Supply Chain Analytics

Streamline Your Supply Chain with Data-Driven Insights – Backed by KEPLER Consulting Expertise

Supply chain analytics are very important in today’s cutthroat business world. They help companies get more work done, cut costs, and make their customers happy. When they look at data from suppliers, transport companies, and the people inside their company, they see how everything in their supply chain works. They find where things are not done well, guess what people will want in the future, and stop too much stuff being stored. When they use smart analytics, they can make choices based on the facts. This keeps the supply chain working in the best way.

Our Supply Chain Analytics, with the help of KEPLER Consulting, make the way businesses work much better. When we give our own ways of doing things, and we have many years of having a better way of doing things in supply chain, we help companies reach what they want. If it is to make suppliers do better, make things come faster, or to better move things around, we get what the company needs from looking at data and using the best ways of doing things to make the supply chain work better.

Our Core Expertise In Supply Chain Analytics

Data gathering and analysis across several goods are needed to balance SKU rationalization with customer demand and satisfaction
Establishing channels of communication with suppliers utilizing various methods Data analytics tech, real-time data streams, and investments in infrastructure and technology are needed for continuous supply chain monitoring and reporting

SKU Analysis and Rationalization Analytics: Machine learning algorithms optimizing SKUs by leveraging metrics for client demand and satisfaction
Collaboration and Integration: Collaborative platforms and efficient data sharing systems with suppliers facilitate a seamless shift from traditional methods to advanced solutions such as control towers and business intelligence systems
Real-time Monitoring and Reporting of Key Supply Chain Metrics: Dashboard for real-time monitoring and insights on supply chain metrics

The thing that is not known and how often bad events happen make it hard to plan for them. This makes it hard to change what is done when bad things happen.
Find the right mix of the quick way to do things and the strong way to make them last.
Making things quick to do may make them weak against bad trouble in how things get to where they need to go.

Calamities Adaptability: Using tools that analyze data to help us see what might happen so we can change when a sudden event takes place
Network Planning: Data analysis solutions for taking care of warehouse space, routes for moving things and the supply chain.

Balancing efficient routing with accommodating unexpected delivery changes is a challenge in route optimization analytics
Collecting and consolidating data from various sources to calculate accurate delivery costs poses a challenge in cost-to-delivery analytics
Selecting and measuring relevant KPIs and driving process improvements presents a challenge in KPI analytics

Route Optimization Analytics: Enhancing delivery routes to save costs, increase customer happiness, and address last-mile delivery issues
Cost-to-Delivery Analytics: Locating opportunities for process improvement and cost efficiency in delivery operations
KPI Analytics: Control towers for monitoring KPIs and identifying areas for improvement

Identifying and reducing storage and stock risks, while keeping cost down and service high, is a challenge in risk work
Effective communication and work between systems and people for full look and hand in the supply chain is a challenge in adding storage-side analytics

Risk Management: Using predictive analytics to reduce storage and inventory management risks
Integration: Data analytics and machine learning for seamless communication and collaboration across supply chain systems and stakeholders

Ensuring data accuracy and quality for predictive models is a challenge in predictive analytics
Managing high volumes of sensor-generated data and finding the right info for real-time checking is a challenge in sensor data
Finding the best KPIs and making sure dashboard data is right and on time is a challenge in KPI dashboards
Picking the right training data and making sure the machine learning models are right and fair is a challenge
Helping share data and work well with others in the supply chain is a challenge in collab sites

Predictive Analytics: Better data quality with a clean-up step that finds and gets rid of data that does not matter or is not good enough; finds and fixes wrong or missed data; and uses special rules
Real-time Monitoring: Using computers with rules for how to find and sort data that cuts down the time a person would need to do this and helps see what is going on with the data in a snap with a set of rules
KPI Dashboards: Find the right ways to count that tell the right story in the right way for a business. Add rules to check if the data is right and to prove that it is right to have good data quality
Machine Learning: Use some of the best ways to find data and parts of data that can be used in a machine learning model. Use a way to get what is best to check bias and to make it less
Collaboration Platforms: Make a way to tell who can get data, how they can get it, and what they can do with data. Use a safe way for people to work with data and talk about it that meets any rules.