Supply Chain Analytics

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

Supply chain analytics are crucial in today’s competitive business environment, enabling companies to accelerate productivity, save costs, and improve customer satisfaction. By analyzing data from suppliers, logistics partners, and internal operations, businesses gain insights into their supply chain, identifying inefficiencies, forecasting demand, and enhancing inventory control. Leveraging sophisticated analytics, businesses make data-driven decisions to optimize supply chain operations.

Our Supply Chain Analytics services, supported by KEPLER Consulting’s expertise, significantly improve companies’ operations. With tailored solutions and years of experience in supply chain optimization, we help businesses achieve their objectives. Whether it’s enhancing supplier performance, reducing lead times, or optimizing logistics, our data-driven insights and best practices drive meaningful improvements in supply chain operations.

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 unpredictability and frequency of calamities pose challenges in planning and preparing for disruptions, affecting adaptability
Balancing efficiency and resilience in network planning
Optimizing for efficiency may lead to vulnerability to supply chain disruptions

Calamities Adaptability: Utilizing analytics tools to proactively anticipate and adapt to unforeseen events and disruptions
Network Planning: Analytics solutions for controlling warehouse capacity management, inter and intra transportation routes and supply chain networks

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 mitigating storage and inventory risks, while minimizing cost and maintaining service levels, is a challenge in risk management
Enabling seamless communication and collaboration across systems and stakeholders for end-to-end visibility and control over the supply chain is a challenge in integrating 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 implementing predictive analytics
Managing high volumes of sensor-generated data and identifying relevant information for real-time monitoring is a challenge
Identifying appropriate KPIs and ensuring accurate and timely dashboard data is a challenge in KPI dashboards
Selecting relevant training data and ensuring accurate, unbiased machine learning models poses a challenge
Promoting data sharing and effective collaboration among supply chain stakeholders is a challenge in collaboration platforms

Predictive Analytics: Enhancing data quality through a cleansing procedure that includes locating and removing irrelevant or lacking data, fixing discrepancies and errors, and making use of advanced algorithms
Real-time Monitoring: Employing machine learning algorithms to automatically filter and classify data, decreasing the need for human interaction and allowing for quick insights based on predetermined criteria
KPI Dashboards: Determining suitable metrics linked to business goals via KPI analysis, integrating data validation and verification for superior data quality
Machine Learning: Applying advanced data mining techniques and domain knowledge to identify suitable data and features for machine learning models. Employing explainable AI methods to evaluate and mitigate bias
Collaboration Platforms: Setting up a data governance framework to define data ownership, access rights, and usage policies. Implementing a secure collaboration platform for seamless data sharing and communication while ensuring compliance