Demand for stock is driven by E&P operations and associated upgrade projects. The complexity and scale of these projects, often in remote global locations, places a burden on supply-chain managers to ensure effective processes are put in place to deliver the right goods to the right place at the right time and in the most efficient and cost-effective manner.

Developing a program of stock optimization that can be easily implemented and incorporated into daily operations without burdening stock controllers with cumbersome reports or calculations is key.

To reach this stage, a business must address three main areas using data from its enterprise resource planning (ERP) or supply-chain system to validate decision-making in stock performance and stock segmentation, including the stock policy and stock optimization.

Stock performance

How much stock does the business have? How quickly does it move? How long is its operational or shelf life? When was it last sent out? How much was lost?

Identifying how to measure the performance of the warehouse to objectively monitor stock movement is vital. Visually examining levels of current stock, usage, and demand using clear graphs and timelines can highlight problem areas and allow the effects of improvement measures to be evaluated easily.

Stock segmentation

With warehouse performance measurements in place, an efficient means of segmenting or classifying stock in the warehouse needs to be defined.

The purpose of stock segmentation is to enable stock controllers to quickly analyze any increase or decrease in demand for equipment. This will support decisions to invest in more kit or to sell it on to the market before the technology is superseded.

Segmentation focuses on demand value because it captures slow- and fast-moving items as well as low- and high-value materials. Demand value is a good basis for an ABC(D) classification, offering an objective method to segment stock in the warehouse.

The ABC(D) classification simply sorts historic and planned demand value from lowest to highest. In the resulting list the highest ranked materials will be classed as A and the lowest as C, and the materials with no or negative demand are classed as D. Fast-moving and high-value materials (A) are the top 80% of the demand value and reflect about 5% of the materials turning over in a warehouse. Slow-moving (C) or dead (D) materials are the bottom 5% of the demand value and reflect about 80% of the materials in the warehouse.

This classic ABC(D) classification approach is integrated in SAP ERP and is well suited to consumables used in maintenance-related work; however, it may not prove to be an accurate measure in other situations, for example critical spares. These do not have a regular consumption value, meaning they could be classed as less important although they are vital and need to remain on the shelf.

Indeed, the mathematical basis for applying ABC(D) classification based on the bell curve does not apply to a lot of spares in maintenance-based industry sectors. Maintenance-oriented organizations must include aspects on availability of spares in the market and lead times, especially for critical spares, which also need to be marked as important. To guarantee uninterrupted production and safe operations, the business must identify and define what those critical spares are. It also has to identify lead times of materials, keep them accurate, and put in place procedures that ensure the stock controller prioritizes its availability. There are two key examples:

  • Materials that are hard to acquire or have long lead times need to be promoted up. For instance, a C material with a lead time greater than 12 months should be promoted to a B; and
  • For equipment that is marked as safety-critical by maintenance, spares should be promoted up as well.

Stock policy

Using the stock segmentation discussed above, an objective stock policy can be defined for stock controllers taking into account usage history, planned demand, lead times, and spare part criticality. A crucial parameter – the service level – has to be introduced. The service level describes how often demand needs to be met with available stock – that is, when stock is picked from the shelf, the probability of not running out of stock is described.

A service level is assigned to each category. For A materials, 99.9% is assigned, with varying degrees for B and C materials down to D materials at 50%. The higher the service level, the less likely the chance to run into a stock-out situation. The statistical foundation of the 50% rating means, in practical terms, that this material should not be stocked.

Stock optimization

Finally, minimum and maximum stock levels can be proposed according to consumption history and planned demand.

It is important to understand the underlying statistical method that governs this. Most methods for proposing minimum/maximum stock levels in ERP packages are based on a bell curve distribution around an average consumption, but this does not apply to a lot of spares. Therefore, visualizing consumption patterns along the investigated timeline of material is key before deciding which method should be used.

Using software for these calculations saves time and mitigates human error. With reports and graphs in place stock controllers can plot the charts, objectively review current and proposed warehouse stock levels, and quickly take action when a material demand increases or decreases.

In summary, stock optimization should not be treated as a one-off activity but instead as an iterative practice. Due to ever-changing technology and a constant cycle of projects, equipment is being replaced all of the time, and underlying spare parts and consumables have to be frequently reviewed.

The impact of stocking policies on other areas of the business, such as maintenance and procurement, also needs to be monitored. The goal is to cut stock levels, but if stock levels reduce too much, this can result in a negative effect on maintenance performance such as operational downtime. To ensure balance, management requires a view of key performance indicators across the business, which can be provided by standard reporting tools.

By taking care to review the data and implement the three areas of stock performance, segmentation, and optimization, the warehouse will be an efficient service partner for the rest of the business.