Supply Chain Analytics

Supply Chain Analytics gives an insight into reforming planning and procurement activities along with intelligent chartering and dispatching of materials. One of the promising features it offers is an overall insight into the business by consuming relevant KPIs via Power BI dashboards.

The supply chain analytics vision is to provide conventional methods, deep learning approaches, and modern open source frameworks like Prophet, to deliver supply chain analytics solutions with more room for modern analytics and dashboard capabilities.

Ability to integrate with supplier and sub-supplier data in different source formats.
Utilize the existing project schedule in a way that the tracking data can be compared against a set of baseline schedule parameters.
Provide real-time visualisation of data in an agreed and effective way in order to communicate efficiently with various people in the ecosystem.
Comply with the security standards without exception or restriction.
Create a two-way communication stream between the supplier and sub-supplier systems.

Need for Supply Chain Analytics

Supply Chain Analytics brings data-driven intelligence to your business, reducing overall cost to serve and improving service levels. It aims to improve operational efficiency and effectiveness by enabling data-driven decisions at strategic, operational, and tactical levels. It encompasses virtually the entire value chain: sourcing, manufacturing, distribution, and logistics.

In today’s competitive global marketplace, excellence in supply chains is a key competitive differentiator. Hundreds of turbulent events can trigger volatility in supply markets, affecting material and service availability and resulting in higher costs. There is a need for real-time performance visibility and insight to provide a strategic advantage that enables operational agility to minimise the impact of disruptive events.

There is an increased interest in real-time supply chain support tools that address supply chain traceability, multi-level inventory optimization, demand signal repository, and leverage point-of-sale data.

Relying on traditional supply chain execution systems is becoming increasingly less viable in the context of global market fluctuations, pricing pressures, and increasing customer expectations. There are also recent economic impacts, such as fluctuating fuel costs, the global recession triggered by the financial crisis and increased competition from low cost manufacturers. All these factors can potentially create excesses in your supply chain, but this is where analytics will help to provide a competitive edge.

Our Capabilities and Approach

All businesses with supply chains devote a fair amount of time and effort to looking for this competitive advantage. Due to their complexity and critical role in global commerce, supply chains play a key part in a company’s cost structure and profitability. The use of analytics in supply chains is a great way to provide an edge over peers. Supply chains can look simple compared to other parts of a business, even though they aren’t. It is possible to dig deeper into existing data and develop more predictive insights, enabling a business to become proactive rather than reactive. With predictive analytics, manufacturers can leverage the data within their systems, both legacy and modern, to derive real-time insights and use that information to optimise all decisions, be they operational, tactical or strategic. The industry is quickly adapting to a range of analytics solutions. An overview of a few of the areas and an indication of the kinds of insights that can be undertaken is given below:

Inventory Optimization
- What inventory buffers should help and where should they be positioned?
- Safety stock recommendations
- Inventory Budget Optimization
Replenishment Planning Analysis
- When, what and where should I Ship?
- Optimize for transportation and ensure feasibility to address handling, storage, and warehouse constraints.
Measuring Supply Chain Metrics and Reporting
- How is my supply chain performing and where should there be an improvement?
- SMART Metrics- Specific, Measurable, Actionable, Relevant, and Timely.
Demand Analytics
- What is my demand forecast, and how does it compare to retail sales?
- SMART Metrics: Specific, Measurable, Actionable, Relevant, and Timely.
Network Planning and Optimization
- Do I have the right network and optimal network flows?
- Number of locations, manufacturing facilities, warehouses, etc.
- Optimized flow paths to fulfil customer demand at the lowest total cost.

Our Capabilities in Supply Chain Analytics include:

Virtual Control Tower: A Cloud-based online platform with a role-based access system Data

Integration Ability
- To integrate with supplier and sub-supplier data in different source formats.
Monitoring and Data Analytics
- Utilize the existing project schedule in a way that the tracking data can be compared against a set of baseline schedule parameters.
Compliance
- Comply with the company’s IT security standards without exception and without any limitation.
Realtime Visualization
- Provide real-time visualisation of data in an agreed and effective way in order to communicate efficiently with various personas in the ecosystem.
Two-way Communication
- Create a two-way communication stream between the company and the supplier systems.
Cognitive Computing Engine
- Compute on Demand, Constraint-based Programming
- Data Analytics, Statistical Models, Predictive Analysis, and Scenario Management are collaborative.
Responsive Mechanism
- Insights, Actions, Tracking, Monitoring & Control
- Internal and External Collaboration
Early 360-Degree Visibility
- Data Consolidation, Mapping, and Preprocessing
- Data Feeds: Any System, Data or Format
- Alerts and Exceptions Management
- Presentation, Mobility & Security

What’s in it for you?

Visibility– E2E visibility into the required stocks, domestic and international supply of the material, and import requirements.
Cost– $/ton reduction and demurrages paid.
Mobility– the availability of critical information on mobile devices.
Drive Decision Making– Start analysing patterns in procurement due to seasonality and external factors and suggest decisions based on the forecasted trend.
Automation– minimal manual intervention for all reports and dashboards is needed for visibility.
Interfacing and Integration– Integration with the existing ERP for data utilization. Also, options for manual entry
Analytics– Analytics for developing models on different scenarios of logistics planning, procurement, inventory and port operations to predict materialization and optimize the resources with effective rake planning and scheduling.

Realize Your Digital Journey

Cloud Migration– Move your On-Premises Infrastructure to the cloud.
Smart Automation using Intelligent Processes
Serverless Computing – Run Scalable Applications
A Semantic Data Model– Data Democratization Within the Organization Constraint-based Optimization
Run Mathematical Models to Optimize Leakages and Costs
Intuitive Reporting – Using the interactive BI layer, realise the true KPIs.
Data-Driven Processes Replace intuition with a data-driven approach.

Move from reactive to proactive operations

Build an agile, connected, and resilient supply chain with Dynamics 365 Supply Chain Management. Streamline planning, production, stock, warehouse, and transportation to maximise operational efficiency, product quality, and profitability using predictive insights from AI and the Internet of Things (IoT).

Sales-Domain:

To run a profitable business, it is imperative to check the sales and identify the areas that are underperforming. To solve this problem, we have created the Sales Dashboard to show the comparison between the trade value and the target that needs to be achieved. This Dashboard gives information on sales quantity and net value, which can be drilled-through based on volume, revenue, tax details, rebate, and trade. The information in the dashboard allowed further slicing and dicing based on the region, products, and wholesalers to further analyse the data. This allowed the user to match the current data with the target data and identify the areas that were lacking to correct the projection to match the expected targets. The dashboard also had historical data comparisons ranging from monthly and yearly with the current data to get a relative idea of the company’s sales figures.