1. Lack of visibility into material and energy consumption in cement production processes
The life cycle of cement plant assets often spans multiple decades. Over the course of their service, these assets have been controlled with legacy systems, which offer little insight into crucial production metrics. For instance, monitoring energy consumption at a plant level proves challenging, because the machines used in the plants do not come with such built-in capabilities. Moreover, most plants still employ paper-based, or manual digital processes to track material consumption in cement production.
However, the inability to track these crucial consumption metrics in real-time covers up the inefficiencies in the underlying processes. Overconsumption can start becoming a significant cost issue, especially with the increased cost of energy and wastage of material. All of these factors put cement manufacturers at a competitive disadvantage. That’s why our client wanted to build visibility into their material and energy consumption with sensor technology and automation.
2. Need for the capability to predict cement quality during production
Cement manufacturing is a chemically complex process and large-scale production calls for highly calibrated processes and consistent raw material quality. The type and quality of limestone, clay, and other additives will have a significant impact on the end product, as will the pressure, humidity and temperature at various stages, and duration of the kiln process. Because cement particle size distribution affects its performance in construction, achieving consistent quality is crucial to prevent quality-related SC events and to ensure customer satisfaction.
Until recently, cement plants employed manual processes to monitor cement quality. However, more recently, it has become possible to predict cement quality with novel sensor technology and machine learning. Our client wanted to gain insights into cement quality through its production processes to achieve the benefits of such a capability – i.e., the output of consistent quality cement, optimal processes that result in minimal waste, and improved customer outcomes.
3. Plant outages resulting from unplanned machine downtime impeding production
In the cement industry, a single day of unexpected plant outage can cost average manufacturers as much as $300,000.
This number can be even higher for larger-scale cement factories, and the cost of downtime has been on the rise in recent years. Being an industry that relies on several machines and fixed assets to enable production, cement manufacturing requires highly efficient machine upkeep processes.
Preventive maintenance strategies can lead to over-maintenance, and reactive maintenance causes unexpected shutdowns. Predictive maintenance is therefore the most optimal maintenance strategy in cement factories. However, implementing it can prove challenging when the assets employed in the cement plants are decades old. Such machines seldom have the interfaces to connect with modern systems.
Our client was facing unexpected plant shutdowns resulting from failures and breakdowns of critical assets. To mitigate this issue, they wanted to implement a predictive maintenance solution and achieve better plant availability.
4. Lack of visibility into key supplier and customer processes causing suboptimal supply chain operations
Finally, our client was facing SC issues due to a lack of visibility into key supplier processes, which were impacting downstream operations. Lack of visibility into supplier processes can result in supply interruptions, variations in raw material quality, and cost fluctuations – all of which impact production quality and efficiency. Moreover, this lack of visibility results in suboptimal inventory management, and inefficient production schedules. The latter may result in more frequent machine breakdown, which in turn, results in higher MRO costs.
At the heart of these issues are data silos in which key information regarding customer and supplier processes resides. Moreover, manual data handling leads to low efficiency, and teams end up playing catch-up to gain insights into the flow of materials across the SC. Our client wanted to gain visibility into SC processes to improve operations, achieve better quality control, and achieve better customer outcomes.