Building supply chain resilience: Key takeaways from our predictive planning webinar

Supply chain volatility now shapes daily planning decisions. Demand changes, supplier constraints, and unpredictable lead times can leave purchasing plans outdated before orders arrive. For companies that rely on spreadsheets and manual processes, the challenge is to respond before conditions change again.

In our recent webinar, Michal Svatek, Streamline Partner and Supply Chain Executive in the DACH region, Jihad Ashour, Co-Founder & CEO of Deep Horizon Business Solutions, Alex Kostin, Customer Success Manager at Streamline, and Jonathan Abernathy, Director of Sales at Streamline, discussed how companies can move from reactive supply chain management to predictive planning.

The discussion covered digital twins and dynamic simulation, supplier-centric planning, and AI-powered capabilities, along with practical applications and customer results.

Why traditional supply chain planning falls behind

Consider a purchasing decision where one product needs immediate replenishment, several other items from the same supplier will need stock soon, and minimum order quantities complicate when and how much to order. By the time the shipment arrives, demand may have changed again.

Disruptions along the Red Sea and Strait of Hormuz shipping routes add another layer of uncertainty. Changes in transportation conditions and customer demand can make lead-time assumptions and inventory plans obsolete within weeks.

For teams that rely on spreadsheets, the time required to collect data, evaluate options, and revise plans creates an additional challenge. The conditions behind a purchasing decision may change before the team can act on it.

Three components of predictive supply chain resilience

1. Digital twins and dynamic simulation

A digital twin brings demand, inventory, suppliers, orders, and incoming shipments into a connected planning model. Discrete-event simulation shows how inventory levels change as orders arrive and demand develops. What-if analysis lets planners test potential disruptions and evaluate responses before making purchasing decisions.

For example, Streamline's Time Machine allows planners to simulate a 20% increase in demand and examine its effect on future ordering plans and projected KPIs. They can then adjust assumptions, compare scenarios, and assess the potential consequences of different decisions.

2. Supplier-centric planning

Predictive planning also depends on coordination with suppliers. Sharing future demand and purchasing plans gives suppliers greater visibility into upcoming requirements, helping them prepare capacity and materials. Tracking supplier performance can also help determine whether better information sharing improves supply reliability.

Streamline incorporates minimum order quantities, lead times, and grouping constraints into purchasing plans. By grouping items from the same supplier, planners can consolidate purchases and choose between fuller container loads and lower inventory levels, depending on their priorities.

A planner facing a supplier-related shortage can adjust ordering conditions and generate a purchasing forecast covering the next 12 months within minutes. Sharing that forecast with the supplier provides a clearer picture of future purchasing requirements.

3. AI-connected planning workflows

With inventory and supplier constraints reflected in the planning model, planners can also access that data through natural-language requests.

Streamline MCP connects planning data with large language models such as Claude and ChatGPT. Users can retrieve information about demand and inventory, identify top-selling items, create filters, and build inventory risk dashboards through natural-language requests.

The connection respects existing Streamline user permissions and uses secure authentication without sharing Streamline passwords with the AI application.

The quality of AI-assisted analysis depends on the underlying planning model. Discrete-event simulation, supplier constraints, and data from multiple sources provide the foundation for planning results that AI applications can use.

What predictive planning delivers in practice

Predictive planning can improve inventory availability, reduce excess stock, limit manual work, and support more informed purchasing decisions. Two customer examples illustrate these outcomes.

Example 1

A Qatari business group operating 88 entities integrated Oracle ERP with Streamline to consolidate procurement processes and improve visibility across previously fragmented operations.

The group reduced its inventory holding period from 75 to 45 days, releasing working capital previously tied up in inventory. Customer order cancellations also fell by 25% following improvements in stock availability. The integrated environment gave management greater visibility into operations across the organization.

Example 2

A PC components supplier with 25 locations across three continents implemented Streamline ahead of its planned SAP S/4HANA implementation. The company established its procurement processes in Streamline first and used that work to define how locations, product groups, and sales channels would operate in the new ERP environment. 

This approach allowed the company to maintain established planning processes through the ERP transition. The project also reduced non-moving inventory, overstocks, and stockouts.

The case illustrates how companies can improve planning before a major ERP implementation and carry established processes into the new system.

Where to start with predictive planning

Demand forecasting provides a starting point for planning automation. Supplier planning offers another opportunity to reduce manual work, particularly when teams need to prepare purchasing forecasts, consolidate orders, and account for supplier constraints.

When evaluating planning solutions, companies should consider their ability to simulate inventory events, incorporate supplier requirements, and connect data from multiple systems. These capabilities determine how effectively AI tools can work with planning information.

Reliable predictive planning starts with current data, realistic supplier constraints, and the ability to simulate future scenarios. Together, these capabilities help teams anticipate disruptions, coordinate purchasing decisions, and use AI to support planning workflows.

See predictive planning in action

Discover how Streamline helps supply chain teams evaluate future scenarios, coordinate supplier orders, and make more informed inventory decisions.