Overview

Lubricant Rack Management

Lubricant rack supply chain management is based on a complex supply chain with multiple distributors, carriers, and re-packagers, driving unique challenges and inefficiencies.

Lubricants are expensive, but necessary parts of the energy industry, being the oils and greases that reduce friction and protect machine parts. The lubricant sector is highly significant for the growth and margin of energy conglomerates and the industry as a whole. Being based on a complex supply chain simply adds to their overall cost, with one of the top global suppliers of lubricants stated that they were spending close to 82% of their total outlay on the supply chain.

Context & Challenges

The lubricant supply chain acts like a mix-and-pack consumer products supply chain, with multiple third parties and intermediaries included in packaging and redistribution.

At the distribution point of the supply chain, contaminants can enter the product. Many distributors transport multiple types of lubricant at once, some of which can be incompatible with each other such as  automotive engine oils and industrial turbine oils. Detergents automotive oil damage turbine oils, as residue left in lines and hoses on the truck can entirely ruin a tank of lube oil.

Improper storage of lubricants can wreak havoc on the product's efficiency and validity. Storing lubricants outside, leaving barrels uncapped, cross-contaminating products, improper labelling or nonstandard transfer containers can all degrade the oil and are more common than they should be. Additionally, storage rooms are not always conducive to a first in first out rotation due to incomplete data and lack of oversight.

Lubricant inventory management can be a unique operational burden:

  • Lubricant is packaged and re-packaged as it moves from bulk volumes down to retail volumes
  • Multiple distributors, carriers and re-packagers all have distinct IT systems which drives incomplete data, and a lack of digitization and effective communication

This complexity creates a particular visibility challenge as the lubricant exchanges hands and is further split down the chain.

The various disconnects and inefficiencies that could occur along the lubricant supply chain put it at increased risk of counterfeiting or unintentional quality breaches. Lubricant products, being consumer-facing retail items with multiple third parties along the supply chain are particularly at risk.

Providers selling lubricants can incur billions in losses due to counterfeiting, not to mention significant damage to their reputations. As counterfeiters evolve and take advantage of supply chain obscurity, the risk only increases.

Unintentional quality breaches are more likely but almost as damaging. Current ambiguity in the supply chain limits a company's ability to effectively recall goods, and compels them to "over-recall" in the hope that by doing so they will seize all affected product, which has negative financial and environmental impact.

STRATO allows for the creation of an ecosystem including an application to track the status of lubricant supply through its useful life, driving the following benefits:

  • A single ecosystem with unique memberships and features for producers, distributors, re-packagers, and brands to use
  • Mobile (phone, tablet) interfaces for remote input
  • The multi-tenant blockchain database (ecosystem setup) allows for faster and simpler development of other oil & gas operation tracking (e.g. other supplies, labor, financing)

Solution Benefits

  • Lot traceability through the supply chain

  • Traceability from bulk to re-packaging

  • Improved data for business intelligence (BI)

Any STRATO insurance solution leverages STRATO's enterprise grade features

  • RESTful APIs for direct connection of IoT devices such as storage providers' iPads to the blockchain network

  • Identity Management, OAuth and SSO capabilities for simplified IoT authorization and user login

  • Privacy via private chains to keep data private and control who sees what data

  • Enterprise Data Modeling for integration into existing data systems, and to ensure interoperability

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