiwise helps with process improvement and digitalization. We have long term experience in many industries, including metal-, wood- and food- processing industries. If interested, please do not hesitate to contact us: info/at/digiwise.eu.

The business processes and planning
of a company

By: Lauri Antalainen

Every company has business goals that they wish to achieve. For example, with the Balanced Scorecard method, the financial, customer, internal processes and learning and development goals are defined to realise the vision and strategy. Many companies only define the financial goals, setting targets for the turnover and profit.

Source: Paul Harmon; significant additions by: Lauri Antalainen

A company employs business processes to achieve their business goals. Business processes are the day-to-day operations of a company, in the course of which the value for customers is created and the activities necessary for the operation of the company are performed. Business processes need to be developed and updated to make them smoother and more efficient. Business processes can be developed with different methodologies and approaches; for example, manufacturing companies often utilise LEAN thinking.

The company’s business processes are carried out by people, using technologies – machines, equipment, and software solutions. A business process can be changed only by changing people’s behaviour or technology, or both at the same time. People can be trained; processes can be digitalised and automated. Significant process developments generally include the digitalisation or automation of processes, by either software or equipment.

Business process modelling plays a key role in a digital transformation. – Jim Sinur

Digitalisation, automation, and other process development activities allow improving the key indicators of business processes, such as efficiency (output per unit of input), productivity (number of processes or output per unit of time), cost-effectiveness (cost of process implementation), capacity (number of processes performed per unit of time), and quality (e.g. the quality and security of supply). In turn, these indicators determine the (gross) profitability of the process and the number of processes carried out by the company per unit of time (for example, per day). In turn, the latter are a prerequisite for the achievement of the desired goals, such as the turnover and profit.

Digiwise helps you set goals for, consider, and implement digitalisation and process development projects. We have long-term experience in the digitalisation and automation of processes in companies operating in different sectors and at different stages of development. If you are interested, please contact us at info[at]digiwise.ee.

Data Science Seminar – 11.12.2019

Lauri Antalainen speaks of the connections between process optimisation and data science at a data science seminar of the University of Tartu.
https://www.uttv.ee/naita?id=29168

Information and knowledge management in modern society

What is information and knowledge management in modern society? Which competencies does a successful business analyst have? Why is the Institute of Social Studies a great choice for a master’s student with a passion for creating and managing information systems?
These issues were discussed in the study studio of journalism by Kaie Jeeser, Assistant of Information Management, and Maris Männiste, Assistant of Information Systems. Fredrik Milani, Lecturer of the Institute of Computer Science, and Lauri Antalainen, a successful business analyst, also offered their opinions on the topic.

The video is in Estonian

Business process optimisation seminar

At the seminar, we will look at the practical examples of problems that can be solved by business process optimisation and which benefits we can expect as a result. The seminar will be led by Lauri Antalainen from Digiwise (formerly CoreGrow).