April 6, 2017

Advanced Enterprise Analytics

 Supply Chain: Advanced Enterprise Analytics


Be it Custom Analytics from your fulfillment and planning processes or standard SCOR metrics for supply chain performance evaluation and then presenting and publishing the same, Our experts are quick at finding the right data sources from your transaction systems to advise and develop the right set of insights for consumption.

Forecasting ability and predictability Analytics wrt model fitment and selection is one of our unique services using which you can add ‘life’ to your demand planning implementations. Our real time volume x velocity x profitability computations makes demand forecasting so much more effortless and meaningful.

  • What is the Expected Expiration Risk of the inventory at this location?
  • What is the possible lost sales?
  • What will be the market share at the current rate in next 5 years?

We talk Analytics that empower you in your day to day business and not a mere intellectual or post facto indulgence.

Big Data and Business Intelligence Solutions

Big data. The combination of conventional way of doing business, and the ever-growing number of compitition on which people have to stay connected, continues to generate untapped sources of data that could help businesses compete more effectively.

But the unprecedented volume, velocity, and variety of the data available make it more challenging to properly analyze and mine it for potential business value.

As companies begin to deeply explore what big data can do for them, it’s important that the chosen enterprise solution is able to address both business intelligence and big data.

We think of this approach as Big Data and Business Intelligence Solutions !!!

Internet of Everything (IOE)

“Data is the new oil and Foundation of the fourth industrial revolution will be laid on connectivity and data.” This is true. Raw data is of a new use. Insights need to be generated from the collect data to make sense of it. Information technology is changing the way industries manufacture products, provide services (or both) and go about conducting their day to day operations. In addition to differentiating the firm from existing and potential competition, it is also important to achieve operational effectiveness. Industrial manufacturing firms, all around the globe, in the manufacturing sector are increasingly using automation coupled with IT to boost productivity and cut costs. Many firms have started using ERP software to improve their operations and inculcate the environment of continuous improvement by measuring, analysing, controlling and recalibrating their processes. Technological transformations are shaping industries of the future and making them more data driven. Everything from conceptualisation, design, manufacturing, servicing and maintenance is being done with the help of data.

Anand IT’s Smart Machines Solutions analyzes real-time and historical data across key health parameters and predicts the serviceable life of assets in order to decrease downtime and increase the asset utilization. The main goal is to collect asset information more efficiently and accurately, in real-time, and also enable usage of analytics to help companies make the right decisions.

The solution brings in a holistic approach to monitor, control, and optimize the assets across all tenants of efficiencies that includes operational, energy, maintenance, information and service.


Predictive Maintenance

Unscheduled equipment downtime is destructive for businesses and simply waiting for a failure to occur is not an effective strategy. Predictive maintenance layers in science to add precision to your business’ maintenance management resulting in operational cost savings.

The mining industry have been challenged with the major problem of downtime of critical assets that has resulted in losses amounting to lacks of rupees even in a very small enterprise. Current challenges include lack of instrumentation of the assets, missing real-time data analytics, lack of context due to missing information from other systems, lack of aholistic focus with other aspects of efficiency like energy, utilization, operations, and serviceability. Effective management of assets would help overcome this scenario, which can be achieved through smart machines.

However, over the last few decades, equipment and systems have become intelligent. They generate data, which if monitored and managed well, can help engineers predict system failures accurately and prevent these failures beforehand.

Some of the key features of smart machines include:

● Condition monitoring: Measure and track key health parameters of assets.

● Diagnostic analysis: Use analytics engine to analyze data, compare with past data, and identify any present anomalies.

● Prognostics: Predict the remaining useful life of assets.