The discussion of construction ERP systems cannot avoid the use of ERP in financial manipulation. The lack of transparency of ERP data and the lack of accurate data on the amount of work from clients and customers opens the door to speculation and information hiding.
Despite the apparent simplicity of processes within ERP-systems, unlike other industries, such systems in the construction industry are often closed, and together with the linking of data from CAD (BIM) systems, such a product looks extremely un-turnable and not flexible, due to the closed nature of databases and the functionality of the system itself, which is pre-configured by developers. Since the 2020s, the initiative in the development of such solutions has been taken by the manufacturers of CAD (BIM) systems, whose databases store the most important information used by the construction ERP system - automatic calculations of quantities and attributes of volumes of project entities. And by analogy with CAD (BIM) system, instead of access to the system's databases, external developers get limited functionality of the user interface or limited functionality via API requests.
The restricted access to automation features and the challenge of fully automating the creation of estimates and schedules, along with pervasive bureaucracy within ERP systems, compel construction companies to seek efficiency and profit gains through manipulation of cost-determining coefficients and factors.
In construction, profit is the difference between the total cost of a completed project (revenue) and variable costs, which include design, materials, labor and other direct costs associated with construction.
This situation is worsened by the cost calculation process being opaque, not just to clients, but also to construction company employees outside of the costing departments. This opacity fosters a privileged group within the company, who have exclusive rights o manipulate the attributes of coefficients in the ERP system.
Estimators often act as "financial jugglers", trying to increase profits by means of various coefficients at the stage of calculations, at the same time trying to keep the cost of work or project attractive for clients and not to undermine the reputation of the company.
As a result, the main scheme to increase the efficiency and profitability of companies operating in the construction industry is not automation and acceleration of decision-making processes, but speculation on the prices of materials and works. Overestimation of the cost of works and materials is carried out with the help of "gray" accounting in closed ERP systems (5D, Excel, ERP, EPM) by overestimating the percentages over the average market prices for materials or volumes of works.
In this case, the customer often receives an inflated cost of work, which is usually hidden in the game with volume figures. And subcontractors, those who are engaged in real construction, often receive an underestimated "cost of work" from the general contractor, are forced to purchase low-quality construction materials just to save their money to the detriment of the customer.
Different percentages of speculation coefficients at the calculation level are the guessing on the coffee grounds and the art of juggling between the quality of work and the reputation of the company
Bottom line, estimating, calculations and budgeting departments, which are key to creating profits in a construction company, are now strictly protected from access by unauthorised professionals, even within the organisation itself. And due to the closed nature of data, databases and systems, without the ability to clearly express the efficiency of processes without speculation and concealment of internal data, it becomes almost impossible for companies to maintain competitiveness.
Using the Pandas library, we convert the extracted text we convert an image or photo table into a structured table view
The code written by ChatGPT uses the pytesseract library (Tesseract for Python) to convert an image into text using OCR (optical character recognition) and the pandas library to convert that text into a structured form, i.e. a DataFrame.
Tesseract is one of the leading open source OCR engines, originally developed by HP and now supported by Google. Known for its accuracy and ability to recognise over 100 languages, Tesseract is widely used for OCR tasks.
The conversion process usually involves pre-processing to improve image quality, after which various algorithms are applied for pattern detection, feature extraction or object recognition. As a result, unstructured visual information is converted into structured data.
