2017 was a landmark year for Cementos Molins, a leader in the European cement manufacturing industry, not only because it sold a record 6 million tonnes of cement, generating an EBITDA of $218 million  but also because it revolutionized its highly traditional operations through the application of artificial intelligence.
The cement production process
Cement manufacturing is a process consisted of two major steps with generally similar cycle times: the Mill, which grinds hard clinker to fine powder, and the Kiln, where this powder is heated at 1500⁰C to decarbonize the material .
The performance of the process –measured through metrics such as energy consumption, CO2 emissions, output quality, etc.– is depended on thousands of variables (e.g. humidity, vibration, external and internal temperature) which are continuously monitored. To optimize the plant’s performance, staff manually adjusts ~50-100 of these variables over regular time intervals (every ~10 minutes).
The relationship between these variables and the process’s performance, however, is highly complex and employees can only depend on their experience, rather than a predefined playbook, in order to deliver their job. Expectedly, this optimization process is very prone to human error, causing frequent blocking/starving, downtime, high energy consumption and thus large costs to the company.
To respond to these challenges, a growing number of plant manufacturers are applying artificial intelligence in order to both eliminate human error and maximize the variables’ adjustment frequency (from ~10 minutes to seconds). In the case of Cementos Molins, the management decided to develop a real-time self-repairing process, with the objective of maximizing production rate while keeping energy consumption to the minimum and product quality to the maximum.
Appling Artificial Intelligence in a cement plant
Given the company’s lack of experience, the management decided to partner, in the short term, with an external startup specialized in the real-time improvement of industrial processes through the application of Artificial Intelligence, Optimitive. Optimitive’s role in this stage was dual:
- To develop and apply the optimization algorithm to Cementos Molins’ factory in Barcelona, which resulted to the increase of the production rate by 3.8%, the reduction of the energy consumption by 5.5% and the improvement of the output’s quality by 3.1% in the first 6 months .
- To train a team of Cementos Molins’ management and factory staff on the development philosophy, application and use of the software. This would enable Cementos Molins’ to oversee the process and, in the longer term, expand the application of the algorithm to other factories.
Going forward, over the medium term, Cementos Molins’ plans to develop an internal team consisted of the trained employees as well as newly hired data scientists. This team will allow the company to continuously improve the algorithm, roll it out to other plants and train local staff whilst avoiding the high consulting fees of external partners.
Looking ahead – How to further leverage Artificial Intelligence
Cementos Molins has done a great start, entering the new digital era of cement manufacturing way ahead of competition. However, this is just the start of the journey. There are many more actions that the company could implement to enhance its operations and its operating margins, with two major ones being:
- Investing in a cloud service: This would allow information sharing between different factories; the algorithms applied to different plants would be able to learn from each other and further improve their performance .
- Adding a self-adapting functionality: The cement industry is highly correlated to the local economic environment whilst demand for different types of cement also fluctuates seasonally . Thus, adding a self-adapting functionality would be highly beneficial for the company since it could customize its manufacturing process and the type of cement it produces in response to demand fluctuations .
Challenges to address
Great changes are often coupled with great challenges. In my opinion, in the case of Cementos Molins the two most important points that the organization should address are:
Data homogeneity: Since the company’s ability to store data is finite, eventually the vast majority of the data stored will have been generated by the algorithm. Thus, the variability of the dataset will be limited and there will be no room for the algorithm to discover new combinations. This, in turn, means that the algorithm will not be able to further improve its performance, even though this may be feasible in theory. How could the company avoid this situation?
Ensure employee’ experience gaining: Employees must oversee the algorithm’s operation and occasionally to take control. However, since they will not actively participate in the process for the majority of the time, the experience they will gain will be limited and thus they may not be properly equipped to take control. How can the organization ensure that, in parallel to running the algorithm, the frontline of the factory will develop?
 Cementos Molins. Annual Report 2017. (2017). Available at: https://www.cemolins.es/uploads/media/B1-Listas/043-Informaci%C3%B3n-financiera_Memorias-anuales/Memorias_Cementos_Molins_ENG/CM2017(Financiera)_ENG_LR.pdf (Accessed: 10 November 2018).
 Globalcement.com. CEMENT 101 – An introduction to the World’s most important building material. (2011). Available at: http://www.globalcement.com/magazine/articles/490-cement-101-an-introduction-to-the-worlds-most-important-building-material (Accessed: 9 November 2018).
 Optimitive. Optimitive saves 5.5% energy and improves productivity and quality in Cementos Molins Industrial. (2017). Available at: http://www.optimitive.com/en/optimitive-news/233-optimitive-saves-5-5-energy-and-improves-productivity-and-quality-in-cementos-molins-industrial (Accessed: 10 November 2018).
 Brynjolfsson and A. McAfee. What’s driving the machine learning explosion? Harvard Business Review Digital Articles (July 18, 2017).
 J. Wilson, A. Alter, and S. Sachdev. Business processes are learning to hack themselves. Harvard Business Review Digital Articles (June 27, 2016).
Industryabout.com. (2017) Sinoma to build US$500m cement plant in Uganda. Available at: https://www.industryabout.com/industrial-news/783-news-cement/40423-sinoma-to-build-us-500m-cement-plant-in-uganda (Accessed: 13 November 2018).