For automotive suppliers and original equipment manufacturers (OEMs), the major concern continues to be reducing costs to maintain competitiveness; however, suppliers struggle to meet OEM requests for price reductions as their own costs rise. Aspects contributing to these increased costs include tariffs on imported raw materials and finished goods, rising raw materials and energy costs, etc. Given the presence of multiple ERP systems in the industry, procurement departments struggle with collecting accurate spend information and determining strategies to optimize their purchasing processes and achieve economies of scale.
Procurement analytics for automotive manufacturers offers a means of addressing these issues. In addition to analysing past purchasing history and applying analytical techniques to identify price gaps and model what parts should cost, assess vendor performance and measure the general health of the supply base, procurement analytics can help identify areas where buying practices can be optimized.
As automotive manufacturers look to improve their purchasing practices through the use of analytics, they can expect to achieve substantial cost savings and enhance operational efficiencies within their purchasing function.
Where procurement analytics creates value for automotive manufacturers
Trends driving the growth of procurement analytics for automotive
There are numerous trends in the automotive industry that have contributed to an increasingly high level of focus on using data to make purchasing decisions. These trends include:
Annual price reductions. Each year, OEMs continue to require their vendors to lower prices while their suppliers face increasing input costs.
Tariffs and trade policy. Tariffs imposed on parts, steel, aluminum, etc. affect landed costs and require rapid response.
Multiple ERP systems. The existence of multiple ERP systems creates a challenge for combining purchasing data from various platforms to provide a comprehensive view of spend.
Part proliferation. Automotive OEMs manage tens of thousands of part numbers, many of them near-duplicates, which makes price comparison hard.
Vulnerability of suppliers. Suppliers with low profit margins and fluctuating volumes are vulnerable to supplying poor-quality product, experiencing delays in shipping or failing entirely.
Shift to electric vehicles. Battery cells, power electronics and electric motors are examples of new commodities sourced by automotive OEMs; these new commodities introduce additional suppliers and cost drivers while reducing the amount of pricing history.
Each trend exemplifies the mounting pressure on procurement to provide results as soon as possible. The increasing complexity of global supply networks poses new challenges for automotive OEMs. Procurement can play a key role in meeting these challenges by using data-based approaches to determine potential cost-saving opportunities and improve operational efficiencies in the supply network. These pressures are just some of the broader supply chain challenges in automotive industry operators face today.
What is included in procurement analytics
The primary capabilities used to support strategic procurement initiatives comprise procurement analytics. Some of the main capabilities are shown in the table below:
| Capability | What it covers |
|---|---|
| Spend analysis | Provides visibility into total spending by category, supplier, plant and part number |
| Price variance analysis | Compares prices paid for similar goods and services |
| Should-cost modeling | Estimates what a good or service should cost based on what it is made of and how it is produced |
| Supplier performance analysis | Measures the performance of existing suppliers based on metrics like on-time-in-full (OTIF), lead time and defective-product rate |
| Risk assessment | Determines whether a supplier is viable by examining indicators including financial condition, geographic location and concentration |
| Commodity pricing index analysis | Examines price changes of commodity indexes (such as steel, aluminum, copper and resin) |
| Tariff and landed cost analysis | Assesses the price effects of tariffs and logistics expenses |
| Purchasing synergy identification | Identifies opportunities to consolidate purchasing activity among various locations or entities |
| Control towers | Displays all pertinent procurement information in one dashboard, enabling users to monitor spend, supplier performance, open orders and risk |
Creating a data foundation
All use cases mentioned above rely upon similar data elements. Examples of common data elements include:
- Purchase order history, including details on quantity purchased, unit price paid and total dollars spent
- Receiving history, including details regarding quantities received compared to those ordered
- Invoicing history, including details on vendors’ invoice amounts, tax charges, etc.
- Harmonized supplier master, so that a vendor with multiple codes is counted only once
- Parts master file information, typically including technical specifications for all parts
- Bill of materials (BOM), detailing the raw materials included within each part or assembly
- Taxonomy of categories, organizing all parts into logical categories
- Contract agreements and terms, documenting all contracts and purchase agreements entered into with each vendor
- Metrics relating to quality and delivery, obtained from internal quality and receiving systems
Most procurement analytics projects start with spend analysis. While spend analysis may appear straightforward, it often includes numerous sources of purchasing data (e.g., multiple ERP systems). Furthermore, the data is unstandardized and therefore difficult to compare across platforms. Once spend data is accurate, opportunities can be prioritized. Next comes price variance analysis, which identifies potential immediate cost-saving opportunities. Subsequent rounds of negotiations will include guidance provided through should-cost modelling. Lastly, both supplier performance and risk assessment follow from previous analyses.
Spend analysis for an industrial manufacturer running multiple ERP systems represents a typical case: supplier and material records had to be matched across those systems before the spend picture could be trusted.
Core procurement analytics use cases in automotive
- Spend visibility and classification
This use case produces a factual representation of total spend categorized by product type, vendor, plant and part number. Automatic classification assigns each transaction to a category regardless of the description contained in the purchase documents. With a clear picture of spend in place, the largest areas for potential cost-reducing activity quickly become obvious (i.e., fragmentation among categories, too many suppliers sourcing equivalent commodities and excessive spend with suppliers whose last evaluation occurred months ago).
- Price variance and benchmarking
While identical parts (or nearly) are often bought at different costs by multiple facilities, the Price Variance/Benchmarking Comparison Tool looks at the price per unit for each facility, vendor, region and time period after considering volume and specifications. The results can be used as guides for all future purchasing.
- Should-cost analysis
Should-cost models predict what a part will cost in terms of the way it is manufactured and materials used based upon factors including but not limited to, hourly wage rates, overhead rates, manufacturing processes and composition of materials. This enables the buyer to have some objective criteria to compare quotations received from vendors. Should-cost models also act as a predictor in that when a quoted price from a supplier is significantly lower than a sustainable price level, this represents an early warning signal requiring careful review.
- Supplier performance and risk
Scorecards used to monitor supplier performance capture metrics relative to delivery timeliness (on-time-in-full), quality and responsiveness for each individual supplier. Assessments add financial condition, location and concentration-based factors to the evaluation criteria and associate each vendor with the components and vehicle programs they support. Consequently, category managers have a visual means of determining which vendors require attention before a delay in shipping occurs. Scorecards and risk scoring are covered in more detail in applying advanced analytics to monitor your suppliers within procurement and supplier risk analytics in the automotive industry.
- Pricing associated with commodity indexes
Automotive contracts often include language that links a specific commodity index (aluminum, steel, copper, or resin) as an element for determining price changes in commodities. Analysis tools link each commodity to its respective materials composition and index which allows buyers to evaluate the amount of real price change occurring due to market factors.
- Tariff and landed cost scenarios
Global trade policies change rapidly, thus making it necessary for buyers to respond promptly to resultant questions concerning landed cost implications. Landed cost models combine unit prices paid for imported goods and services, duties, logistics expenses and inventory holding expense to enable buyers to simulate alternative sourcing strategies under changing trade policy regimes.
- Purchasing synergy
Business units operating multiple locations or entities having experienced mergers and acquisitions frequently source the same items independently. Comparative studies analyze buying practices across business units to identify opportunities for consolidating volume and standardizing pricing.
One example of purchasing synergy was when two large auto parts groups engaged Kepler Advanced Analytics to conduct comparative purchasing studies across their respective organizations. The study covered eleven categories and 121 subcategories of goods and services. The study identified $56 million in savings from just the top ten suppliers alone.
- Procurement control towers
Control towers integrate spend, supplier performance, open orders and risk into one graphically displayed view, available daily to category managers.
A commercial truck manufacturing company located in North America hired Kepler Advanced Analytics to develop a procurement control tower integrating data from suppliers, purchasing and commodity teams into one viewable dashboard. Commodity managers’ preparation time decreased by approximately 80 percent.
The eight use cases described above illustrate the core applications of procurement analytics for the automotive industry.
Below is a summary table listing these use cases:
| Use case | Data needed | Primary value lever |
|---|---|---|
| Spend visibility and classification | POs, invoices, vendor master, category taxonomy | Identify fragmentation in spend and targets for consolidation |
| Price variance and benchmarking | Unit prices by part, plant, vendor and date | Eliminate price gaps among plants and suppliers |
| Should-cost analysis | Part specs, material content, processes, labor, overhead and margins | Base negotiations on facts, not subjective opinion |
| Supplier performance and risk | Receipts, quality data, financial and external signals, BOMs | Reduce disruptions through earlier intervention |
| Commodity and index pricing | Material content, contract index clauses, market indices | Fair price adjustments up and down |
| Tariff and landed cost scenarios | Part prices, origin, freight, duty rates | Faster, better re-sourcing decisions |
| Purchasing synergies | Group-wide spend and part data | Savings achieved through combined volume and aligned pricing |
| Procurement control tower | All of the above, with regular refresh | Daily view for category managers |
Connecting procurement to demand planning and supply chain planning
When procurement uses analytics, it works best when connected to other elements of the supply network. Volume commitments and supplier capacity reservations depend on how much will be needed, and lead times determine requirements for inventory management.
Better automotive demand forecasting using data analytics gives procurement a stronger foundation to make those commitments and feed back lead times into inventory and production planning processes, including automotive inventory optimization.
KPIs that matter to automotive procurement
Three essential focus areas exist for KPIs in automotive procurement: cost, supplier performance and risk, and realized savings; however, monitoring only savings misses half the story.
| KPI | What it shows | Watch-out |
|---|---|---|
| Spend under management | Proportion of spend managed by the procurement organization | Low coverage masks savings opportunities |
| Spend classified | Proportion of spend classified by product type | Poor classification undermines every other form of analysis |
| Price variance | Gap between prices paid for comparable goods and services | Adjustment for volume, specification and terms |
| Identified savings vs realized savings | Difference between savings identified during analysis vs those ultimately captured in financial statements | Significant disparities highlight poor tracking or execution issues |
| Contract compliance | Portion of spend on agreed-to contracts and prices | Off-contract purchases dilute negotiated savings |
| Supplier OTIF | Percentage of deliveries met on time in full (OTIF) | Measure against confirmed and requested dates |
| PPM defect rate | Number of defects per million parts delivered | Track trends, not only thresholds |
| Single-source share | Percentage of critical spend concentrated with one supplier | High concentration increases risk |
| Procurement lead time | Time elapsed between requirement and receipt | Shorter, steadier lead times reduce inventory needs |
From insight to negotiation
Analysis only yields tangible returns when it affects how you manage your suppliers. In automotive, this typically occurs in four distinct areas:
- Annual price negotiations, where fact-based agendas are established using should-cost models and/or price variance analysis
- New program sourcing decisions, where landed costs and risk assessments guide supplier selection
- Supplier development initiatives, where objective performance data supports joint improvement plans
- Executive reviews, where single views replace conflicting spreadsheets regarding spend, savings and risk
Additionally, establishing a shared fact base enhances relationships between buyers and suppliers. Instead of discussing opinions about price based upon subjective judgment regarding what is fair, dialogue moves to evidence-based discussions concerning cost.
Common pitfalls
- Neglecting the data foundation. Developing analytics built upon unmatched vendors and part numbers that are not consistent results in no trust being placed in the produced output.
- Prioritizing savings without monitoring their realization. Failure to capture identified savings in the financial records damages procurement’s credibility.
- Relying upon vendor quotes alone. Establishing fact-based quotation evaluation standards requires should-cost or benchmarking analysis to support quote testing.
- Isolating cost from risk issues. Even though lower cost may be the most attractive option to consider, the cheapest supplier can become the most expensive if it fails.
- Creating standalone reports. Procurement analytics needs ongoing refreshes to remain useful.
- Failing to engage category managers throughout process design. Tools developed without their input are often unused.
Getting started with procurement analytics
A practical approach might resemble the following:
- Define scope. Begin with one high-spending direct material category or a group of plants.
- Establish the data foundation. Extract, clean and match POs, invoices, supplier and part data across ERPs.
- Create spend visibility. Classify spend by categories in spend cube format and create spend cubes.
- Identify price gaps. Run unit price comparison analysis across plants, suppliers, regions and dates.
- Implement should-cost analysis. Develop cost models for the highest-spending components and identify the greatest opportunities for cost reduction.
- Develop supplier performance and risk assessments. Establish scorecards and associate each vendor with specific components and programs.
- Include embedded processes. Incorporate outputs into future negotiations, supplier reviews, new programs, etc.
How Kepler Advanced Analytics can help
Kepler Advanced Analytics provides procurement analytics for automotive manufacturers, working with vehicle OEMs and tier-1 component suppliers on their existing ERP, purchasing and quality data. The team combines automotive procurement expertise with analytics engineering and an offshore delivery model, as part of Kepler’s wider automotive data analytics services and procurement analytics practice.
Typical support includes:
- Direct Material Spend Diagnostic: a fixed-fee review that shows where spend, price variance and supplier concentration sit
- Multi-ERP spend analytics with harmonized supplier and part data
- Should-cost and direct-material cost modeling at part-number level
- Purchasing synergy studies for groups with multiple plants, business units or acquisitions
- Supplier performance and risk analytics linked to parts and vehicle programs
- Procurement control towers that give category managers one daily view
- Tariff and landed cost scenario modeling for sourcing decisions