“Transform your operations with data-enabled supply chain optimization.“
Supply chain analytics consulting is a vital sector for businesses in the current age because organizations need optimised and data-driven strategies, to remain competitive and excel. Business organisations in all industries leverage supply chain analytics to identify areas of improvement, enhance efficiency, decrease their costs of operation, boost customer satisfaction and stay ahead of the competition.
The services provided by KEPLER Advanced Analytics supply chain analytics consulting firm vary based on their clients’ requirements. As a general rule, we entail collecting and pooling data, analysing and modelling the data, tracking the company’s performance, utilising predictive analysis, and providing strategic suggestions.
An important advantage of supply chain analytics consulting is making data useful in the business process. The massive collection of data, both inside and outside a business, can be used effectively with advanced analytics techniques to find patterns, opportunities and trends that would otherwise remain hidden. Advanced analytics techniques such as machine learning, optimisation algorithms and simulation models help consulting firms analyse and predict buyers’ behaviour, improve purchasing decisions, assist in forecasting and demand planning and raise the chances of triggering favourable outcomes.
The thrust of supply chain analytics consulting, then, is innovation: you put historical data and performance metrics on a digital map, and unleash strategic consultants who make sense of it towards finding areas for possible gains by proposing process improvements, technology upgrades and automation solutions.
Our Core Expertise In Supply Chain Analytics
- This logic can be applied only if data collection and analytics are performed across and between SKU rationalisation and customer supply and satisfaction.
- Establishing channels of communication with suppliers utilizing various methods
- Data analytics tech, real‑time data streams, investments in infrastructure and technology are required for ongoing supply chain monitoring and reporting.
- SKU Analysis and Rationalization Analytics: Harnessing machine learning algorithms to optimise SKUs based on metrics such as client demand and satisfaction.
- Collaboration and Integration: Collaboration platforms and the rapid deployment of efficient data-sharing with suppliers enable the smooth swap from traditional to collaborative supply-chain solutions such as the control tower and business intelligence systems
- Real-time Monitoring and Reporting of Key Supply Chain Metrics: Present a real-time dashboard of key supply chain metrics and insights.
- The uncertainty and timing of disasters makes planning and preparing for disruption difficult, lowering flexibility.
- Balancing efficiency and resilience in network planning
- Optimizing for efficiency may lead to vulnerability to supply chain disruptions
- Calamities Adaptability: Tapping analytics to anticipate and accommodate unexpected events and disruptions.
- Network Planning: Analytics used for the management of warehouse capacity, inter and intra transportation routes and networks.
- Route optimisation analytics must achieve efficient routing but still be flexible enough to allow for unexpected changes in delivery needs.
- One of the difficulties of cost-to-delivery analytics is that it needs to collect information from several different sources in order to come up with a true delivery cost.
- Selecting and measuring the right KPIs and driving relevant process improvements are challenges in KPI analytics.
- Route Optimization Analytics: Helping to shorten delivery routes to save money, make customers happier and tackle the challenge of last-mile delivery.
- 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
- Minimising the cost of mitigating storage-and-inventory risks, while maintaining service levels, may require new forms of agility: Could a railroad container be broken down for various uses, while anticipating the outcome of production?
- Integrating storage-side analytics for end-to-end supply chain visibility and control is hard to achieve across systems and stakeholders
- Risk Management: Using predictive analytics to reduce storage and inventory management risks
- Integration: Data analytics and machine learning to enable communication and collaboration across all supply chain systems and actors.
- Adopting predictive analytics for binning and balancing the data and targeting customers are driven by business goals, whereas using data to reduce costs, sell more products and enhance user experiences are driven by business practices. Improving the accuracy and quality of the data to support predictive model development is a technical issue, and so is handling data at different levels of detail with varying precision and speed.
- Masses of data from environmental sensors need to be parsed into meaningful information that can be monitored in real time.
- Primarily, measuring suitable KPIs and making sure that KPIs dashboards convey the data correctly and also in a timely manner is a significant challenge slightly- – Primarily, making sure that KPIs dashboards is an important track in a firm since the right KPIs means that business has been adequately born.
- Selecting relevant training data and ensuring accurate, unbiased machine learning models poses a challenge
- Sharing of data and collaboration between stakeholders across different companies in the supply chain is a barrier to collaboration.
- Predictive Analytics: improving data quality by subjecting it to such a cleansing procedure as finding and eliminating data that is irrelevant or incomplete, correcting errors and inconsistencies, and using advanced algorithms.
- Real-time Monitoring: Using machine learning algorithms to automatically filter and classify data. Less interaction with human operators is required, and characteristics that are a priori labelled as of interest can be surfaced in near real-time.
- KPI Dashboards: Perform the KPI analysis to identify suitable metrics, map them against business goals, and integrate data validation and verification functions to ensure data quality.
- Machine Learning: Advanced data-mining techniques combined with domain knowledge to identify potentially useful data and features for machine-learning models. Utilisation of explainable AI methods to assess and mitigate bias.
- Collaboration Platforms: Environment: Delineate who owns data, who can view it, and who can use it through a data governance framework.Build: Provide secure access to data that is shared and controlled by a single organisation, as well as facilitate communication among team members, in the same environment.Comply: Ensure that the platform is tamper-proof and facilitates real-time compliance validation against organisational data protocols.
Industries We serve
Personal Care
Unleash the potential of personal care industry with cutting-edge analytics!
Automotive
Drive into the future of the automobile industry with analytics at the helm!
Medical
Revolutionize healthcare with insights from the forefront of data analytics!
Private Equity
Revolutionize private equity performance with the strategic use of analytics and big data.
Packaging
Packaging that delivers - elevate your brand with data-driven analytics!
Energy
Empower your energy business with data-driven insights and advanced analytics technologies.
Aerospace
Harness the power of analytics to improve aerospace operations and customer experience.
Defense
Transform defense operations and decision-making with cutting-edge analytics technologies.
Railways
Unlock the full potential of railway transportation through the power of analytics.
Retail
Unlock the full potential of retail sales and profitability through strategic use of data analytics.
Personal Care
Unleash the potential of personal care industry with cutting-edge analytics!
Private Equity
Revolutionize private equity performance with the strategic use of analytics and big data.
Aerospace
Harness the power of analytics to improve aerospace operations and customer experience.
Automotive
Drive into the future of the automobile industry with analytics at the helm!
Packaging
Packaging that delivers - elevate your brand with data-driven analytics!
Defence
Transform defense operations and decision-making with cutting-edge analytics technologies.
Medical
Revolutionize healthcare with insights from the forefront of data analytics!
Energy
Empower your energy business with data-driven insights and advanced analytics technologies.
Railways
Unlock the full potential of railway transportation through the power of analytics.
Retail
Unlock the full potential of retail sales and profitability through strategic use of data analytics.
