Automotive & Industrial
Often derided as yesterday’s news or rust-belt has-beens, automotive and industrial manufacturers are unsung heroes of the global economy. While not typically flashy or in the public eye, these manufacturing and distribution stalwarts keep the wheels of commerce turning.
Despite their low profile, players in this sector face difficult operations management challenges. Complex networks are subject to frequent change, whether due to market shifts, factors like changing fuel costs, acquisitions, or even government action (e.g., changing tariffs and taxes). Planning demand in the face of low volumes and long development cycles and planning supply for a complex network of first- and second-tier suppliers are both hard. And satisfying requirements for post-sales support presents yet another challenge.
End-to-End Analytics has deep experience working in this sector, and we frequently help our clients to find good solutions to problems like these:
Complexity is the bane of automakers’ lives, but simplifying the automotive supply chain is not straightforward. For example, complexity costs are diffuse and can be tricky to quantify. It can also be difficult to isolate the biggest driver(s) of complexity. Our analytical approach allows us to:
- Systematically identify possible and actual build combinations.
Identify and analyze drivers of complexity.
Rank performance of build combinations by turns and profitability.
Dynamically visualize complexity trees and performance metrics
- Reduce complexity by focusing on most desirable configurations.
Optimize for local and regional market differences.
Implement incentive systems and promotions to increase overall profitability across the supply chain
Forecasts are always wrong, but how wrong? A common error is to obsess over a “point” forecast, when time would be better spent quantifying the range of possible outcomes around that forecast. The resulting “range forecasts” turn out to be far more useful than point forecasts for making decisions around inventory, capacity, and supply chain risk.
In implementing the range forecasting approach, we work with clients to:
- Systematically quantify short-term and long-term forecast error.
Identify and analyze outliers and root causes.
Develop statistical forecast models to validate internal forecasting processes.
Reduce forecast error through process improvements and advanced analytics
- Develop and apply range forecast modeling to generate statistically sound upside/downside forecasts.
Apply forecast ranges to make business decisions in capacity planning, finance and other planning functions.
Validate “what-if” scenarios against statistically sound ranges
Supply chain management often focuses heavily on inventory. But decisions around capacity are often more important: Capacity typically involves larger, less liquid investments, and longer lead times that increase the risk of mismatches between demand and supply. To improve capacity planning we work with clients to:
- Optimize and align internal assembly capacity and external supplier capacity.
Identify possible constraints and perform what-if analyses.
Perform risk analysis using ranges
- Optimize sourcing strategy across commodity groups and raw materials.
Monitor and react to forecast and other changes .
Sourcing decisions can be critical for automakers, as they lock in a large fraction of final costs and constrain future flexibility. Our analytical approach to sourcing begins with a long-term forecast which is continuously refined over time. This allows us to:
Develop demand projections to support strategic and tactical capacity alignment with supply base.
Coordinate sourcing across products and global regions.
Share capacity across regions
Optimize sourcing strategy across commodity groups and raw materials.
Conduct upside/downside risk analysis.
Inventory optimization for automakers is complicated by factors such as the large number of parts involved, widely dispersed inventory banks, and frequent engineering changes. To establish control over inventory, we work with clients to:
- Measure historical inventory performance.
- Analyze the root causes of poor performance.
Project future inventory levels and stockout risks.
Optimize inventory and safety stock policies
Manage End-of-Life (EOL) situations and engineering changes.
Manage trade-offs in order to reduce obsolescence and constraint cost.
Analyze spare parts / accessory demand and capacity
Launch Planning & Execution
Planning and executing the launch of new automobiles is a process fraught with difficulties. Evolving customer preferences, last-minute design changes, and supply constraints all combine to erode already thin margins. To improve launch planning and execution we help automakers to:
Deploy systematic processes to collect and analyze Pilot Orders prior to launch.
Reduce complexity to streamline planning.
Develop and analyze launch what-if scenarios.
Initiate capacity studies to identify and address capacity constraints
Develop launch play books, update and monitor forecasts and schedules.
Monitor demand and available capacity.
Alert for upcoming constraints, address shortfalls and manage allocation strategies
Allocation & Distribution
Producing great products at competitive costs is not enough for success in automotive supply chain management: automakers also need to put products in the right place. Our work in the twin areas of distribution and allocation helps manufacturers to:
- Monitor what is being actually being sold and feed this back to update forecasts
Improve product allocation and consensus cycles processes.
Monitor days of supply and identify where is product needed.
- Manage forecast aggregations and analysis.
- Synchronize forecasting and production scheduling across channels.