All case studies

Banking on Enterprise AI

Reconfigure the way you think about BFSI

Unlock new experiences with internal and external stakeholders

Customer Churn Management

Industry Problem:
Early signs of churn like balance reduction, missed transactions,​ and late payments are often very hard to foresee and detect. Relationship Managers (RMs) find themselves in a tough spot as they are unable to identify churn and decreased wallet share risks, often months after a client has already shifted loyalties to a competitor​. At MAG, we work closely with clients to understand all potential pain points and the best way to alleviate them.

Solving For Customer Churn Management, The MAG Way:

  • Our AI algorithms are fine tuned to scan each client’s engagement, business performance, transactions, and economic data to identify key risk drivers​ of churn

  • We suggest optimized retention strategies​ by identifying the most critical customers to focus on and why

  • Clients we have catered to have witnessed large reductions in AUM and customer attrition​, increased customer lifetime value and improved levels of customer service

Rate, Fee, and Price Optimization

Industry Problem:
Prices are applied bluntly across broad segments and credit classes for each product by most financial services companies. This results in high prices and dissatisfaction for some clients and less-profitable pricing for others​. To find the optimum balance, MAG works with clients to understand and optimize pricing strategies based on a host of different internal and external parameters.

Solving For Rate, Fee, and Price Optimization, The MAG Way:

  • Our data scientists build tailored algorithms that have the capability to analyse client behaviour and needs

  • It is also equipped to evaluate competitor and industry benchmarks to predict rate sensitivity for each product​

  • Our solution recommends optimal pricing and provides supporting evidence to enables RMs to optimize revenue with each client through both human and automated pricing decisions​

  • Our AI augments existing client processes allowing them to increase win rates and optimize long-term revenue for each client while ensuring continued customer satisfaction​

Client Profitability Management

Industry Problem:
Many clients at financial services companies are unprofitable​ on account of substantial capital, regulatory, and servicing costs. In such cases, there is significant ebb and flow in profitability for clients over time​. Rapidly identifying and remediating risks for unprofitable clients is extremely challenging for finance professionals and can result in detrimental effects to the bottom line​. At MAG, we work with concerned teams to classify and mitigate potential risks by understanding key threats to the business.

Solving For Client Profitability Management, The MAG Way:

  • We apply machine learning to identify at-risk clients before they become unprofitable​ using a host of different data sources that are at our disposal

  • Effective strategies for each at-risk client to attain and increase profitability or to offboard​ if necessary are then recommended by our solution

  • Banking institutions can proactively optimize client portfolios and personalize client offerings to protect and grow profits​ armed with a unified view of client profitability

Next Best Offer

Industry Problem:
The processes employed by companies today to manage today’s highly divergent and fast-evolving needs are highly reactive, typically requiring clients to self-report emerging needs​. This results in RM’s frequently missing opportunities to out-manoeuvre competitors and provide services​ that are valuable and highly relevant. Our aim at MAG is to work with companies to transform their processes and make them proactive instead of reactive.

Solving For Next Best Offer, The MAG Way:

  • Our ML models identify emerging needs as well as predict the likelihood that a client will qualify for a particular offering like a new loan, for example

  • To maximize client value while mitigating credit and other risks​, our solution then guides users to structure the offering accordingly 

  • MAG customers see increased wallet share, revenue growth, and a deepening of client relationships with a proactive approach to prospecting

Revenue Forecasting

Industry Problem:
Companies must deal with a host of variables that are not always in their control but can significantly impact the top and bottom lines of their business. The last two years have seen the pandemic ravage a wide range of businesses across sectors, with many having to shut down permanently. To build a business that is resilient, a company needs to be on top of any possible situation that may arise and move swiftly. At MAG, we provide companies with the tools to build and implement the right revenue strategies by leveraging as many datapoints as we can.

Solving For Revenue Forecasting, The MAG Way:

  • We apply sophisticated AI algorithms to both structured as well as unstructured datasets, to accurately predict revenue for each client

  • We analyse pipeline data to track and measure future sales and make reasonable estimates using historical win rates to add confidence to the model

  • Bottom-up forecasting allows us to provide accurate and detailed forecasts that can highlight capacity surpluses/shortfalls in addition to projects ahead of or behind schedule  

Bridging New Frontiers

Realign with the latest in retail technology

Implementing New Retail, the right way

Customer Churn Management

Industry Problem:
In retail, unless customers have had a particularly bad experience with your product or customer service, they don’t tend to tell you they’re leaving. Many retail customers just drift away gradually, rather than making a conscious decision to leave.

Instead of a swift chop, retail churn therefore presents as a slow slide out. Typically, our clients will see a gradual decline in the level or frequency of sales, showing that customers are either spending less on their product or moving to competitors.

Solving For Customer Churn Management, The MAG Way:

  • We use Slider Modelling to help retailers dig into the data and interpret the complex signals customers are sending through their behaviour

  • Our AI algorithms are finetuned to scan each customer’s engagement, business performance, transactions, and economic data to identify key risk drivers​ of churn

  • We suggest optimized retention strategies​ by identifying the most critical group of customers to focus on and why

  • Clients we have catered to have witnessed large reductions in customer attrition​, increased customer lifetime value and improved levels of customer service

Demand Forecasting

Industry Problem:
Heraclitus couldn't have been more right when he said change is the only constant, especially when it comes to demand forecasting in retail. Today’s customer expectation and preferences are more complex than ever. Fulfilment preferences, purchasing channels and methods, and customer segments are multidimensional and very different from classical models.

That makes it very hard for retailers to make accurate and more reliable forecasts to get the right inventory to the right location.

Solving For Demand Forecasting, The MAG Way:

  • Our data scientists build tailored algorithms that create more accurate short-term baseline forecasts at the zip code/day/SKU/fulfilment preference level for long-life products as well as for short-life or highly seasonal products

  • By using an AI-powered probabilistic demand forecasting system, we can make more accurate promotional and long-term forecasts

  • We Leverage fast learning algorithms and explainable AI to predict when, where and how omni-channel customers want their orders to be fulfilled

  • MAG offers tailored products that determine the right amount of inventory required at stores and distribution centres to achieve greater visibility into the future for better planning

Predicting Customer Lifetime Value (CLTV)

Industry Problem:
CLTV represents the total amount of money a customer is expected to spend in a business during their lifetime and is an important metric to monitor because it helps to make decisions about how much money to invest in retaining existing customers and acquiring new ones.

Businesses are often in the dark when it comes to analysing customer behaviour and their cost of acquisition vis-à-vis their total spends over a given period. Knowing CLTV helps them build effective marketing strategies with a positive Return on Investment (ROI).

Solving For Predicting Customer Lifetime Value (CLTV), The MAG Way:

  • We use exploratory data analysis to get a more granular look into a client’s sales data and know more about the spending habits of customers 

  • Our solutions are built to estimate the recency, frequency, and total amount of purchases by each customer

  • We use several regression techniques and probabilistic models to predict CLTV by fitting on past data to make estimations about transaction counts and associated sales

Next Best Offer (NBO)

Industry Problem:
Historically, retail experiences have been built around personal bonds with the consumers. Salespeople knew each other as well as the customers they were catering to. However, today’s retail stores lack this personal touch and the only way to build a loyal customer base is through data analytics and technology.

The NBO is a way of providing better and more precise marketing to inspire a purchase, drive loyalty or both. They are usually derived from data mining, statistical analysis, segmentation, and predictive modelling, and then executed based on business rules.

Solving For Next Best Offer (NBO), The MAG Way:

  • Our ML models identify customers’ emerging needs as well as predict the likelihood of them buying a particular product next

  • Effective personalized marketing strategies can be built around sophisticated customer analytics brought to you by our solutions 

  • MAG customers see increased revenue growth, and a deepening of customer relationships with a proactive approach to prospecting

Employee Churn Prediction and Anticipated Turnover

Industry Problem:
Companies must often deal with high attrition rates in retail. A reliable workforce is the backbone of any business so naturally, businesses struggle when employees leave. To ensure smooth day-to-day functioning, companies must be aware of overall employee morale and seek to address any issues they may be facing.

At MAG, we build solutions that help companies stay ahead of any potential situations that may lead to employee attrition and investigate numerical and categorical data to keep attrition rates in check.

Solving For Employee Churn Prediction and Anticipated Turnover, The MAG Way:

  • We conduct extensive exploratory data analysis by using distribution plots and heatmaps to understand employee data at a more granular level

  • Our solutions use feature engineering and several classifiers to understand the extent of impact of different factors on employee attrition

  • K-folds cross validation is employed for multiple-channel ML models to test its efficacy on unseen data and finally provide customers with a holistic view of key drivers of attrition

Oil and Gas in the Age of AI

Reshaping the oil and gas sector with intelligent analytics

Implementing intelligent algorithms for oil and gas companies that drive customer success


Industry Problem:
Companies involved in the oil and gas industry leverage AI to drill and mine raw hydrocarbons and other products required to produce fuel. AI helps these companies by developing algorithms that provide accurate and precise intelligence to guide drills on water and land. Precision drilling helps in reducing the risk of accidents, oil spills, fires, and enhances the rate of penetration.

At MAG, we build solutions that can make accurate predictions by aggregating real-time data, providing an administrative dashboard to the organization in charge and enabling them to be more efficient in their drilling process.

Solving For Drilling, The MAG Way:

  • Our AI models provide the right intelligence at the right time to companies that are involved in drilling activities on land and water

  • This information can be used to substantially mitigate any threats and massively reduce the risk of occupational hazards thereby securing the health and safety of workers


Industry Problem:
AI helps the oil and gas companies in optimizing their production by identifying the areas of inefficiency. AI systems can be trained on large volumes of raw production data that enable the automatic recognition of patterns and refining the data to generate analytics.  

At MAG, we leverage big data to help companies become more efficient with their production schedule by providing a holistic view of all the areas that can see increased efficiency basis the capability available on site.

Solving For Production, The MAG Way:

  • We build sophisticated solutions to improve the production performance of oil and gas companies by identifying areas of inefficiency using past and real time data 

  • Our pattern recognition systems are tailored to work with data generated by oil and gas sites and has shown remarkable improvements in the past across several large implementations

Reservoir Management

Industry Problem:
Oil reservoir management involves several technicalities like seismic interpretations, geology, management of reservoir, and production, for which the degree of optimization and maintenance is very high. AI systems can be trained on the data of these technicalities and help in field surveillance, reducing the reservoir maintenance costs, and reservoir engineering, among other things.

Solving For Reservoir Management, The MAG Way:

  • We build and implement AI-based systems that can significantly aid companies in improving reservoir engineering and lowering the cost of maintain the site

  • We develop sophisticated systems that can aid and guide companies to surveil a site by utilizing our knowledge in the fields of geology and seismology among other cross-functional domains


Industry Problem:
Monitoring of oil fields, gas stations, plants, mines, and equipment will ensure the safety of employees and the environment. Accidents can be monitored from AI-powered cameras, robots, drones, among others to reduce the extent of potential damage.

Frequent inspection of equipment and risk assessment will help companies in taking predictive measures to avoid unforeseen circumstances. The implementation of AI-based solutions can lead to higher predictability and major cost savings.

Solving For Monitoring, The MAG Way:

  • Our AI-based systems can supervise a host of different types of energy sites for any anomalies that may prove to be detrimental to the health and safety of workers

  • Our solutions can help you flag irregular behaviour using anomaly detection through a time-series analysis thereby significantly precluding the risk of a catastrophic event


Industry Problem:
The oil and gas industry are a highly labour-intensive industry, and the employees work in highly uncertain conditions. Automation of tasks can help in saving time, labour, and costs. AI and ML help in automating almost all the routine tasks while identifying the best practices, thereby safeguarding the lives of employees, apart from reducing human errors and increasing efficiency.

Solving For Automation, The MAG Way:

  • Intelligent AI systems are capable of automating routine tasks for the oil and gas industry such as pipe handling and data logging 

  • Upstream producers working in remote areas can benefit from automation by using drones and submersibles to help monitor inspection processes, data from which can then be read by our solutions for further analysis 

Transportation in the Age of AI

Reshaping the transportation sector with intelligent analytics

Implementing intelligent algorithms for transportation companies that drive customer success

Real-time Parking Occupancy and Availability

Industry Problem:
Real-time information about the number of parked vehicles and empty parking spaces in a parking lot can prove to be extremely beneficial for a variety of organizations that provide parking to their customers. 

At MAG, we build solutions that can make accurate predictions by aggregating real-time IoT data, providing an administrative dashboard to the organization in charge and enabling reservation in advance or on the spot among other things.

Solving For Real-time Parking Occupancy and Availability, The MAG Way:

  • Our AI models can predict future parking demand for different locations by using historical time, weather, day of the week, peak hour timings and traffic level data

  • This information can be used for both planning of new spaces and evaluating the utilization of current available spaces​

Adaptive Ramp Metering

Industry Problem:
Freeway congestion can be reduced by controlling the frequency at which vehicles enter the freeway using ramp metering traffic signals on freeway on-ramps, preventing large groups of vehicles from entering together. They can be installed quickly are a lot less expensive than widening a freeway.

Solving For Adaptive Ramp Metering, The MAG Way:

  • At MAG, we provide a corridor management system accounting for real time upstream and/or downstream traffic congestion.

  • Our ramp metering system can adapt to varying traffic demands due to seasonal variation, weekend congestion, traffic growth or travel pattern changes over time.

  • Freeway incidents can be managed a lot more effectively

Proactive Incident Management

Industry Problem:
There has been an exponential rise in traffic and consequently the risk to drivers and passengers due to increasing urbanization over the years. Appropriate action is vital for safe driving under various road conditions. Identification and notification of accident-prone zones while driving has become easier with the rise of intelligent vehicular systems. Data analytics can improve driving safety in such regions and thereby save invaluable human lives.

Solving For Proactive Incident Management, The MAG Way:

  • We build and implement AI-based systems that can read understand visual data by sifting through imagery from thousands of cameras already employed by an institution

  • Heatmaps can be built by analysing data from locations that see higher than average accident rates over the course of a given period 

Predictive Maintenance

Industry Problem:
Many companies are struggling with the realities of AI implementation even as Industry 4.0 continues to generate media attention. The benefits of predictive maintenance such as helping determine the condition of equipment and predicting when maintenance should be performed, are extremely strategic.

The implementation of ML-based solutions can lead to higher predictability, major cost savings, and increased availability of the systems.

Solving For Predictive Maintenance, The MAG Way:

  • Our ML-based RUL prediction can give you insights on predicted machine failure allowing you to schedule maintenance in advance

  • Our solutions can help you flag irregular behaviour using anomaly detection through a time-series analysis

  • Our implementations are capable of failure diagnosis and recommendation of mitigation or maintenance actions post failure

Multi-modal Intelligent Traffic Signal System

Industry Problem:
Traffic signal control has experienced very few fundamental improvements in the past 50 years. While tools and methods have been developed to enable traffic engineers’ better use of traffic signal control, the fundamental logic and operations of the controller have not changed.

Most systems today depend on loop detectors or video-based systems that are located at fixed locations in space to call and extend signal control phases. These detection systems provide basic information such as vehicle count, occupancy, and/or presence/passage information. This limits the use of advanced logic that can potentially be built into modern day traffic signal controllers.

Solving For Multi-modal Intelligent Traffic Signal System, The MAG Way:

  • Intelligent Traffic Signal System using high-fidelity data collected from vehicles through V2V and V2I wireless communications as well as pedestrian and non-motorized travellers, this proposed application seeks to control signals and maximize flows in real time

  • The Transit Signal Priority allows transit agencies to manage bus service by adding the capability to grant buses priority based on several factors

  • The Mobile Accessible Pedestrian Signal System integrates information from roadside or intersection sensors and new forms of data from pedestrian-carried mobile devices

Separating the Signal from the Noise

Reimagining tele
communications and networking

Implementing intelligent strategies for telcos that drive customer success

Network Optimization

Industry Problem:
In the telecom space, always providing impeccable network reception to customers is a big challenge as it involves a lot of variables that are sometimes not withing a telco’s control. It can lead to an experience that is frustrating for customers even prompting them to move to a different provider.

Since the rollout of 5G, customer expectations have risen exponentially with users always expecting fast and reliable connectivity. This means telecom providers need to make significant efforts to ensure that user experience is not impeded in any way.  

Solving For Network Optimization, The MAG Way:

  • We use AI to help CSPs build self-optimizing networks (SONs) to support this growth

  • Our solutions use advanced algorithms to look for patterns within the data, enabling telecoms to both detect and predict network anomalies

  • CSPs can proactively fix problems before customers are negatively impacted

Predictive Maintenance

Industry Problem:
Companies often struggle to provide fast and reliable service in a lot of areas due to several infrastructural and topological issues. Actively monitoring all the infrastructure at their disposal can be a big task for then.

AI-driven predictive analytics can help telecoms provide better services by utilizing data, sophisticated algorithms, and ML techniques to predict future results based on historical data. This means operators can use data-driven insights to monitor the state of equipment and anticipate failure based on patterns.

Solving For Predictive Maintenance, The MAG Way:

  • Implementing AI in telecommunication allows CSPs to proactively fix problems with communications hardware, such as cell towers, power lines, data centre servers, and even set-top boxes in customers’ homes

  • MAG builds products that allow CSPs to achieve network automation and predictive intelligence enabling better root cause analysis and prediction of issues

  • At MAG, our solutions underpin more strategic goals, such as creating new customer experiences and dealing efficiently with emerging business needs

Virtual Assistants for Customer Support

Industry Problem:
Companies often struggle to retain good support staff who can offer customers personalized help for when they need it most. However, this can prove to be cost prohibitive for companies very quickly as building a good support team that is large enough to cater to millions of paying customers is challenging.

Virtual assistants can efficiently automate and scale one-on-one conversations providing customers with the support they need, right when they need it with no waiting time. It can be substantially cost effective from an implementation perspective as well.

Solving For Virtual Assistants for Customer Support, The MAG Way:

  • Our AI solutions help telcos contend with the massive number of support requests for installation, set up, troubleshooting, and maintenance, which often overwhelm customer service centres

  • Operators can implement self-service capabilities that show customers how to install and operate their own devices

Fraud Prevention

Industry Problem:
Targeted instances of scamming people via telecommunication channels have been on the rise and is one of the biggest issues for telcos today. With industry estimates indicating that 90% of operators are targeted by scammers daily – amounting to billions in losses every year – AI applications are especially timely for CSPs.

Solving For Fraud Prevention, The MAG Way:

  • Our AI and machine learning algorithms can detect anomalies in real-time, effectively reducing telecom-related fraudulent activities, such as unauthorized network access and fake profiles

  • Our solution can automatically block access to the fraudster as soon as suspicious activity is detected, minimizing the damage

Robotic Process Automation (RPA) for Telecoms

Industry Problem:
CSPs have vast numbers of customers engaged in millions of daily transactions, each susceptible to human error. Robotic Process Automation (RPA) is a form of business process automation technology based on AI.

RPA can bring greater efficiency to telecom functions by allowing telcos to manage their back-office operations and large volumes of repetitive and rules-based actions more easily.

Solving For Robotic Process Automation (RPA), The MAG Way:

  • RPA frees up CSP staff for higher value-add work by streamlining the execution of complex, labour-intensive, and time-consuming processes 

  • Our solutions enable companies to simplify mundane processes so they can such focus on business-critical ones like billing, data entry, workforce management, and order fulfilment

  • Cognitive computing is expected to substantially transform the telco industry in the coming years, and you can be part of that journey with MAG

Learning 2.0

Restructuring administrative and learning systems

Implementing intelligent algorithms for learning institutions that drive student success

Finding At-risk students

Industry Problem:
Students enrolled in learning institutions do so to understand and learn about various topics of enquiry to eventually strengthen their employment prospects. However, not every student can do so at the same pace. MAG develops solutions to help schools and universities understand which students are unable to keep up so they can be provided with additional assistance.

Solving For Finding At-risk students, The MAG Way:

  • We build predictive models that discover which students are at the highest risk of dropping out or failing the academic year

  • The model works on historical data and takes in parameters like academic history, course/degree difficulty, probationary status, etc.​

Intelligent Course Advisor

Industry Problem:
This is a recommendation engine that finds the best electives for each student, focused on courses that are geared toward the students interests and increased grades.

Solving For Intelligent Course Advisor, The MAG Way:

  • Schools and colleges can expect a 15% increase in median class GPA with a better selection of courses and programs

  • Can be scaled up to guide students for university applications (with emphasised focus on Oxbridge, Ivy League schools etc.) ​

Practice Simulator

Industry Problem:
This is an ML-based exam preparation simulator that provides questions to students based on historical exam patterns​.

Solving For Practice Simulator, The MAG Way:

  • Questions are dynamically generated based on an assessment of where the students stand, and which modules require more practice

Chatbots and Digital Assistants​

Industry Problem:
To facilitate the exchange of information campus-wide, a digital assistant in the form of a chatbot may be embedded on the website​.

Solving For Chatbots and Digital Assistants​, The MAG Way:

  • The chatbot answers are built on top of a big data layer and can answer questions on a range of topics – admissions, degree programs, fees, scholarships, etc.​

Classroom Utilization & Facilities Planning

Industry Problem:
Besides curriculum and teaching methods, AI technologies can optimize and streamline management processes for educational institutions. 

Solving For Classroom Utilization & Facilities Planning, The MAG Way:

  • Our AI-based tools can help in the proper designing and planning of classrooms according to the number of students​

  • With MAG, you can rest assured that resources are timely distributed to areas of higher demand and cut down on unnecessary expenses​

Insurance in the Age of AI

Reshaping the insurance sector with intelligent analytics

Implementing intelligent algorithms for insurance companies that drive customer success

Fraud Detection

Industry Problem:
AI is a key watchdog in the fight against fraudulent claims for insurance companies. We build solutions that are excellent at detecting patterns that might escape human cognition.

Solving For Fraud Detection, The MAG Way:

  • Our ML algorithms provide details on suspicious claims with potential repair cost and liability assessments, suggesting procedures that can resolve and enhance fraud protection

  • Our cognitive ML algorithms have a very high accuracy rate for detecting fraudulent insurance claims and have been employed successfully by many companies

Claims Management and Human Error Reduction

Industry Problem:
With a complex distribution chain that involves a series of middlemen examining information between the insured and the carrier, the insurance industry is winding and complex. This can lead to a lot of human error and manual work that slows the process. However, AI is starting to fix this problem.

Solving For Claims Management and Human Error Reduction, The MAG Way:

  • Our algorithms can save time and reduce the number of errors as data is passed from one source to the next. 

  • At MAG, we conceive and build various products that can predict claims severity (from first notice of loss) as well as claims subrogation

Customer Service Chatbots

Industry Problem:
Even though insurance is a sector resistant to change, good customer service is of utmost importance. People often stop using the services of institutions with bad customer service, which is a big reason why so many insurance company websites now include chatbots.

Solving For Customer Service Chatbots, The MAG Way:

  • We build and implement AI-based tools that can guide customers through numerous insurance-related queries without human intervention 

  • An AI-driven chatbot can not only be faster in resolving routine queries on an average, but it can also often make companies realise substantial savings in cost as well

Sales and Marketing

Industry Problem:
To facilitate the amplification of tailored marketing to boost sales of insurance products, companies need to have a very good understanding of their customers including their purchasing behaviour. MAG is instrumental in building tools and capabilities for companies that allow them to do just that so that they can not only enrich the overall experience of existing customers, but also attract new ones into their fold. 

Solving For Sales and Marketing, The MAG Way:

  • We build solutions that measure various customer parameters to suggest personalized insurance products built specifically for them

  • Companies that enjoy a high retention rate amongst their customers use MAG products that are built to reduce churn for insurance renewals

  • Our solutions are also built to facilitate the prediction of agent profitability for distribution optimization


Industry Problem:
Pricing insurance products right is a highly specialized activity and one that requires a significant understanding of past and future trends. At MAG, we aid companies in insurance product pricing by building AI solutions that take a variety of parameters into account and ensure optimal pricing that is sustainable as well good enough to retain customer to a high degree.

Solving For Pricing, The MAG Way:

  • Our AI-based tools can help insurance companies provide dynamic pricing to their customers 

  • It is also capable of predicting expected loss costs of a company 

Logistics and Supply Chain in the Age of AI

Reshaping the logistics and supply chain sector with intelligent analytics

Implementing intelligent algorithms for logistics and supply chain companies that drive customer success

Linehaul Planning 

Industry Problem:
Rules-based analytics are reactive and too late to impact OTIF performance​. Our aim is to streamline operations within complex logistics networks by using AI models to automate and optimize trip planning. Increasing service level expectations, high volatility in volumes and disconnected data in silos are some of the issues companies regularly face

Solving For Linehaul Planning , The MAG Way:

  • Our AI models to suggest the most optimal network plan to align demand and supply​ 

  • Companies can reduce network and procurement costs while increasing service levels

  • We build an optimized load factor that can reduce the frequency of errors

  • We develop a unified data model that integrates with existing TMS, CRM and ERP systems

​Asset Positioning

Industry Problem:
A powerful asset positioning tool calculates the most optimal storage, repositioning, and maintenance strategy for empty assets​. Among the challenges faced by companies are high storage costs at demand locations, under-utilization of assets, lack of end-to-end business process visibility, and disconnected data in silos. At MAG, we build solutions that alleviate these issues in the most time and cost-effective way.

Solving For Asset Positioning, The MAG Way:

  • Our AI driven capacity matching ensures you always have the correct number of assets at the optimal location

  • ​Our solutions can help reduce the storage and repositioning costs for empty assets 

  • Get visibility over operations, with all data available to you always leading to less empty containers and a decreased fleet size.

  • We develop a unified data model that integrates with existing TMS, CRM and ERP systems​

Predictive Maintenance Planning

Industry Problem:
At MAG, we build proactive algorithms that designed specifically for efficient maintenance of transportation assets​. Among the problems faced by companies are unexpected breakdowns that disrupt operations and reduce service levels​, growing pressure to keep maintenance costs low​ and manual planning that is effort-intensive for large fleets and prone to errors​.

Solving For Predictive Maintenance Planning, The MAG Way:

  • We build and implement AI-based tools that can guide customers through numerous insurance-related queries without human intervention 

  • An AI-driven chatbot can not only be faster in resolving routine queries on an average, but it can also often make companies realise substantial savings in cost as well

Inventory Management

Industry Problem:
Companies generally struggle with excess raw material and inventory parts​, demand forecasts being non-granular (at the part level)​, poor On-Time-In-Full delivery levels​, disparate IT systems puts supply chain data into silos​, and the inability to view worldwide supply chains in near real time​. At MAG, we build system that solve these problems in the most affordable and time-efficient manner.

Solving For Inventory Management, The MAG Way:

  • Our AI-based optimization recommends reorder parameter changes (e.g., safety stock)​ 

  • Our ML models generate part-level demand forecasts by learning from historical variability in demand

  • Our based lead time predictions and alerts for projected late orders & supplier deliveries to meet OTIF targets​ 

  • We develop a unified data model that integrates with existing CRM, ERP & MRP systems​

  • Our end-to-end supply chain digital twin shows all inventory levels in near real time​

Planning and Scheduling

Industry Problem:
Among the business problems facing companies are inaccurate demand forecasting, poor On-Time-In-Full (OTIF) performance, ​frequent change order causing schedule disruptions​, rigid Linear Programming solutions being difficult to maintain​, and multiple tools, disparate IT solutions leading to data silos​. At MAG, we build effective solutions that mitigate these issues providing companies with valuable insights that help them streamline their planning.

Solving For Planning and Scheduling, The MAG Way:

  • Our AI-based demand forecasting leverages all relevant historical data​

  • We conduct a holistic optimization of all manufacturing and distribution schedules​

  • We build AI-powered solutions that can predict customer order modifications​

  • Our multipurpose what-if planning and scheduling analyses for different time horizons for all products​

  • We develop a unified data model that integrates with all systems

In need of smarter ways forward? Get in touch.