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Future-Proofing Supplier Capacity Management in the Automotive Sector

  • Sharad Jadhav
  • Shish Kumar
  • Jitendra Kandpal
  2018

Staying competitive in today’s complex and dynamic automotive market requires automotive original equipment manufacturers (OEMs) to balance their demand-supply equation. This makes it possible to not only meet customer demand, but also optimize overhead and keep capital expenses under control.

While the industry has significantly evolved in responding to customer demands quickly and efficiently, it still faces a number of issues related to effectively managing supplier capacity. These challenges lead to significant lost capital and untapped opportunities.

So, how can OEMs ensure that the right capacities are created and maintained? An efficient and responsive solution that continuously monitors demand signals, analyzes their components, and compares them with supply to highlight constraints and overcapacity is a must. Such data-driven analytical tools and techniques can offer better supply chain visibility across different functions and help build a robust supply chain strategy through advanced analytics.

Capacity management

Many OEMs lack an integrated, automated capability to calculate demand for parts based on forecasts or actual orders and the ability to compare it with supplier-installed capacity across the entire product life cycle. Identifying areas of risk and quantifying the effects of changes to forecast production demand also are limited, typically resulting in demand-supply mismatch, overused supplier capacity and compromised customer aspirations. On many occasions, this leads to line-run-without-component situations and, ultimately, failing to deliver vehicles on promised dates. This of course negatively affects the customer experience.

Other key challenges facing automotive OEMs today include the following:

  • Product complexity. The uniqueness of the automotive industry lies in its ability to offer millions of vehicle configurations to satisfy every customer segment. A feature is a unique product functionality that differentiates a vehicle. Each feature translates into demand for one or more unique parts, potentially creating high product complexities that pose challenges in parts procurement.
  • High demand variability. Given the deep focus on customer-centricity, OEMs often are forced to alter a completed order just a few days or weeks before delivery. This leads to high demand fluctuations and influences downstream activities, including supplier capacity.
  • Global expansion. This provides OEMs with opportunities to examine existing efficiencies that directly affect profitability. However, it also increases the challenges related to seamlessly integrating fluctuating demand with supplier capacity.
  • Ineffective feature-to-part mapping. A significant problem in understanding demand signals lies in the complexity of the feature mix and demand calculations. Customers can choose from among hundreds of features across multiple car lines. Each option is made up of several parts. To streamline production, it is essential to accurately estimate part demand and enable visibility across product lines.

Furthermore, the absence of analytical methods to identify and improve data quality in capacity planning leads to the additional challenges:

  • Supplier capacity is recorded at the part level, while demand is monitored at the feature level.
  • The data used by each of the cross-functional teams involved in demand and capacity planning is neither connected nor transparent, making close coordination a challenge.
  • Inconsistent data across the product life cycle and limited what-if scenario capabilities bring about significant manual effort related to supporting change management and collaboration.
  • Incomplete planning for after-sales parts demand is left out of capacity planning.
  • Disconnected systems cause error states to go unnoticed.
  • There is difficulty analyzing the impact of future allocation changes on the supply base.

Real-world solutions

The bottom line is that automotive OEMs struggle to connect disparate data with actionable insights. Fortunately, there is a solution: an approach centered on data that maximizes the value of existing information technology tools and investments. This tactic provides visibility to capacity information, enables what-if studies and helps users gain access to actionable insights by connecting disparate, cross-functional data.

This data-driven solution recently was built for two large automotive OEMs in order to help them enhance capacity, achieve greater visibility and enable better stakeholder decision-making. The following two case studies illustrate the proven potential of next-generation capacity management.

A luxury car manufacturer focuses on delivering unique, customer-desired configurations. A global expansion in terms of production footprint and sales penetration led to the proliferation of product variants offered and pressurized supplier capacity in terms of volume and variety. Millions of configurations made demand forecasting very challenging. Demand fluctuations affected supplier parts availability downstream, influencing delivery performance and freight.

The issues demanded an advanced analytics solution equipped with a capacity variance dashboard tool. Such a tool is designed to help the business

  • compare demand and capacity variance across program and production stages
  • enable what-if analysis and identify the optimal approach to minimize capacity gaps and maintain production using a supplier-capacity-scenario analyzer
  • provide a single repository for capacity control and a data visualization dashboard with additional reporting capability.

The highly cross-functional nature of the solution demanded a unique approach. The product creation and evaluating dependencies, limitations and challenges and presenting them to project stakeholders and sponsors at requirement workshops. Tools and dashboards were designed prior to approval and finalization through follow-up sessions with subject matter experts. The environment encompassed capacity planning data, gateways, a business intelligence engine and output in the form of dashboards for cross-functional teams.

The solution collates capacity planning data from multiple sources and drops the data at different gateways. The business intelligence engine then extracts, integrates, transforms and displays logical data on dashboards, providing a single source of information to cross-functional teams. As a result, the OEM has real-time visibility into demand and supplier capacity variance, from program to production, along with what-if analysis capabilities. Other benefits include the ability to

  • translate vehicle demand into part level demand through the product life cycle
  • gain up-to-date information from suppliers at the part level to identify capacity gaps
  • enhance collaboration between supplier and buyer teams
  • use intuitive dashboard features with drill-down capability
  • see instant alerts on any supply-capacity gap to related buyers and the production planner
  • identify data quality issues.

The tool also automates the process of collecting data from multiple sources and creates specific capacity management dashboards by integrating data from various sources. It reduces waste and increases efficiency by minimizing the level of manual intervention required by all functions.

Moreover, accurate visibility into part-level demand enables the OEMs to optimize sourcing decisions. Real-time visibility into demand and capacity variance, combined with the optimization engine, enhances planning, allocation and supplier capacity utilization. In addition, by determining the implications of product mix allocation and the derived part demand, the automotive OEM is now in a position to avoid under-or over-utilized supplier capacity.

A similarly positive result was seen by a top 10 global automaker with customers in approximately 150 countries. The company faced supplier capacity visibility, availability and commitment challenges. Suppliers that had made deep cuts in capacity were recovering from the fallout of the 2008 recession and were unwilling to make capital investments to meet the uptick in vehicle demand after 2013. Three different capacity planning solutions supported three major regions, with poor synchronization between them, resulting in visibility challenges for regional capacity planners. Plus, regional capacity management tools provided an inadequate view of the demand-supply situation, especially with increasing demand variability and regional expansion, which caused an increase in shared parts.

The new capacity management solution focuses on maximizing the value of the company’s existing capacity management tools while optimizing process changes. These shifts were required to address the parts capacity management issues related to visibility, collaboration and actionable insights. A proof-of-value statement described the end state and detailed a journey map that would help align business and technology requirements as well as the timeline. The statement also articulated various business scenarios available with best-in-class capacity management solutions and contextualized them. Existing information infrastructure was leveraged and scaled for future needs using hybrid digital and mainframe technologies.

The solution was designed to help the automaker

  • gain visibility across different brands and regions
  • integrate capacity planning with other critical business functions
  • enable predictive what-if scenario analysis
  • manage capacity at a part-aggregate level as opposed to part-plant, -product or -region levels
  • provide tactical support for incident response and recovery
  • manage supplier collaboration
  • handle volume fluctuations with modified allocations through predefined workflows.

Today, the capacity management tool is heightening efficiency and productivity and generating business value by offering a holistic, integrated, insights-driven user experience. It provides full visibility into joint capacity requirements for common parts. Increased volumes enable the automaker to get better negotiated rates on the materials and services procured. Additionally, optimal cross-regional capacity utilization has lowered premium freight charges while improving the absorption of demand fluctuations has brought down tooling and investment costs.

Better visualization of long- and short-term volume variance, global part visibility, actionable insights and an enhanced what-if scenario analysis around demand f luctuations provide the automaker with significant operational advantages. In addition, access to dashboards covering capacity challenges by individual part — and alerts in the supplier ecosystem — equip capacity planners with actionable insights.

Finally, the digitized and mobile-compatible solution is based on a scalable network, enabling simple future business developments, while the need for reduced computing power leads to lower hardware requirements and significant cost savings. This, in turn, makes it possible for the company to optimize its supplier capacity management and compete successfully in the evermore challenging automotive market.

Jitendra Kandpal is a domain consultant with Tata Consultancy Service. He has 11 years of industry experience in plant floor management, lean manufacturing and supply chain management, with a focus on process improvement and cost reduction. Kandpal may be contacted at jitendra. kandpal@tcs.com.

Ashish Kumar is a domain consultant with Tata Consultancy Service. He has 17 years of experience in supply chain consulting and application development and maintenance in the automotive, manufacturing, pharmaceutical, airline and logistics industries. He may be contacted at ashish.ku@tcs.com.

Sharad Jadhav is a managing consultant at Tata Consultancy Service with 18 years of experience in business and information technology strategy consulting for the automotive, manufacturing, retail, aerospace and logistics industries across North America, Europe and Australia. He may be contacted at sharad.jadhav@tcs.com.

The authors would like to recognize Deepak Mavatoor, managing partner, Tata Consultancy Services, and Anandh Rajappa, head of supply chain consulting, Tata Consultancy Services, for their contributions to this article.

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