Operational Excellence and Inventory Optimization are now key components of every major med device manufacturer’s long-term strategy.
One of the main focus areas for limiting waste and practicing lean operations is through effective inventory placement at the hospital locations.
Solving consignment inventory questions requires multiple steps.
Managing consignment is one thing.
Knowing how much you should be managing is another thing entirely.
To get the right answers, you need the right data.
In order to get the right data, you need the right tool.
If the tool is not easy to use, it won’t work.
If the tool is not ERP agnostic, it won’t work.
If the tool is a CRM that has been “customized”, it won’t work.
If the tool relies too much on physical equipment (like RFID), it won’t work.
The right tool: works, and gives you the right data.
Once your sales and ops teams have the ability to properly monitor and control your consignment inventory while collecting the important data, you can then use that data to create modern consignment models using advanced analytics.
How much inventory do I consign at a hospital?
In a previous article I described demand-based asset optimization and how to optimize inventory at medical device field offices; however, most medical device companies have a significant amount of inventory consigned at hospital locations. It is often a top priority of clients to also reduce this type of inventory in the field. Using the combined strength of actionable data collected and statistical insight, the Movemedical platform enables advanced analytics that provide a robust solution to this problem. Typically, demand signals are not available for this type of inventory, so instead, we utilize consumption patterns at hospitals to compute optimal inventory levels. Similar to our Demand-based Asset Optimization Model, our consignment model relies on data-based systematic adjustments – not the behavioral modifications that traditional models require.
The Data Collected
The Movemedical platform tracks every single item individually. Even items with identical lot numbers are serialized internally, and every inventory interaction is recorded in detail. This allows us to create incredible insights and analytics.
For this consignment model, we collect historical implant consumption data at the SKU level by location in a particular region. We collect the consumption at a daily and weekly level in addition to the volatility associated with the consumption.
There are two major challenges OEMs face when modeling consignment data. The first challenge is determining accuracy of inventory physically at the site. The second challenge is organizing the data in a manner that is clinically relevant to the sales reps and physicians whom this modeling directly impacts.
Movemedical solves the accuracy challenge by giving field operations teams a simple and effective way to manage their assets. This leverages Movemedical’s automation, atomic stock, and premier user experience.
The second challenge is solved using data structures such as kitting, bundling, and non-stockable kits to organize the loose piece inventory commonly found in customer accounts. By organizing the data in this surgically-relevant method, we drive adoption of consumption-focused analytics that bridge the gap between corporate analysts and field operators.
We use two different approaches to consignment modeling, each with different levels of complexity based on our client maturity. Both options deliver significant reductions in parked assets by leveraging data to drive decision making.
The simplest method leverages asset turn thresholds and time-period (daily/weekly/monthly) max consumption at the SKU level. This method is a great starter for consignment modeling because it requires less change management and is easily understood by field operators. It is an extremely effective model for driving critical conversations and change among field users who are not relying heavily on data analytics to manage their operation.
The next level method is a statistics-driven approach. This works for analytically advanced enterprises focused on achieving more aggressive reduction targets. Similar to the Demand model, we leverage average consumption and volatility to statistically model the service-level probabilities. These models lend to more aggressive reductions while minimizing the impact to incremental loaner support. This model also enables flexibility to drive different service levels based on account segmentations. This allows you to more strategically drive your business based on factors of growth, revenue, profitability, geographic location, etc.
Figure 1: Implants grouped by Kit showing optimized stock level (blue bar) and total quantity in stock (gray bar) for one hospital.]
The figure above shows the two different models run for one specific hospital. The optimized inventory level for implants during the observation period showed a staggering reduction of more than 80%, which for this location was $220k of inventory savings at cost, and that was just one for one location in a much broader market!
Let’s start with the daily max model which is the bottom chart. The first four blue lines (first kit) shows the optimal quantity by SKU the consignment model recommends. If we compare the 12 items for the first kit the model suggest, against the 27 items currently in stock (represented by the gray bar), we can reduce the consigned inventory by this location by more than half. Going one step further to the stats model (top chart) recommends 8 pieces, which is a 70% inventory reduction. In some instances on the chart, the optimal quantity (blue bar) exceeds in stock quantity (gray bar) which illustrates that we need more of these items than currently in stock. The last kit on the chart shows no recommended inventory because we have seen little to no usage on these items, so the model suggests this inventory should not be consigned at the hospital.
This sort of analysis goes hand-in-hand with Days On Hand calculation, which is a native feature of the Move medical platform. The consignment model and Days On Hand calculations are the winning combination in managing consigned inventory levels in the field.
One of the challenges of the consignment model is that consigned instruments used typically do not have consumption, so they are excluded from this particular analysis. As our clients become more advanced users we encourage the intraoperative scanning of instrument trays which enables Move to capture usage on those particular trays. In turn, we can use this data to model the consignment of instrument trays to identify where consignment sets make sense, and where we might achieve greater efficiencies by moving those to SISO (ship in ship out) locations. This instrument tracking will also enable the profitability analysis of cases as instrument set sterilization costs can often be a huge variable expense to hospitals for joint replacements.
Data is awesome. The right data can change your life. Well, at least your business…
If using next-level analytics to power you operational excellence efforts makes sense, reach out:
firstname.lastname@example.org / call 877.469.3992
or watch this VIDEO (executive case study)