Our client's company operates in more than 100 countries and provides IT services and other things. The company offers a wide variety of products for a variety of industries, including those in the automotive, manufacturing, retail, financial services, transportation, public sector, and energy and utilities.
Our strategy included providing all the necessary market insight to our client before taking investing decisions.
Research Nester made sure that risks are kept to a minimum and the need of the client is met.
Predictive maintenance boosts manufacturing and industrial plants' output, product quality, and overall efficiency. It also offers a reduction in downtime and the number of pointless stops, as well as a reduction in repair expenses. Work done as part of predictive maintenance, such as installing sensors, retrieving information, maintaining models, and doing maintenance, assists in lowering maintenance costs. However, in the case of our client’s business, Some of the main causes of declining business profits were excessive maintenance costs, downtime, poor performance, and effects on product quality. Perhaps more importantly, poor maintenance management seriously impaired the capacity to produce high-quality goods that can compete on the global market.
Our Client employs AI-based IoT solutions to improve customer service and do preventative maintenance. The price of maintenance increases as the number of systems increases. As a result, the company found it difficult to upgrade and manage AI-based IoT systems while continuing to supply solutions. Furthermore, there was a need to keep an eye on fleet performance to improve utilization analysis
Our four steps ANDECON Model for taking sound investment decisions:
1. Analyze the current state of the market.
2. Determine the risk profile and set an investing goal.
3. Decide on the best investment niche.
4. Conduct risk assessments and keep an eye on investments
With the use of data from Research Nester, our client turned this knowledge into a transformation road map. To lower the maintenance costs, the use of sensors, information retrieval, model creation and upkeep, and maintenance procedures were suggested.
To assist our client, the team of experts provided cutting-edge data, research, and analytics, looked for holes in the marketplace, and matched clients' market expectations. To foresee equipment failures and malfunctions IoT-enabled service employs asset sensors were used asset sensors for continuous condition monitoring and predictive analytics.
Our investment professionals routinely monitored market growth and decline to aid our customers in making wise, informed, yet adaptable investment decisions. Additionally, the option to spend more on equipment repairs to lengthen their life was advised to function more efficiently. This will also have the advantages of lowering repair costs and lowering the amount of power used.
IoT-based predictive maintenance was employed to process massive quantities of data and run complex algorithms, save maintenance costs, enhance asset utilization, lengthen asset life, increase field staff productivity, and improve safety and compliance. It decreases expenses by 12%, improves uptime by 9%, lowers risks related to safety, health, the environment, and quality by 14%, and increases the useful life of an aging asset by 20%.
Based on the company profiles, a thorough analysis of the product portfolio and company profile was conducted. It was advised to the business to conduct a retrospective analysis of previously discovered production failures to identify the proper failure modes.
Sound investments were crucial for meeting the demand in the worldwide market for predictive maintenance, which also made the right price model selection and market analysis necessary. The company wanted to enhance profits while lowering total financial risk. Going further, our client was able to find possibilities to increase the investment by 30% over three years as a consequence.
By increasing the investment in predictive maintenance solutions, demand was met up to 15%.
The uptime was increased by a factor of 8 %, and expenditures were lowered by 10% by applying predictive manufacturing in maintenance. Additionally, by extending the lifespan of aging assets by 18%, the safety hazards, the environment, quality, and human health are reduced by about 12%.
By taking into account market insights, our client improved the quality of services it offers, which further resulted in a 5-7% rise in profitability.
Furthermore, 5,000 pieces of equipment were monitored using a large-scale application of AI for predictive maintenance. Additionally, from more than 2 million sensors, around 10,000 algorithms capture 10 billion rows of data each week. Every day, 12 million forecasts are produced using this data and algorithmic capability.