Distribution network optimization
10–15% reduction in logistics costs
Distribution network optimization
Key use cases
A light vehicle OEM optimized transportation routes and calculated its actual costs to identify savings of up to 20% on transportation costs
Case example 2 / 9
The automaker lacked transportation cost transparency for its network of global suppliers. In response, it simulated various routes and transportation supplier scenarios that considered freight rates in order to optimize routes and quantify the savings potential.
This resulted in a detailed should-cost model for individual transportation routes. Side-by-side analyses with supplier cost breakdowns helped it identify individual routes in the overall transportation scheme for the greatest cost savings opportunities. Overall, it identified transportation cost savings of up to 20 percent.
A tire distributor in North America realized savings of 10-15% in logistic costs using analytics-based distribution network optimization
Case example 1 / 9
The distributor ships products through a network of 150+ warehouses located throughout North America. To maintain high service levels, it redesigned and consolidated its distribution network.
The tire distributor employed advanced analytics techniques to optimize the costs associated with its supply chain networks. The tools allowed the company to move, insert, or delete warehouses, consolidation centers, or plants at any location, while preserving or rerouting flows accordingly and automatically updating the costs based on new transport lanes and warehousing factor costs. Network optimization and redesign using advanced analytics toolkits reduced its logistics costs by 10 to 15 percent.
10–15%
reduction in logistic costs
Analytics-based transportation route target costing
Analytics-based transportation route target costing
10–20% savings of transportation costs
Supply chain risk modelling
Supply chain risk modelling
40% reduction in high-risk spend
Analytics-based scheduling optimization
Analytics-based scheduling optimization
25% production throughput increase
Digital warehouse design
Digital warehouse design
10% reduction in capital expenses
Automation of physical flows to and from production
Automation of physical flows to and from production
25% increase in operator efficiency
Analytics-based demand forecasting
Analytics-based demand forecasting
5% sales increase; 15% inventory reduction
Supply chain data integration for planning
Supply chain data integration for planning
20–30% increase in planner productivity
Digital S&OP control tower
Digital S&OP control tower
50% reduction in inventory value
A global electronics manufacturer identified a 40% reduction in high-risk spending by performing a risk assessment of its global manufacturing and supplier network
Case example 3 / 9
The company was concerned about supply chain risk caused by the COVID-19 pandemic. It had a global manufacturing network of 10+ plants and a large multi-tiered supply base with over 5,000 suppliers with high concentrations in certain categories and a large number of multi-source arrangements.
The manufacturer created a digital model for the entire supply chain to assess its relative vulnerability across multiple factors, such as lead time, supplier concentration in different categories, COVID-19-specific country risk, and financial resiliency. This allowed it to develop a risk mitigation plan. It identified an over 40 percent reduction in high-risk spending, more than a hundred priority high-risk suppliers, and flagged over 10 financially distressed and previously unknown tier-2 suppliers for review.
A large industrial company boosted its production throughput 25% using a production scheduling optimization tool
Case example 4 / 9
The company relied on heuristics decisions to plan and schedule production. It operated a wide variety of equipment and shared inventory storage between production lines. To improve its planning and scheduling processes, the company developed a mathematical model of the entire production process including all relevant constraints and cost drivers. The model had 45 nodes, 80 flows, and 50 technical constraints, and the company used it to build an optimization tool that could maximize profit.
Use of the tool resulted in a 25 percent throughput increase, and it allowed the company to completely avoid outsourcing costs.
25%
production throughput increase
A North American manufacturer used a digital warehouse design to consolidate multiple warehouses, reducing planned capital expenses 10% and operating expenses 30%
Case example 5 / 9
The company decided to consolidate several regional manufacturing and warehouse locations into a single campus, with separate buildings dedicated to manufacturing and warehousing. However, it faced several challenges, including capital constraints, insufficient warehouse space, and a need to move fast.
Instead of relying on traditional tools, it used a digital warehouse-design approach to simulate various options across warehousing, kitting, and value-added operations. The digital tools allowed the company to develop detailed opex and capex estimates for both manual and automated solutions. This made it far easier to evaluate various business cases and scenarios. In all, the company reduced planned capital expenses approximately 10 percent and operating expenses more than 30 percent.
Schneider Electric increased operator efficiency 25% and reduced run time 80% by automating the request and delivery of parts with AGVs
Case example 6 / 9
The company wanted to improve production line efficiency and identified several repetitive tasks concerning the request and delivery of parts to and from production. Consequently, It decided to automate these processes by introducing automated guided vehicles (AGVs) within the production line.
The company thus introduced laser-guided AGVs to automate its request and delivery of parts to and from the production line. As a result, operator efficiency increased up to 25 percent, while throughput time fell 80 percent.
25%
increase in operator efficiency
Collecting and analyzing data along the supply chain enabled an advanced industrial company to reduce inventories 20% and increase planner productivity 20–30%
Case example 8 / 9
The company completed a major effort to integrate its supply-chain processes and implement a new ERP system. As part of this effort, it set up data streams from sources across its entire supply network.
The company fed all incoming data into a common data engine to connect and analyze information from different sources. This helped it identify how activities and decisions in one part of the supply chain influenced operations elsewhere. Within a few weeks, the company uncovered several systemic issues, such as mismatched lead times and past-due purchase orders that prevented reliable indicators of future demand from reaching suppliers. Since then, the data engine has enabled the company to reduce its inventory by 20 percent and improve the productivity of its planners 20 to 30 percent.
20%
reduction of inventory
A medical device manufacturer captured reductions of 50% in inventory and 60% in backorders by using a digital control tower for its S&OP processes
Case example 7 / 9
The company was struggling with its operational planning performance combined with constrained supply, which resulted in low service and excess inventory. In addition, it lacked transparency and forward visibility.
The manufacturer built a digital control tower for its sales and operations planning (S&OP) processes, using existing data sources. It implemented automated workflows to merge data sources and prepare the data for visualization in interactive dashboards. This enabled it to improve service and inventory performance, identify systemic problems and prevent future issues. The implementation resulted in a 50 percent inventory reduction, a 60 percent decrease in backorders, higher visibility of future shortage situations, and a 10 to 20 percent service level improvement.
50%
reduction in inventory value
10–20%
savings in transportation costs
40%
reduction in high-risk spend
100+
priority high risk suppliers identified
10%
reduction in capital expenses
30%
reduction in operating expenses
80%
throughput time reduction
60%
reduction in backorder value
20–30%
increase in planner productivity
A heavy machinery player increased sales 5% and cut inventory 15% by rolling out an advanced analytics tool for demand forecasting
Case example 9 / 9
The company struggled to achieve demand forecasting accuracy and introduced an advanced analytics-based tool to improve its capabilities. The tool aggregated company and market performance, including financial performance parameters, and integrated those data with the total market forecast. This advanced predictive analytics approach considered multiple demand-influencing variables and made it possible to consider different scenarios, including possible outcomes and their likelihoods. With this tool in place, the company improved its demand forecasts from 40% to 70%, resulting in a 5% increase in sales due to higher product availability and an inventory reduction of 15%.
5%
sales increase
15%
reduction in inventory