Optimizing inventory in a context of supply disruption


Our client was a global manufacturing firm with a broad range of products, both make to order and make to stock. The company was engaged on a drive to reduce inventories in order to free up working capital, but did not want to sacrifice growth or customer service.

nVentic carried out inventory evaluations to quantify and target reductions while slightly improving service levels. The baseline service level % was in the high 90s. However, there were two major externalities to manage:
  1. The client was going through a brand switch at some of its main sites. The old brand was being phased out and a new brand phased in. This needed managing very closely to minimize obsolescence of the old brand while not damaging the introduction of the new brand
  1. Severe disruptions amongst the supply base had led to shortages the previous year and safety stocks had been built
A high percentage of raw materials came from Asia and average lead times were 3 months.


nVentic’s analytics and consulting support helped the client to right size inventories in the face of significant uncertainty. Key to achieving this was ongoing sensing of demand:
  • We helped the client to decouple forecasts where appropriate. For around two thirds of items, historical demand was a poor guide to actual demand, while sales forecasts had systematic bias. Widespread use of deterministic planning meant that forecasts were being turned into fixed orders before planners could react
  • Sensitivity analysis was carried out to model different degrees of variability and the impact of specific scenarios such as supplier failure and lead time increases
  • To keep planners comfortable that supply disruption would not be a problem, we took a conservative approach to lead time variability by selecting maximum lead times for all items and updating target safety stock levels on that basis. We also helped them assess the data quality within SAP and quickly take appropriate action to update key parameters like lead times within the system
  • On a weekly basis, forecasts and orders were updated based on the latest information

At the heart of the modelling was predictive analytics. The first stage of this was done using a number of standard reports from SAP:
  • Stock reports
  • PO lists to show items on order along with expected delivery dates
  • Forecasts
nVentic created a report to predict stock levels for the coming 6 months. This predictive report was given to planning teams weekly, allowing them to layer on known externalities (i.e. all information not within SAP).

The granularity and flexibility of the data reports made it very easy for planners to foresee shortages and overstock positions, and so update plans and orders.


nVentic helped our client segment the data to target additional safety stocks only for those suppliers who genuinely represented additional risk. This alone allowed safety stocks to be greatly reduced.

Specific mitigation plans were put in place to deal with the actual supply disruption, including strategic building of inventories for some items and the sourcing of alternative suppliers.

A good deal of attention was also put on cycle stock. At the start of the project, a lot of planning was done manually and orders placed with set cadences – monthly or weekly – based primarily on planners’ bandwidth. nVentic’s analysis allowed the client to see how much benefit could come from shortening order cycles for certain items. This better smoothed demand to suppliers and helped reduce upstream variability, as well as reducing cycle stocks.

Perhaps the biggest impact was achieved by giving planners forward visibility of predicted stock levels. All of the data they required was in SAP, but previously they did not have the means to extract it and manipulate it in a timely manner.

In addition, new techniques were introduced to the planners. There had previously been an excessive reliance on a small number of MRP types in SAP. nVentic helped the planners to understand where to apply different MRP types. This facilitated greater automation for some items, giving planners more time to deal with the more difficult items.

Using the predictive analytics, the client delivered a 20% decrease in inventories in one year, while improving fill rates. Furthermore, much greater transparency was created, with planners having a much better idea what future inventory levels were likely to be. At the end of the year a further 15% improvement was identified by the planning team itself for the following year.

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