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Shelf-life rules are fully embedded in the planning engine. On the demand side, the minimum remaining shelf life requested can be handled per region, plant, DC, or customer. This requested shelf life is matched to total shelf life on the supply side. This also involves multilevel inheritance. For example, the drug product date of manufacture is used to calculate the shelf life of distributed finished goods. This minimizes disposal due to products exceeding their expiry date. Service levels are assured by planning for an expected shelf life.

The characteristics-based planning and registration frameworks in OMP allow full control of regulatory constraints. You can define product flavors, along with the sequence of plants, production lines, and working cell banks involved. Combined with batch-specific characteristics, the flavors can be linked to time-phased regulatory approvals at regional or national levels. This ensures that the right product is available in the right way at the right time, even in uncertain regulatory contexts.

OMP can model complex routings and recipes, with multiple operation steps in converging, diverging, and parallel sequences, as well as BOMs with continuous linear or non-linear product release and consumption. Our industry experts are familiar with typical production methods, for example perfusion or fed batch. They provide best practices guidance in modeling, allowing reality-based planning, resulting in optimal use of capacity.

In OMP, CMOs and other suppliers are modeled as an integrated supply chain node providing full inventory visibility. Through the OMP cloud portal, suppliers can jointly work on the plan in an integrated way, managing committed supplies, planned capacity, and batch allocation, etc. In addition, it is possible to model contractually agreed minimum and maximum volumes and values over different time periods and product groups. Leading CMOs also use OMP as their SCP SOR.

There is great value in integrating clinical and commercial supply chain planning processes, because there are multiple areas where they interact, such as competing for capacity on production lines. OMP allows you to plan both streams concurrently, allowing for separate ownership but shared visibility.

Artwork versions can be modeled using time-phased bill of materials changes which can be linked to regulatory approvals at region or country level. This makes it possible to optimize the transition between artwork versions, minimizing wastage.

Potency factors are managed at batch level. In addition, an average expected potency factor can be defined for planned production. This factor is used to calculate grams of active ingredient from volume-based inventory projections. In more advanced optimizers, the factor allows the optimal blend of drug substance intermediates to be determined.

Bottleneck tanks and other storage equipment can be modeled. Their degree of occupation is linked to filling and emptying production steps within or across multiple orders. Availability can be used as either a soft or a hard constraint, with smart solvers available to optimize the tank flow schedule.

You can define rules on how and when to apply quantity tolerances, avoiding unwanted or unneeded new orders being created, ensuring plan stability and minimizing noise.

Downstream POS or serialization data can be used to identify trends and anticipate sudden increases or decreases in upstream demand. In life sciences, IQVIA is a typical data source used for this purpose. Other examples include forecasting per therapeutic area based on parameters such as patient prevalence rates, bypassing the need for historical data. The net result is a more agile supply chain, able to respond more rapidly to such forces as market dynamics, competitor stockouts, and parallel trade.

Quality lead times can be modeled along with changeover, production, and transport times. This also allows for intense collaboration with quality department planners, who can sequence and prioritize batches for release, resulting in shorter lead times in the event of urgent patient need.

Product life cycle management is fully embedded in both demand and supply planning, allowing the modeling of new product introductions, phase-in and phase-out (for example of artwork changes), and other types of events. In tracking inventory, it is possible to separate qualification batches from commercially approved batches. This all results in a quicker time to market for newly developed drugs.

In OMP, tenders can be tracked separately from base demand and forecasting. You can assign certainty levels (probabilities) to be used in simulation planning. This ensures that your supply chain is prepared for any possible outcome.

You can peg any demand and supply element in compliance with the various rules that you integrate into the model, including regulatory, quantity tolerance, and shelf-life related rules. In addition, pegging can be applied retroactively by tracing and storing the full genealogy of batches produced. This creates true end-to-end visibility over the internal and external networks, from the drug substance and its components all the way to final demand.

The what-if simulation framework in OMP allows you to make changes to master data parameters as well as demand and supply changes. This could include changes to safety stocks, shift plans, etc., allowing you to respond quickly to any possible future scenario.

Campaigning rules can be defined to group production runs with similar characteristics, on all levels from drug substances to finished goods, to reduce changeover times and allow lean manufacturing. Smart solvers are available to automatically derive the optimal campaign length and sequence, and apply fixed or dynamic rhythm wheels.

Inventory can be tracked per batch and can be tracked separately per inventory type, such as ‘available’, ‘in quality inspection’, and ‘blocked’. In addition, batch characteristics such as shelf life are used in pegging and planning. OMP delivers a fully compliant plan which takes into account all the possible batch level constraints.

OMP includes multi-echelon inventory optimization (MEIO). This involves a holistic calculation of optimal safety stocks across the entire network from the production of drug substances, drug products and finished goods to the distribution network. Global cost and value are optimized, with target service levels at end nodes. This reduces inventory levels while at the same time guaranteeing or even improving service levels.

Production or inventory volume constraints over different time periods and groups of products can be modeled similarly to contractual agreements with CMOs.

OMP offers full support for modeling multi-sourcing strategies. Using smart solvers, you can either predefine percentage splits (depending on plant targets) or automatically derive cost-optimal splits, while considering capacity and other constraints. This allows you to prepare your supply chain for future growth.

We follow the evolution of new modalities to model them into the OMP solution. Cell and gene therapy are already covered. Key capabilities include support for demand-driven planning (MTO), Available-to-Promise and Capable-to-Promise, equipment and labor modeling, and scenario planning.