Our team developed a new part for an existing ERP platform from scratch, which covers the production structure, interrelationships between its components, cost calculation, and simulation.
The first part of the solution represents a scheme with a plant structure. Users can build a custom model of their factory, adding level by level – workshops, equipment, and modules. The scheme helps analysts understand the model of a factory better and allows references to be made at a glance.
Clicking the items at the lowest level of the scheme, users open a section with accumulated information about each manufacturing unit: pages with a general overview of work and output, manufactured and defective products, materials used, losses, and labor. It is possible to set KPIs with different parameters (output, scrap, etc.) for each machine and use charts to track changes in absolute and relative terms. Analysts can check the efficiency of each machine: how many products were manufactured, at what speed, and with how many resources and losses.
Products, materials, and labor pages contain tables with more detailed information about each item. For instance, a labor page shows the actual number of workers vs standard brigade size, actual working hours and overtime vs standard shift time, and so on. To examine expenditures, users can look into tables with total salary and its breakdown by employees, working hours, and overtimes. As a result, such a structure enables analysts to identify bottlenecks and new growth points from the outset, and dig deeper into each parameter to find out the ways for improvement of the production process and financial performance.
Another part of our solution was the cost module. Its main page looks like a dashboard with a summary of the production costs of a chosen time period. It provides accumulated data about output, total costs, costs per unit produced, etc. Users can also compare data with a reference period to track changes over time. A small P&L table gives more information on spent cost per element (labor, raw materials, energy, depreciation, etc.) in comparison to the allocated budget and percentage of losses. For clarity, all cost categories are also presented in a graph. This overview allows users to understand at a glance the weight of each category in total production costs and identify problem areas for their optimization.
To get more details about cost structure, users can plunge into the information presented in large P&L spreadsheets, which are another part of the cost module. They include more parameters of each P&L component and allow users to calculate how production costs depend on output volume, price changes over years, and losses.
The spreadsheet configuration already provides a list of general elements which are common for different types of plants – direct and indirect labour, raw materials, packaging, energy, maintenance, quality control, operating expenses, etc. Therefore, users only need to choose required elements in settings and adapt them to their own needs: they can change names if required, insert additional lines with components, and add actual costs and prices.
The spreadsheet interface is especially easy to use: each row has a few buttons for editing data, adding a new nested row, and repeating and deleting the same row. All formulas and complicated calculations with many interrelated parameters are “hidden” in the backend, so ordinary users without admin rights won’t accidentally change or spoil them. The data can be imported from other tables (e.g. CSV, XML Spreadsheets), saving users a lot of time and minimizing manual work.
The next part of the cost module makes it possible to simulate top-down and bottom-up scenarios. Analysts can set reduction targets (e.g. by costs, losses) and get estimations of cost optimization by year. This helps to find the perfect combination of all parameters, allowing production companies to minimize costs at the same quality and increase their EBITDA.
When a scenario is approved, it is used as a benchmark to track the current efficiency of production. Thus, analysts can detect serious errors in the efficacy and timing and recommend on taking corrective actions.
In order to make the analysis of data faster and more efficient, our development team has implemented a Machine Learning algorithms module. Its aim is to define the patterns of changes in plants’ work parameters and analyze their effectiveness. These algorithms automatically receive the information from the ERP system and learn which ways were the most efficient in achieving the goals set for each plant, workshop or even machine. This allows the module to find trends and patterns that cannot be seen so easily but can be efficiently used in manufacturing cost optimization.
This module works within the manufacturer’s perimeter and doesn’t label or store data about any branch, plant, workshop, or machine. Only statistical data is analyzed by the ML algorithms. This means that this module is secure and can’t provide the potential intruder or insider with any vital information on the manufacturing powers and items of the client.
As the system was designed from the outset to be as user-friendly as possible it took just an hour to train employees working within a new module. Results became visible within a week of implementation: the preparation of reports became 3 times faster than before, and users got more time for closer examination of data and scenario simulation. This enabled analysts to come up with a more calibrated set of findings and recommendations for each factory within a shorter period of time, thus enhancing business value and increasing customer loyalty.
Moreover, our team has managed to keep the whole ERP system as secure as it used to be before modifications were made thanks to the machine learning algorithms not interacting with any vital data while analyzing only patterns and trends that come out of certain decisions and actions.