Supply Chain Software Development Services

Upgrade Your Unique SCM Processes

Since 2012, Edvenswa has provided companies in 30+ industries with consulting and practical assistance on the design and implementation of reliable supply chain management software.

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Supply Chain Management

Supply Chain software development aims to help companies drive improvements across their processes and innovate their business operations with the help of effective software and Supply Chain 4.0 techs: IoT, AI, big data, blockchain, AR and VR.

Benefits of Digital Supply Chain Management

Accurate Demand Forecasting

Increased accuracy of demand forecasting

Supply Chain Visibility

Improved visibility into an extended supply chain and efficiency of risk management activities

Optimized Inventory Planning

Enhanced reliability of inventory planning and reduced inventory carrying costs

Efficient Resource Utilization

Higher resource utilization (labor, transportation, etc.)

SCM Solutions Edvenswa Specializes In

We offer development services for the implementation of one or several integrated SCM solutions:

Supply Chain Planning & Optimization

Demand forecasting.

AI-supported recommendations on supply chain optimization (e.g., how much and when to order).

Supply chain digital twin – a real-time supply chain model to test different supply chain action plans (e.g., safety stock planning, transportation optimization) and evaluate their impact before choosing the best one.

Supply Chain Risk Management

Supply chain control tower, offering real-time, AI-supported visibility into all supply chain operations, including operations of suppliers and external carriers.

Configurable dashboards for a unified view of supply chain processes and related KPIs (fill rate, order cycle time, etc.).

Collaborative issue resolution with suppliers via a shared space for issue discussions, issues status tracking and notifications, etc.

Inventory & Warehouse Management

Inventory levels and location tracking (with barcode or RFID technology).

Optimal safety stock calculation.

Automated replenishment triggers.

Lot and serial number tracking.

Expiration dates and shelf life monitoring.

Logistics Management

Freight tracking.

Planning and optimization of route schedules.

Vehicle accident case management (accident notifications and reports, repair request issuing and routing, etc.).

IoT connectivity to monitor product condition during transportation.

Procurement Management

Quick template-based creation of purchase requisitions, RFxs, and purchase orders.

Automated approval workflow for purchase requisitions and purchase orders.

Analytics-based recommendations on supplier assignment to purchase orders.

Creation and management of preferred supplier lists.

Automated three-way matching (purchase orders, order receipts, and supplier invoices are cross-compared to reveal inconsistencies if any).

Purchase order execution tracking (for several tiers if required).

Collaboration tools to discuss orders with suppliers.

Supplier Relationship Management

Analytics-based supplier pre-qualification (financial viability, technical capabilities, ethical business processes, etc.).

Quick template-based creation of sourcing events (e-tenders, e-auctions).

Collaboration with internal teams on supplier selection and nomination.

Supplier performance analytics and ongoing compliance checks.

Supplier portal for improved capacity planning (via collaboration with suppliers), etc.

Automatic alerts for suppliers to update expiring data (accreditations, certifications)

Order Management

Centralized multichannel sales order processing.

Automated order routing to an optimal fulfillment location.

Support of multiple order fulfillment methods and types (BOPIS, ship-from-store, same-day delivery, etc.).

Return management.

Supply Chain Software Development Cost Factors

The number and complexity of functional modules required to cover business needs.

The number and complexity of integrations with corporate software, IoT devices, etc.

Data volume used for analytics, the need to implement machine learning algorithms and their complexity.

Data storage type (a centralized database or a private blockchain).

Application availability, performance, security, and scalability requirements.

Required application types – web, mobile, desktop – and a number of platforms supported (for mobile apps).

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