Businesses that have incorporated digitalization strategy using digital tools and technologies across their operation make their decisions based solely on data. As the operation becomes more complex, the role of data analyses gets more fundamental in delivering enterprise value. Data volumes increase, and there are more process parameters that have to be considered. Digital companies use trained mathematical models to analyze data.

However, this is not achieved by simply choosing the appropriate analytics technology to identify the next strategic opportunity. Softline offers complex support and consultancy for companies looking to exploit the opportunities offered by data analytics. Through numerous case studies and successful projects, we supported our customers in reducing costs, performing quality assurance and eliminating unscheduled downtime. Learn more about our projects involving several industries to understand the potential in data.

1.     Reliability assessment for pumping equipment in oil wells

An oil company needed to predict failures of electric drive centrifugal pumps and other equipment for oil extraction from wells. The solution had to take into account different operation modes and geological conditions and help to plan preventive maintenance.

The customer provided extensive historical data concerning the failures of various units, which made it possible to build an operational model of pumping equipment in oil wells. The model can determine the optimal equipment configuration (combination of specific unit and device models) for any specific oil well and thus maximize the mean time between failures.

The solution reduces operating and capital costs by minimizing the number of incidents that require on-site visits by repair teams, which are very costly due to the fact that oil wells are often remote and difficult to reach.

2.     Forecasting electricity consumption volumes and tariffs

Large enterprises utilize a lot of electricity. They must forecast consumption and remain within the forecast. Compliance with the plan allows them to select optimal electricity tariffs, while non-compliance causes excessive costs.

Our task was to optimize electricity costs through accurate hourly consumption planning with an error margin of 1.5% or less. To solve the problem, we preprocessed the data, seeking, excluding, and smoothing anomalies and gaps. Then, we built predictive models for time series and dashboard data, taking into account various factors, such as weather, calendars (production, workloads, maintenance works), and macroeconomic indicators.

3.     Technical diagnostics of equipment at nuclear power plants

Although the nuclear power industry has effective means of preventing emergencies and monitoring the equipment's health, minor equipment failures are not impossible. Our customer, a nuclear power plant, accumulated an exhaustive database of such failures and the results of their analysis. The customer representatives asked our colleagues to create an expert system that would enable quick monitoring to identify and classify failures based on their characteristics.

Having created the model, we trained it on the customer's historical failure data. The model became the core of a monitoring and alert system that tracks abnormal situations and helps personnel classify them based on a statistical analysis of the failure archive database.

4.     Monitoring the purchases of stationery

Our team has created a deep analytics tool, called Digital Auditor, allowing institution to monitor purchases, compare prices, and perform visual analysis via dashboards.

Before the project has been implemented, there were no price thresholds for many goods, works, and services purchased by state organizations, as well as guidelines for calculating the allowable prices and detecting the excesses. As a result, there were many cases when, at the stage of budget review, the planned budgets were found to be overvalued.

In our Digital Auditor system, each product is described by a probability distribution of price. It assesses the distribution and derives its parameters (type, expected value, variance) to determine the recommended price and the deviation between the declared and the recommended one. After calculating the fair price, the model shows the difference between the recommended price and the actual price and assesses the difference. Softline also developed an index to rank organizations on the level of deviation from the fair price, taking into account the purchase volume.

Data analytics from Softline excel at creating processes involving predictive analytics, machine learning, and big data. The solutions that we've highlighted here are just a few applications of data analysis. Book a consultation with our experts to discuss how analytics can solve your business challenges on a brand-new level.