Replenishment policies and data analytics as a source of competitive edge in inventory management

“Know what you own, and know why you own it.” Peter Lynch


As it was shown in a previous release, inventory management has become a complex and time consuming process. Hence, the attention paid to it varies among industries, but the ones who decide to take a closer look had been able to transform it in a source of competitive edge for their companies.

In order to give an example on how complex it has become, car manufacturers are now forced to offer a large range of vehicle models and options to the extent that a single model series of a premium German automobile brand can now reach up to 1,017 possible automobile variations!

The enormous product variety-induced complexity makes it more challenging to ensure efficient logistics, considering the aftersales service; the variety of parts and components to be kept in stock can be extremely large. The variety of parts puts immense pressure on replenishment policies around the supply chain.

As e-commerce increases its penetration into the market, it has brought the relevance for a retailer not to delay in product availability. If a customer does not find what he requires, he will simply move on to another retailer. Therefore it is expected higher service levels on a SKU level, does it really pay off for the extra inventory to be held?

ALG has been supporting clients in conciliating the ever growing pressure for product customization with the necessity to keep the number of SKU as low as possible along the supply chain. In order to contribute and to see the opportunities that companies could find when taking a proactive approach in reviewing their stock polices, some cases have been selected and discussed below.

If you are interested in warehousing, warehouse lay out and footprint designs please refer to our previous release.

In this release it will be discussed how replenishment polices and data analytics could improve company performance:

Case 4: Global inventory optimization after a major acquisition in the Oil Sector.

Case 5: Data analytics to improve warehousing performance in a vehicle storage facility for a leading European car manufacturer.

In the closing words of this article, a framework is presented to give a rough estimate up front on the savings and benefits that can be expected from such a project. This framework allows the ROI calculation of a project aimed at streamlining, warehousing and replenishment polices along the supply chain.

Case 4: Global inventory optimization after a major acquisition in the Oil Sector

A major European Oil company acquired an important Oil Company operating in the Americas. Following a merger, there was a necessity to harmonize the materials along the supply chain to create a new global chain for the new company. Most of the cost synergy and reasoning of this acquisition was powered by the expected improvement in scale. The new and more efficient supply chain had to play a key role in materializing the expected savings.  This was a massive project that involved the optimization and harmonization of the stock polices and materials across the globe. This project was undertaken by the consultants and the internal team of 24 Supply Chain Managers from 24 counties in a 12-week effort.

The objectives of this massive optimization were:

  1. Cash recovery, applied to most of the countries
  2. Sharing project surplus material,  where it were not restricted by BU depending on contract agreements (JoAs) and local governments
  3. Production inventory optimization applied when there are operated wells and JoA

From the total of 24 countries in the study there were some with special considerations:

  • Countries with restrictions. Not included in the business case
    • 1) & 2) Cash recovery & Project inventory reutilization: countries with legal / JoA restrictions
    • 3) Production inventory optimization: we have not been able to quantify target inventory for the American Company countries without movement data to calculate coverages.
  • Countries with some risk to materialize business case. Included in total business case but marked as “Risk Countries”:
    • 1) & 2) cash recovery & projects: countries with JoA Partner with some challenges to manage

The joint company recognizes that stock management has to reach a new optimal level of inventory and therefore the first massive effort was harmonization of all SKU across the two companies. This exercise was developed in waves as shown in the following figure:

Understanding a properly classifying the SKU
Understanding a properly classifying the SKU

Once the SKUs were homogenized, it was possible to merge the inventories and optimal level of inventory had to be calculated. In the oil and gas industry, there are two major reasons to carry inventory:

  • The maintenance of current infrastructure: This in turn have two components
    • Preventive maintenance
    • Corrective maintenance
  • To build new installations (projects): Those materials come from the BOM of a specific project that will be undertaken. It has to comply with the overall SKU denomination to avoid creating a new SKU, enabling the usage of leftovers in the post construction phase. It keeps the purchase complexity lower and increase the overall buying per SKU

The following model can be used to estimate the optimal stock levels to be held for the first ones:

Understanding a properly classifying the SKU
Understanding a properly classifying the SKU

The optimal level of inventory was calculated for each SKU. Then a complete audit of existing inventory was done, also per SKU, and identified as “addressable inventory” for reutilization or cash recovery. The inventory was classified by age:  old inventory (+ 2 years) and new (< 2 years) and by availability according to BU declarations (Available to be sold or reused, and Not available assigned to project or production MRO). The following figure shows the results:

Post-merger inventory audit by purpose. Advanced Logistics Group
Post-merger inventory audit by purpose

The final result of this effort was that the total inventory may be reduced by more than 50% by three main levers to materialize inventory benefits as represented by the following figure:

The Return on an Inventory optimization after a major acquisition in Oil and Gas

Case 5: Data analytics to improve warehousing performance in a vehicle storage facility for a leading European car manufacturer

Our client was the importer of vehicles to the Iberian Peninsula market. It wanted to better manage the time once the car arrives in the country until it is delivered to the dealership. The objective of this project was the creation of a forecast model to identify early problems in the warehousing performance and to establish a course of action to resolve them as early as possible. Such a model had to be embedded into a management tool that would monitor the dates in which the cars are moved along the car silo. The tool should inform more precise dates for the processes of customization and when each vehicle would be ready for delivery to the dealership. It should send alerts in case any of the various processes was not handled within the expected timeframe. Moreover it should support the identification of the main root cause leading to the observed delay or problem.

The final forecast algorithm identifies potential problems, enables action taking and calculates the timing for each of the processes that a vehicle (chassis) passes through the logistics chain. This allows:

  • Greater control over the location of a vehicle at national and international level.
  • Vehicle prioritization and planning by the dealer
  • Reduction of errors through greater control of transport and management of stock in transit
  • Reliable delivery forecast, contributing to the improvement of customer satisfaction
  • Reduction of delivery times to the customer
  • Cost reduction through the earlier sharing of information, allowing carriers to better plan the location of their fleets.
  • Improved cash flow, through the reduction in delivery and collection times

In order to understand all the processes that occur with each vehicles the entire logistics chain starting on the production facility going all the way to the delivery on the dealership was defined. The following lead times per each process phase was defined.

Demand Forecasting Unit (DFU)

The following characteristics have to be built into the model while defining the forecasting model.


The model was constructed with capabilities for early identification of future problems and enabling action taking in order to reduce lead time and improve fulfillment rates.

During the pilot implementation of the model, a substantial reduction in the delivery times was obtained and on top of that, the variance in the total lead time was reduced leading to a lower mode value on the overall delivery time. The before and after results were as follows:

  • Initial delivery times: 77 – 103 days
  • During the pilot ran the new delivery time: 70  – 90 (74 days mode)

As a cascade effect in the supply chain, the following benefits were observed after the reduction in the LT spread:

  • Increase in occupancy levels (increase 15%) of transportation resources, reduced fleet requirements.
  • Increased customer satisfaction and meeting customer promises
  • Reduced bullwhip effect

Closing words: The ROI on a typical project

After applying the suggested framework towards various projects aimed at improving the warehousing and inventory optimization across various industries, we are convinced that there are numerous opportunities for a strong return in such projects.

The following figure provides an overall view of the financial improvements and some qualitative aspects that a project aimed at optimizing the warehousing and streamlining the replenishment polices can delivers to a company.

Potential savings based on the financial results - Annualized
Potential savings based on the financial results – Annualized

In order to enable a quick estimation of the benefits that a company can expect from undertaking such optimization, the following example is presented.

Considering the following company metrics:

  • Yearly sales volume: 2.100 AED Mio
  • Gross Margin: 32.1%
  • Net income: 262.3 AED Mio
  • Inventories: 1.150 AED Mio

According to our framework for such company, the expected ROI on warehousing and stock optimization project could lead up to 260bps increase in gross margin and lead up to 6.9% increase in sales.

We believe that many companies neglect this opportunity when considering how to improve financial performance. ALG can run a warehouse and inventory audit that can quantify and qualify this opportunity upfront pinpointing the areas to be addressed attending to the specifics of a given company.

Supply chain in today’s globalized world comprises of a long chain with inventory moving through multiple levels until it reaches the final consumption point. The following factors have to be considered while designing the chain:

  • Multi-sourcing network
  • Total lead times between locations (*)
  • Lead time variability and schedules compliance
  • Forecast and forecast accuracy
  • Holding costs
  • Production and distribution lot sizes
  • Target service levels

Once all of these factors have been considered, the reorder point in each location and the safety stock in each location have to be calculated.

Upfront, it seems very complex; therefore, as it was mentioned earlier companies forego this opportunity. But, if one company decides to undertake such endeavor it will lead to companies fully recognizing the relevance of each of them and transform them into a competitive advantage.

In our view, the following key points are the most neglected ones while designing warehousing and replenishment policies:

  • Properly differentiate customer segments and accordingly define adapted stock strategies for each segment, this will lead, normally to get heavy stock levels reduction
  • Segment products and assigning different target service levels to focus on more profitable and less volatile products. Properly address the proliferation of SKU number (optimize packaging options) to reduce complexity. Define the best postponement strategy
  • Define multi-echelon stock policies to reach service levels and optimize cost by considering all the interactions in the network
  • Integrate stock policies in supply chain planning model to standardize processes, review stock policies systematically and update data
  • Take care of forecast quality to update safety stocks and stock levels properly. Update data involved in stock policies calculation to maintain accurate stock policies and levels
  • Warehouse layout, review stock location strategy, yard storage vs covered areas, palletizing methods and materials, storage solutions and rack systems, pick and order processing to align with stock management polices

About the authors
Pablo Ruiz del Real is MSc in Civil Engineering, Partner at ALG and Head of Middle East operations
Flaviano Moreira is MBA, Chemical Engineer and Manager at ALG
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