Complexity reduction of materials and parts
>1,000 parts identified for elimination
Complexity reduction of materials and parts
Key use cases for procurement
An automotive company identified a 10-25% savings potential from using machine learning (ML) and natural language processing (NLP) techniques
Case example 2 / 9
An automotive company stored its procurement spend data in multiple systems and across several legal entities, which created challenges when it attempted to merge data for analysis. The company automatically combined and harmonized spend data and used machine learning to extract and cleanse relevant product data for complex standard parts. It detected data clusters in the overall spend data. Based on these clusters, it could derive overall procurement spending categories.
Furthermore, natural language processing allowed the company to extract technical requirements from documents to compare matching part prices and enrich the categorization process.
The analytics enabled the company to identify 10 to 25 percent savings potential for overall spending by, for instance, streamlining double-spending by different legal entities.
A truck producer used machine learning to compare similar parts to reduce number of part numbers and thus identified savings of more than EUR 20 million
Case example 1 / 9
The OEM had a high number of variants that resulted in a “long-tail” spend profile, as well as complaints from tier 1 suppliers. It conducted a part similarity analysis using machine learning to reduce the number of parts that did not deliver additional value but drove complexity.
This resulted in a significant reduction of the overall part numbers for all commodities and suppliers, which reduced complexity. The analysis earmarked over 1,000 parts for potential elimination. This led to a savings potential of more than EUR 20 million.
>1,000
parts identified for potential elimination
Category analytics for spend data
Category analytics for spend data
10–25% savings for complex standard parts
Software should costing
Software should costing
-10–30% software purchasing costs
Spend intelligence
Spend intelligence
20% savings through price optimization
Automated RFP processes to reduce MRO spend
Automated RFP processes to reduce MRO spend
11% savings in MRO spend
eAuctions for suppliers
eAuctions for suppliers
20% savings for consumables
Intelligent process mining for process optimization
Intelligent process mining for process optimization
10–15% savings for optimized processes
Optimized sourcing of package delivery services
Optimized sourcing of package delivery services
20% savings in package delivery
Automated benchmarking of service supplier quotes
Automated benchmarking of service supplier quotes
25% price reduction
An automotive OEM cut its software purchasing costs via AI-enabled benchmarking
Case example 3 / 9
Procurement departments often see software as a black box, making it difficult to evaluate optimal purchasing costs and the actual competitiveness of vendor offers. To identify the cost of software accurately, one automotive OEM conducted artificial intelligence (AI)-enabled benchmarking. It first broke down large software applications into blocks of manageable complexity and compared them to a set of reference projects.
As the basis for comparison, it used key complexity drivers like lines of code, number of tests, number of variants, safety requirements, the code type, and so on. This comparison led to an estimate of the software’s “should costs.”
This bottom-up effort supported better negotiations and enabled cost reductions of 10 to 30 percent while de-risking the development process.
A global aerospace company introduced spend intelligence for C-parts to identify savings potential of 20%
Case example 4 / 9
A global aerospace company addressed the challenge of reducing complexity and increasing harmonization in low cost, high volume commodity C-parts like fasteners and bearings. Theses parts comprised a total spend of around EUR 45 million. The company implemented spend intelligence, which includes analytics for linear performance pricing and the analysis of pricing variances. Spend intelligence enabled the company to identify actionable opportunities on price optimization automatically.
The successful implementation led to a combined savings of 20 percent for the aerospace company.
20%
savings through price optimization of C-parts
An automotive supplier generated 11% savings in overall maintenance, repair, and operations (MRO) spend
Case example 5 / 9
The automotive supplier, which spends more than USD 90 million a year with MRO suppliers across global locations, lacked sourcing standardization. It wanted to consolidate its spend among fewer suppliers and standardize its processes.
By introducing a tool to automate its request for proposal (RFP) process, it could approach many suppliers in a short period of time with a standardized process. The tool covers over 7,500 stock keeping units, addressing a highly fragmented supplier landscape with more than 200 global suppliers, accounting for 40 percent of the company’s MRO spend. Suppliers all over the world could now react immediately to RFPs and make offers.
An effective visualization tool helped it identify the best offers based on a bidder's activity, quick bid aggregation and quality analysis without manual invention, thus immediately highlighting the best offers. The tool has enabled savings in total MRO spend of 11 percent.
A North American manufacturer realized 15–20% savings for consumables via e-auctions
Case example 6 / 9
Manufacturing electrical components and products for commercial/end-consumers use, the company spends over USD 60 million for consumables in 14 categories. It experiences long lead times (6 months and above) when changing suppliers for consumables due to certification and testing requirements.
To tap the full potential of its certified supplier base, the company implemented an e-auction tool in four weeks that covers 10 out of the 14 categories. The tool enables the manufacturer to run fact-based negotiations with existing suppliers.
The tool enabled the manufacturer to realize cost savings in each category between 15 to 20 percent though cheaper offers from certified suppliers.
>15%
cost savings for consumables
A North American logistics company analyzed its outbound logistics spend to save 20% on package delivery costs
Case example 8 / 9
The company spends about 2.5 Million USD in outbound package delivery. To identify savings, it used an optimization algorithm to search for potential savings in various areas. It sought the lowest price option for the same service-zone-weight combination and compared different transport options like air versus ground shipments, while respecting boundaries such as criticality and on-time delivery.
The analyses enabled the company to capture a total of 20 percent in savings by, for example, reducing spend on surcharges for non-critical packages or bundling packages within a week to ship fewer heavier packages. The company launched the algorithm in about 3 weeks.
20%
savings in package delivery
An automotive OEM automatically generated a large-scale data cube of supplier cost information to gain powerful negotiation arguments, resulting in savings of over 25%
Case example 7 / 9
Scattered across more than 1,000 Excel files, the automotive OEM had several million data points on suppliers that covered various regions, vehicles, commodities, and parts. It introduced a tool that automatically consolidated 1,600 supplier cost break downs, enabling extensive cost parameter benchmarking for instance on labor rates, mark-ups, and development costs. For negotiations with suppliers, the tool automatically prepared fact packs supporting market-based arguments.
These led to an over 25 percent price reduction on engineering, design, and testing costs. The tool identified 9 percentage points of additional potential and a reduction potential of more than 20 percent on machine rates for selected suppliers. Additionally, it identified over 40 percent in mark-up reductions (over USD 20 million in SG&A and profits) for suppliers with excessive mark-ups.
>25%
price reduction on engineering, design, and testing costs
10-25%
savings on complex standard parts
10–30%
reduction of software purchasing costs
11%
savings in the maintenance, repair and operation spend category
A distributor of industrial goods reduced its costs and cycle times via an automated analysis of internal process variances
Case example 9 / 9
The company found the manual analysis of internal processes (e.g., the order-to-invoice process) for exceptions difficult due to large and rapidly increasing transaction volumes and data volumes (e.g., sales orders, orders across sales offices/channels, or customer data). The distributor of industrial goods and supplies therefore implemented an automated process mining approach to analyze transaction events to understand the root causes of process variations. This approach included the automated creation of process monitoring dashboards and an updated list of insight-based priority processes.
The implementation of intelligent process mining enabled the distributor to lower its process costs by 10 to 15 percent and reduce cycle times by 20 to 30 percent via optimized processes.
10–15%
cost savings for optimized processes
20–30%
reduction of cycle time