Real-time unit cost visualization, improvement & optimization

Introduction

It is difficult to argue with the notion that knowing your unit cost is essential for running a successful and profitable business. This is particularly critical for the businesses that operate large scale industrial processes where volumes are high and even a slight change in the unit cost might have a significant impact on their profitability. Of course, all these businesses would have their unit cost calculated and closely monitored. However, in most cases, it is done retrospectively by dividing the total spend by the number of units produced over a certain period e.g. a month or a quarter.

The problem with this approach for process-based industries is that it effectively presents only an average unit cost over this period and hides how it is influenced by important process-related variable factors i.e. how efficiently the process was operated. We argue that this traditional approach is no longer sufficient and in the modern digital age companies that operate complex industrial processes, which are equipped with multiple sensors and data gathering software, must have the real-time (or at least an hourly) unit cost available to them. This will enable them to analyze how the process changes during the day, week, month, etc, identify periods of good and bad performance, investigate what influenced it and this will unlock numerous opportunities for the process improvement and optimization.

This article highlights the importance of having a model that calculates the real-time unit cost for complex industrial processes, outlines the main steps in developing such a model, and then explains how the real-time unit cost can be used to improve and optimize the process and achieve the best possible unit cost. Overall, this might be quite a challenging and difficult journey, and this is why Hint Global and CogInTech recently developed a solution that can help operators to achieve tangible results quickly and without significant investments in software or hardware. This solution is briefly presented in this article as well.

Unit cost for process-based industries

 A unit cost is traditionally defined as a total expenditure incurred by a company to produce, store, and sell one unit of a particular product or service. This is normally done once a quarter, by the company accountants who calculate all the fixed and variable expenses and then divide it by the number of units produced. The company’s financial statement often reports the unit cost and it is closely analyzed by the company management and external stakeholders as it is an integral indicator that combines most aspects of the company operations and indicates how well the company is run.

However, for the processed based industries, the efficiency of the industrial process is one of the main factors that have a big impact on the variable costs and a traditional approach completely hides this influence by reporting only the average value. To illustrate this point let’s have a look at the plot below that shows the contribution of the process efficiency to the unit cost during a particular day along with the mean unit cost value that would be presented on the balance sheet. The graph clearly demonstrates that on the 1st of April 2020 between 15-00 and 19-00 hours something happened with the process and this resulted in the unit cost spike well above the average value that would be in the financial statement i.e. it reached $900 instead of the average value of $570.

Imagine you are the operations manager running that plant, would you like this graph reported to you every day? The answer is, of course, a ‘yes’ as this graph not only gives valuable insight into how well the process is run but also allows you to identify and investigate such occurrences to eliminate their root cause. Moreover, as we will show below, this approach also opens up opportunities for early problem detection, better shutdown and maintenance planning, process optimization and also reducing the unit cost to the minimal possible values. 

However, it is currently extremely uncommon to have such reports available for managers and engineers. There are complex reasons why it is the case, but the main ones are that financial, process, and operation departments typically work in silos, and currently there is also no simple tool that would bring the process and financial data together. Hint and CogInTech recently developed a solution for the latter issue and our experience shows that the implementation of this tool brings different departments together by formulating the common goal that enables breaking those silos.

Unit cost model - prerequisites for success

OK, we now seem to agree that it is a good idea to have the real-time unit cost calculated and available for analysis. However, developing a model that will deliver this is a relatively complex project. Therefore, before starting this project it is important to highlight a few main perquisites for its success.

First of all, since building this model will require cross-departmental cooperation it is necessary to ensure the buy-in and support from the management along with employee involvement and engagement. This is absolutely essential for the success of this project since to build such a model one will need to talk to process engineers, operators, financial and commercial personnel as they hold critical pieces of information required for the model to be reliable and trustworthy. It is normally quite feasible to get this buy-in as the benefits of having the real-time cost calculated is easy to communicate to the management and they are typically quite supportive of this idea.

Secondly, it is necessary to ensure that the process data that is needed for building the unit cost model is good quality data that is ideally managed and verified by specialized tools. Hint AML solution is one such tool and it offers a comprehensive solution for Information Management, Maintenance and Data Acquisition System. It connects the dots from the sensor to the boardroom, changing real-time data into quality data, information, and finally into knowledge. If the quality of the process data is questionable then all the unit cost calculations are likely to not be reliable, so it is probably not worth progressing this project until the data quality issues are resolved.

Finally, although not essential it is preferred that the process that will be used in the unit cost model is relatively problem-free and running relatively smoothly. Otherwise, the model might be quite “noisy” and difficult to utilize. If the process has got obvious issues it is worthwhile focusing on eliminating them through traditional process improvement techniques, maintenance, etc.

Unit cost model – where to start and how to build it

Once it is ensured that the listed above prerequisites of success are in place it is possible to start building the unit cost model for the process. Although, it is in theory feasible to model the entire plant it might be more efficient to start with modeling a particular process unit within the plan, focusing on the biggest contributors to the variable cost. For example, it might be the crude oil quality and volumes as a function of chemical consumption for an upstream separation train or high energy-consuming processes or units for downstream or petrochemical plants e.g. crude distillation or hydrocracking units. This will enable gaining experience in building simple models that can then be used to build a complex and comprehensive model of the entire plant or even company.

Once the process or unit is selected, the next step is to build its model that takes into account all the main process parameters (e.g. flow rates, energy consumption, liquid levels, valve positions, etc), all the interactions between them and combines them with all the relevant financial data (e.g. energy cost, raw material cost, etc). The goal is to calculate the unit cost as a function of the main process parameters and financial figures. This might be a bit tricky, so it is better to use some tools to facilitate this process.

It is possible to use whatever tool you prefer, but one of the options is the CogInTech Digital Twin Builder and is free in its basic version that you can find at www.cogintech.com. This tool is designed specifically for building unit cost models, it is easy to use and supports using formulas. This tool also allows building the model of your process in a way that makes the further analysis easier as it defines the inputs/features and goals/labels very explicitly and also identifies that variables can be controlled, and which ones are just measurable.

The tool allows you to move from having P&IDs or PFDs as your process representation to a have a simple model of the process where only the main elements of the process are shown. The tool will help you to visualize the interactions between the elements and supplement it with formulas that will calculate the required parameters if needed. An example of such a transition from P&IDs to a simple model is shown below.

It is recommended to have a half-day workshop to develop a model of the process with all the relevant specialists and stakeholders in the room. It is essential to ensure that all the knowledge about the process will be represented, as it will be required to combine the financial and process details in the model. Thus, the presence of the process engineers, operators, and finance specialists is absolutely essential for the success of this workshop and building a reliable and accurate model of the process. The goal of this workshop is to connect financial numbers with the specific features of the technological process and to be able to calculate the unit cost as a result.

What is next? AI-powered Digital Twin and process optimization

Once the model that connects financial and process data is ready, the unit cost can be calculated by plugging the historical process and financial data into the model. Depending on the particular process and monitoring equipment installed, the frequency for the unit cost calculations might vary from every few minutes (or even seconds) to only every 12 or 24 hours. It is typically possible to calculate the hourly unit cost and it is sufficient for most practical applications. So, what are the main options for using this hourly unit cost data?

First of all, the analysis of historical data is likely to reveal numerous insights into how the plant and process have been operated in the past. The variability of the unit cost tells the story of how well the process was run and whether there are periods when the efficiency of the process was suboptimal, which led to higher unit costs. Investigating such periods might lead to detecting problems with equipment or how deviation from the maintenance regime influenced the unit cost. Additionally, if there are periods when the unit cost was lower than usual it is worth analyzing them as well and extracting some practices and reasons that led to this low unit cost. After the historical data is analyzed it is worthwhile to run weekly or daily unit cost reports that are likely to be very useful for the management, as they are an integral performance indicator of how well the process was run and if there were any obvious problems.

Secondly, connecting the model to real-time data can provide an operator with valuable insight into the process of health status. If the unit cost is close to the average, then it is reasonable to assume that things are normal. However, if the unit cost starts creeping up then it is worth investigating the reason for that increase and addresses it as soon as practically possible. An extension to Hint Global AML Solution can help to deliver this option.

Finally, perhaps the most powerful application for such a model is process optimization. Once the model of the process is built and there is effectively a function that connects process and financial data to calculate the unit cost, it is possible to apply modern machine learning tools and develop a digital twin of the process using historical data. This digital twin can then be interrogated to extract the best practices and best process operating conditions that allow minimizing the unit cost. This can be used to develop a set of operational instructions that will help to run the process at the maximum efficiency level. Moreover, if this digital twin is connected to the live process data then it can provide real-time advice to operators on how to adjust the process settings to achieve the best performance. Hint in partnership with CogInTech can help operators to build such digital twins.

Conclusion

Real-time unit cost being an integral characteristic of the process health status is a valuable tool for modern process-based industries, as it opens numerous opportunities for increasing efficiency through process optimization and improving reliability through detecting the problems early. The projects of building the unit cost models typically have very high ROI (Return of Investment) and payback within months.

Hint in partnership with CogInTech is happy to assist companies with building the unit cost models and digital twins for optimization. We offer the following tiers of services:

1. Development of a simplified digital twin for unit cost calculations
2. Analysis of historical data using the simplified digital twin and providing recommendations for the process improvement
3. Connecting the simplified digital twin with the live data to provide real-time unit cost and regular reporting
4. An analysis if the process can be accurately modeled by neural networks
5. Development of a comprehensive AI-powered digital twin for a process, with all the interactions within the process accurately modeled by neural networks
6. Detailed process optimization recommendations based on the AI-powered digital twin model of the process
7. Connecting the AI-powered digital twin to real-time data to provide recommendations to operators in real-time and detect problems early

 If you are interested, please get in touch and we will be happy to help. To learn more about Hint please visit our website www.hint-global.com or call/email Wolter Last, Cell. +1 832 762 1908 or mobile +31 653314828, email: [email protected]