Biomanufacturing supply chain optimization

© Bioproduction Group. All Rights Reserved.

whitepaper

Biomanufacturing supply chain optimizationBalancing risk against flexibility and just-in-time production

© Bioproduction Group. All Rights Reserved. 1

BIOMANUFACTURING SUPPLY CHAIN OPTIMIZATION

iNtrODUCtiON

The biopharmaceutical supply chain presents unique challenges for supply chain planners and their supporting technologies, primarily because of the need for very high supply reliability. High costs and the life-preserving nature of biopharmaceutical products mean that planners must avoid outages and ensure supply continuity. Such imperatives make it difficult to meet the traditional goals for planners: flexible supply chains that carry minimal inventory and manufacture ‘just-in-time’ to meet demand.

In this whitepaper, we discuss ways of modeling biomanufacturing networks that explicitly account for supply chain risks. Capturing the likelihood and effect of risks is a critical first step in determining how much inventory is needed to buffer against adverse events, and therefore the level of safety stocks that are required. In some cases, supply chain resilience and flexibility are not mutually exclusive, and with careful planning, a network can be constructed that optimizes both.

Biomanufacturing supply chain optimization

Balancing risk against flexibility and just-in-time production

“My boss tells me that ‘no patient shall go without’. But what does that mean in reality? Nothing can be totally certain – we need to be able to quantify that risk.”Senior Supply Chain Planner, Large Biopharmaceutical Manufacturer

CUrreNt issUes iN the BiOpharMaCeUtiCal sUpply ChaiN

Biopharmaceutical supply chains represent a unique challenge for planners because process variability, as well as contamination and other adverse events, need to be explicitly considered in supply planning. The threat of the known unknown – risks that cannot be accurately predicted – must also be incorporated into plans. Without characterization of these issues, traditional inventory models produce results that can appear to be optimal, but actually expose the business to unacceptably high levels of risk. We outline some of the key factors that must be considered below.

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1. Cell GrOwth tiMes aND yielD VariaBility

Current cell lines are relatively slow-growing and require very tightly controlled growing conditions in terms of temperature, pH, and growth medium. Up to 30 days is required to express sufficient material for a single batch. The effect of this long growth phase is that manufacturers have focused on increasingly large batch sizes and multiple serial production trains to ensure sufficient production quantities. It also means the time required to react to adverse conditions is long.

In addition, current cell lines are extremely sensitive to growing conditions and contamination by other cells such as bacteria or viruses. Cell growth is exponential (since in a well-designed process it is only constrained by the doubling time of the cell), but this growth pattern means that small variations in growth are magnified over and over again. The result is highly variable output quantity and quality of 30% or more (Shah, 2004). This variability is also highly auto-correlated, so that many successive batches in a manufacturing campaign may be affected. This is a particular problem for the biopharmaceutical industry where a small number of batches are typically produced, and the time to react to manufacturing issues is long.

2. CONtaMiNatiON aND rejeCt rates

Biological-based manufacturing also requires exceptionally stringent levels of cleanliness since cells grow in conditions well suited for many other bacteria and viruses. Even with completely clean equipment, biological contamination can be introduced from other sources including the feed stock (media), water, operators, detection mechanisms, air handling systems, etc.

Once biological contamination is present, it can be extremely difficult to remove. Typical biological protection mechanisms such as a protective thin film formation (seen, for example, in slime) or spores (a reproductive mechanism designed for survival in unfavorable conditions) are very difficult to chemically treat and usually need to be physically removed from pipes (e.g. with an abrasive slurry).

The focus for biopharmaceutical manufacturers has therefore been to reduce bio-burden (biological contamination) as much as possible using mechanisms like HTST pasteurization of media, and to introduce purification steps that will filter out any contamination. Specific types of bio-burden will render conditions unsuitable for the fermentation process to continue; testing systems are designed to detect this and abort the batch as early as possible. Even with such systems in place, contamination events are common – as seen in the 2009 contamination event in Genzyme’s Allston facility.

3. testiNG aND QUality assUraNCe

Product and process-based testing puts a considerable burden on supply chain cycle times and throughput. Hundreds of tests are done on each batch as it progresses through production to ensure efficacy and safety of the final product. A number of tests are done in-line and are relatively quick to complete, while some (e.g. bioassays) can take up to 2 months to give results.

“what’s especially troubling about the allston facility event is that it could happen to any biomanufacturer, at any time. Genzyme was just unlucky.”Engineer, Cell Culture Operations Group



FIGURE 1: Qa/QC release CyCle tiMes

Overall biotechnology supply chain cycle times are currently between 41 days and 3 years; the value-added time (time when the material is actively produced) is between 0.3% and 5% of this (Shah, 2005). The single greatest cause for these very long cycle times is the need to test batches at each stage of production. It is not uncommon for batches to be delayed for 6 months or longer, if an irregularity is found.

In aggregate, the prolonged uncertainty of testing results and consequent delays of production result in large stock levels of intermediate materials; stock levels typically range from 30% to 90% of annual demand quantity (Shah, 2005), an order of magnitude higher than traditional pharmaceutical products manufactured using chemical processes. This is problematic because these intermediate products can tie up millions (or hundreds of millions) of dollars in inventory.

4. prOCess-BaseD reGUlatiON

Since quality testing of the final product cannot provide a complete assurance of product efficacy and safety, regulatory authorities around the world have adopted a process-based approach to regulation. Agencies such as the Food and Drug Administration (FDA) require licensure of the entire process surrounding the production of biotechnology product. This means that any changes to plant or process design must be certified in each country in which it is sold, a process which may take up to 3 years for major projects.

Process-based regulation has an important effect on innovation in the industry, since it provides a large disincentive for companies to change their processes. Many companies have pursued a ‘license and leave’ approach to drug manufacture where no significant changes to a plant are permitted after the facility has been licensed to produce a product. This means that technological changes that may have a dramatic impact on manufacturing cost, speed or even product safety are not pursued since they would require re-licensure. Examples can be seen in the slow adoption of continuous fermentation and purification systems, disposables, and in-line testing systems. This is unlike other high-tech industries such as the semiconductor industry, where technological innovation provides competitive advantage and the only disincentive to innovate is cost-based.

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Some batches require more than 1/2 a year

80% of batchestake less than120 days

Target release cycletime of 70 days.Average cycle time 85 days, standard deviation 30 days

No target releasecycle time. Averagetime 60 days, standard deviation of 70 days



FIGURE 2: reCOVery strateGy fOr 3 DiffereNt CONtaMiNatiON seVerities

5. prOCess DesiGN fOr MaNUfaCtUraBility

Most production facilities in existence are currently multi-product capable in the sense that the basic site infrastructure allows for the manufacture of a range of products. At least in principle, this means that new drugs can be produced in existing facilities. In practice, however, it is quite difficult for facilities to change production modes because of the sensitivity of culture growth and purification to hundreds of production parameters. Almost all existing biopharmaceutical production is ‘product specific,’ that is, the facility is designed around the specific drug compound being produced in order to maximize the titer and yield of that cell. This leads to a highly irregular supply chain design in firms with more than one product.

One of the growing issues in process design is the need to match the capacity of each of the major production steps. Fermentation, or cell growth, has increased titers more quickly than the ability of purification processes to manage the additional material produced. This evolution of titer has created bottlenecks in recovery operations and the need to make significant changes in downstream processing to accommodate these higher titers.

Case stUDy: CONtaMiNatiON Of a BUlk BiOMaNUfaCtUriNG faCility

One of the critical issues in supply chain planning is the need to model the effect of adverse events like contamination. Bioproduction Group was asked by a large biomanufacturer with a number of facilities to evaluate the likelihood and impact of a contamination event in their facilities similar to Genzyme’s 2008/2009 events. The team determined early on that it was not possible to ignore or ‘average out’ the above issues and instead constructed a high fidelity, high accuracy facility-network model using Bio-G’s patented Simulation System technology. Using data from plant managers and engineers, the model was designed to accurately depict the production process and constraints of all the facilities in the network.

BACKUP FACILITY A2 MO.

BACKUP FACILITY B2 MO.

1 MO.1 MO. + 3 MO. 1 MO.

REORDER CAMPAIGNS, USE TWO BACKUP FACILITIESJAN 2009 JUL 2009 JAN 2010 JUL 2010

6 MONTH OUTAGE

6 MO.

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CONTAMINATION

RESTART CAMPAIGN

JAN 2009 JUL 2009 JAN 2010 JUL 2010

2 MONTH OUTAGE

3 MO.6 MO. 3 MO.2 MO. 1 MO. 1 MO.

Through discussions with subject matter experts and key members of the scheduling department, Bio-G determined several possible reactions to a facility contamination to prevent product shortages. “When our client says ‘no patient shall go without treatment,’ it is not corporate rhetoric: they mean it,” says Principal David Zhang. “There is an intelligent way to utilize available network resources to keep production flowing while avoiding unnecessary disruption to the supply chain.”

© Bioproduction Group. All Rights Reserved. 5


FIGURE 3: iNVeNtOry risk prOfiles aNalyZeD By the siMUlatiON systeM

Bio-G’s strategic model was used to evaluate a number of possible contamination scenarios, depending on the severity of the contamination event. Low severity (2 month recovery time), medium severity (4 month recovery) and high severity (6 month recovery) were each considered to determine the range of effects on the supply chain network. The diagram above shows the range of possible responses to adverse events, depending on the event’s severity. Backup facilities were used in more severe contamination cases to ensure stockout would not occur. The Simulation System aided planners in quickly evaluating the best response in each case.

2 MONTH OUTAGE

4 MONTH OUTAGE

6 MONTH OUTAGE (BACKUP)

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Bio-G’s strategic model provided a decision support tool that allowed for the quantification and mitigation of risk for strategic decision-making. The results showed that stockouts would occur with some of the response strategies being considered by the manufacturer, leading to policies and accurate inventory setting to prevent these from occurring. The tool was then used by the client to determine the best responses amongst all possible scenarios, increasing confidence that the manufacturer had sufficient agility to respond to adverse events.

MOViNG tOwarDs leaN aND jUst-iN-tiMe

One of the most critical questions facing current supply chain planners today is how to optimize their available capacity over the short and medium term (1-5 years) while also considering the impact of many of the key variables mentioned above. New product versions, for example, bring higher titers which move bottlenecks in the network. New technologies – disposables, HTST, modular skids, etc. – are being adopted, but their impact on the supply chain is not yet clear.

While SAP and other traditional inventory planning toolsets have been applied to this problem, they have been found in practice to grossly underestimate the level of variability in the network and overestimate the feasibility of supply plans. This is due to the fact that such toolsets are inherently deterministic, using a single number to represent each parameter in the scenario rather than allowing a range of possible values. Such systems are also inherently ‘push’-based, and are inflexible to changing supply and demand conditions (Hopp & Spearman, 2000).

Bio-G’s approach to this problem is different. Instead of relying on single-point estimates, the software gives planners the ability to generate and explore ranges of values. This means the software produces results that explicitly account for variability, risk and its effect in the supply chain – analysis that is desperately needed in the biopharmaceutical industry.

“Bio-G’s unique software plays a role in keeping U.s. biomanufacturing capacity globally competitive as well as ensuring lower costs for patients.”Industry Analyst, Biopharmaceutical/Pharmaceutical Sector



FIGURE 4: New prOteiN-free fOrMUlatiON at 60% lOwer OBserVeD titers at the pilOt sCale

FIGURE 5: iNtrODUCiNG a New prODUCt VarieNt DOUBles the NUMBer Of skU’s iN the sUpply ChaiN

Case stUDy: exaMiNiNG the effeCt Of a New prODUCt VariaNt ON sUpply ChaiN reliaBility

Bioproduction Group was asked by a large biopharmaceutical manufacturer to implement a toolset to allow them more robust analysis of the effect of a new product introduction. The new product used media that was not derived from animal-based products, meaning a lower risk of supply outage due to raw material contamination. However, the new product was exhibiting significantly lower harvest titers than expected, leading them to re-evaluate manufacturing capacity.

In addition, the biomanufacturer’s supply chain was highly fragmented by the need for a number of product variants for dosage, capping type and market. The introduction of this additional new product variant required the manufacturer to smoothly transition from the existing product to the new, while allowing countries to purchase the old product variant until the new was approved. Such an approach caused an explosion of product variants that the supply chain was required to handle, with an uncertain effect on inventory levels and risk.

CAPPING TYPE

PRODUCT DOSAGE

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NEW PRODUCT (NO ANIMAL-DERIVED PEPTONES)

= 60 PRODUCT VARIANTS

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feeDBaCk

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fUrther reaDiNG

Shah N (2004) Pharmaceutical supply chains: key issues and strategies for optimization. Computers and Chemical Engineering 28: 929-941 Hopp and Spearman (2000) Factory Physics, McGraw-Hill

MOre iNfOrMatiON

BiOprODUCtiON GrOUp [email protected] www.BiO-G.COM

Bioproduction Group’s approach was to build a high speed supply chain simulator that could evaluate the capacity of each of the manufacturing facilities used by the manufacturer, along with hundreds of rules and constraints on the manufacturing process. The result was a model that was both a highly accurate representation of the manufacturing network, and one that could easily be altered by planners to perform sophisticated what-if analysis.

The approach allowed the biomanufacturer to confirm that a world-wide introduction of the new product posed an unacceptably high risk to their supply chain. Using data from over 10,000 years of simulated time and hundreds of automatically generated scenarios, the Simulation System suggested a staggered product introduction that focused on markets that were likely to approve the product quickly, while leaving countries like Japan and Canada (with long and highly variable approval times) for a later date.

The figure above shows the effect of implementing the staggered production introduction strategy vs. the original strategy where the firm launched simultaneously in all markets. The difference between the two strategies is not visible to the supply chain organization until nearly 9 months after launch when inventory starts falling below target levels and manufacturing reacts by shifting to higher capacity. But this capacity increase is not sufficient to bring inventory levels back to target values for nearly 2 years after launch, posing an unacceptable risk to the business.

In this graph, the use of a simple yet powerful aggregated metric ‘Supply Chain Risk’ based on a Value at Risk or VaR calculation, allows the business to see with one view real comparisons between supply scenarios. The ability to perform this analysis allowed the business to bring concrete analytics to their supply chains, providing a ‘virtual supply chain’ that analysts could quickly alter to evaluate what-if scenarios.

CONClUsiONs

Biomanufacturing Supply Chains represent unique challenges for planners: the need for high service levels in a heavily constrained industry with long lead times, high levels of regulation, and increasing focus on inventory and cost reduction. Bioproduction Group’s Simulation System provides the means to model such complex supply chains in an easy-to-use, powerful framework. The patented technology provides a 100-fold increase in speed over existing simulation toolsets, allowing planners to perform sophisticated design-of-experiment and what-if optimization easily and quickly. This approach has been used to quantify millions of dollars in direct cost savings to biomanufacturers, while aiding risk avoidance and increasing supply reliability to customers.

“the ability to rapidly and confidently model and analyze a complex supply chain could have enormous impact on a firms’ ability to remain competitive and to continuously improve high technology production processes.”National Science Foundation Evaluation of Bio-G Technology, 2010

Staggered Product Introduction Strategy

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ORIGINAL STRATEGYSTAGGERED INTRODUCTION STRATEGY

INVENTORY FALLSBELOW TARGETS,MANUFACTURINGAT CAPACITY

LAUNCH IN 2 MARKETS LAUNCH IN 4 MARKETS LAUNCH IN ALL MARKETS

FIGURE 6: Var CalCUlatiON fOr the MaNUfaCtUrer’s OriGiNal sUpply ChaiN strateGy, CONtrasteD aGaiNst a staGGereD prODUCt iNtrODUCtiON strateGy

Biomanufacturing supply chain optimization

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