• CLIENT: One of the largest copper mines in the world

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• RESULT: We generated $ 20 million in savings in 12 weeks in strategic and transactional spend categories

• CHALLENGES:

1. China’s decrease in its growth rate triggered a sharp reduction in copper prices

2. ​Rigid long run contracts with suppliers, equipment leasers, and financial institutions

3. Strong syndicates with restrictive legislation

4. In the mining industry, negotiation processes are traditionally long

• We tackled over USD $150 million in spend: Including 2 mining sites, a smelter and supply chain / administrative offices

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• Overall savings of 13% realized in 12 only weeks

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• Savings of 19% in strategic categories and 7% in transactional categories

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• Spend Areas Included:

1. Exploration / Geology

2. Smelting

3. Processing Plant

4. Inbound and Outbound Trucking

5. Support Services

• We Identified and Negotiated with:

1. Local Suppliers (in-country)

2. Regional Suppliers (on a continent-wide basis)

3. Global Suppliers in the case of reagents (chemicals)

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• All categories were implemented including adjustments to processes to ensure long term sustainability

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• Formal training for key users including the purchasing department

• Cognitive Supply Chain Optimization

• Working Capital

• Clean Sheet Budgeting: OPEX

• Organizational Efficiency

Cognitive Tail Spend Sourcing

• Fresh liquidity started to be generated in only 4 weeks

• All relevant suppliers were covered within 10 weeks

• $ 5.2 million in savings generated

COGNITIVE SUPPLY CHAIN MANAGEMENT

• Cognitive Supply Chain Optimization

Cognitive Tail Spend Sourcing generates savings between 7% - 12% with the first savings captured in 4-6 weeks; typically we’re able to negotiate with all suppliers in an 8-12-week timeframe

Data-Driven Supplier Categorization Method (DSCM):

• K-Means Subspace Clustering Algorithm: By categorizing suppliers on behavior rather than classification, we optimize the negotiation process and maximize the fresh cash generation. Suppliers’ Business Behaviors:

1. Set of Items Provided in Each Time Period

2. The quantity of each item in each order

3. The Orders Frequency

4. Product Quality

5. Product Arrival Time

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• All the clustering is concatenated with a Financial Optimization Model, thus determining the supplier groups that maximize the generation of fresh cash and the ease of negotiation.

Contract and Invoices Optimization Intelligence:

• We Maximize the Price Reduction by:

1. Creating a Data Corpus from all the invoices and contracts from any source (PDF, email, image, physical)

2. Detecting Anomalies in all the contracts and invoices

3. Optimizing the Negotiation Process relative to the negotiation expense for each supplier category