Digital Transformation

➤ Digital Leadership

➤ Digital Maturity Model

➤ Digital Organizational Design

One of the largest and most recognized material distributor and service provider in the prominent part of the world needed transformative improvements in their working of the supply chain management. They specialize in providing tailored services in the functional areas of global material distribution and materials management. They process millions of orders yearly catering to several thousands of established customers.

The esteemed organization desired the full utilization of data insights through algorithms. Three major roadblocks were obstructing their ambition. No presence of the right data. The questionable data quality. And the lack of appropriate technology for central storage of data in a proper format. The comprehensive plan was to diligently analyze data and develop algorithms for improved materials deployment.

The initial key task was optimizing specific problem areas that involved network simulations. The client aimed to optimize transportation costs and timely deliver stock without the sacrifice of service levels. The result was an Artificial Intelligence solution developed in-house. Since its initial deployment, it has been upgraded with updated knowledge and industry expertise necessary to drive improvements in target areas.

The Artificial Intelligence program was developed with the helping hand of a major Silicon Valley giant’s cloud platform. The self-learning algorithms ingrained in the program recommend suitable stock levels, machinery maintenance models and malfunction warnings. It recommends the physical sites that should be expanded and the ones to be shut. It assists the employees with tasks like assigning materials to industries, identification of the best transport route and recognition of the needs of individual locations.

The solution accelerated the decision-making process. Supplying locations of customers were easily identified, all relevant data was taken into account and the overall efficiency saw a steep elevation.

A prominent food manufacturing business approached Chools for their ambitious digital transformation project. Chools with its pioneering minds crafted a digital strategy that delivered a mesmerizing customer experience. Located in a city equipped with major industrial entities, the company had its processing plants spread over many other locations. Their next processing plant was already in construction.

The client wanted to effectively leverage the critical business insights provided by advanced technologies like Artificial Intelligence, Machine Learning and Business Intelligence. They possess the capability to form a data-driven organization culture. The company faced several challenges which acted as key obstacles for accomplishing their initiative.

➤ They lacked overall visibility. The data was stored on multiple locations which made it difficult to picture the holistic view of the business.

➤ Presence of multiple data sources. Data management at various locations and cloud applications was challenging.

➤ Prevention of misuse of data. The crucial task was to provide access to the right data for the right users.

The digital transformation was exclusively focused on improving supply chain operations. Next was the resolution of data and analytics related issues. The company embarked its data journey by adopting the digital transformation in phases.

➤The First Phase

The first phase comprised of envisioning sessions. The Chools team focused on understanding the key components and data flow of the business. The team studied meticulously the client requirements and formed a compelling vision for digital transformation. Chools team members displayed the potential capabilities of the solution developed and how it can be utilized to enhance the client’s business.

➤The Second Phase

Technical envisioning sessions were carried out. The architecture was worked out. Operational data store was built. SMA requirements were defined. Technical solution design was curated. Data sources were analyzed.

➤The Final Phase

In this concluding phase, the data model was defined. The data elements to support new models were identified. This phase was reviewed with BI resources. Technical sources were gathered. Comprehensive data sources were determined.

The Chools team carried out a diligent analysis of the data transformations. Data definitions were built through metadata mapping. In conclusion, the right level of granularity was identified for each level of the organization. Chools established itself in a pivotal role for crafting the digital strategy and monitoring the evolution of the data journey until the undertaking reached a satisfactory closure and possessed high business potential.