Inventory optimization for a process industry

nVentic helped a manufacturer identify double-digit improvements. We also quantified the impact of existing constraints through process industry inventory optimization.
Context
Our client was a manufacturer of high-quality material solutions in a very competitive market. To deal with their competitive landscape, they had successfully driven down production unit costs. However, their production schedule was constantly running at or near capacity. This created a surplus of inventory. Challenges with service level had only recently been addressed to bring them close to target.
Production and supply planning were done manually. Important ERP fields such as lead time and Minimum Order Quantity (MOQ) were left blank. This was a barrier to automation. Because the most important raw materials were chemicals, the system used backflushing to record raw material consumption based on the bill of materials. This is a classic challenge in process industry inventory optimization.
Approach
nVentic’s standard Inventory Evaluation faced a number of challenges due to the data. Backflushing was causing significant negative balances in WIP but as the overall value was low, we decided to exclude it. We worked with local planning teams to derive good, conservative estimates for lead times as well as the MOQ’s actually in use, which were very significant. These data inputs were collated with other master data and all transactional data directly from the ERP. Service levels were set very high to account for late delivery penalties agreed with customers.
Because the MOQ’s were very high compared to average demand, nVentic also carried out sensitivity analysis to quantify the impact of those MOQ’s on inventory levels precisely. This then allowed our client to reassess their production constraints in light of the impact on working capital.
Results
nVentic’s Evaluation, even with the full constraints applied, identified a reduction potential of over 20% while improving service levels fully to their target levels. Moreover, our sensitivity analysis demonstrated that the MOQ’s applied by Production to maximise their output KPI’s more than doubled the overall inventory requirements. Reducing batch sizes allowed more products to be manufactured each week. This reduced inventory requirements while improving responsiveness to changes in demand.
Our work in master data also allowed bulk uploads of validated critical data to the ERP, which allowed the client to move towards a digital transformation in planning technology
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