FROM -

“Pharmaceutical supply chains: key issues and strategies for optimization”; Shah, Nilay; Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College of Science, Technology and Medicine, London SW7 2BY, UK; 2003

for Introduction/Abstract

Supply chain optimisation is now a major research theme in process operations and management. A great deal of research has been undertaken on facility location and design, inventory and distribution planning, capacity and production planning and detailed scheduling. Only a small proportion of this work directly addresses the issues faced in the pharmaceutical sector. On the other hand, this sector is very much ready for and in need of sophisticated supply chain optimisation techniques. At the supply chain design stage, a particular problem faced by this industry is the need to balance future capacity with anticipated demands in the face of the very significant uncertainty that arises out of clinical trials and competitor activity. Efficient capacity utilisation plans and robust infrastructure investment decisions will be important as regulatory pressures increase and margins are eroded. The ability to locate nodes of the supply chain in tax havens and optimise trading and transfer price structures results in interesting degrees of freedom in the supply chain design problem. Prior even to capacity planning comes the problem of pipeline and testing planning, where the selection of products for development and the scheduling of the development tasks requires a careful management of risk and potential rewards. At the operation stage, it is often difficult to ensure responsiveness. Most pharmaceutical products involve primary active ingredient (AI) production (often multi-stage chemical synthesis or bioprocess) and secondary (formulation) production. Both of the stages are characterised by low manufacturing velocities and are hampered by the need for quality assurance activities at several points. It is not unusual for the overall supply chain cycle time to be 300 days. In this environment, supply chain debottlenecking and decoupling strategies together with co-ordinated inventory management are crucial for quick responses to changing market trends. A good understanding of what actually drives the supply chain dynamics is also required. As often as not, erratic dynamics are introduced by business processes rather than by external demand, and may be eliminated by the re-design of internal business processes or supplier/customer relationships.

Improvements to the strategic decision-making process

We can consider this in a bottom-up fashion. The current nature of the process technology is one the main supply chain bottlenecks. There is a need for more agile equipment which will shorten process cycle times by an order of magnitude and require minimal time for cleaning and changeover. This will avoid long campaigns and should lead to “pull”-based active ingredient manufacturing, and therefore more responsive supply chains. The underlying processes will have to change as well, with the focus being on designing processes that operate at intrinsic rates (e.g. being limited by reaction kinetics)

rather than being limited by equipment performance (e.g. heat transfer, mass transfer or mixing characteristics) of traditional equipment. In principle, the low tonnages involved should lead to much less capital intensive plants if this is achieved. Generally speaking, significant improvements to manufacturing technology have not been of the highest priority in this field to date. Processes should be designed with a much greater level of mechanistic understanding and controlled tightly if reductions in quality control activities are to be possible. The impact of this has been demonstrated earlier. The integration of development management and capacity and production planning will be very important. Currently, capacity issues are often not considered at the development stage. Booth (1999) lists three undesirable outcomes of not co-ordinating these activities: (i) shortages of materials for pre-clinical studies; (ii) shortages of materials for clinical trials; and (iii) delays in time-to market—which is not only dependent on having material available, but also on generating demand at the late stages of development. The approach of Maravelias and Grossmann (2001) is a very promising platform to treat the overall pipleline/capacity planning problem, but it needs to be integrated with a sophisticated treatment of risk (e.g. through the use of real options theory) and economics (e.g. taking account of local taxation regimes, transfer pricing, duty drawbacks, etc.), both of which have a very significant impact on investment decisions. Furthermore, the modelling of testing and trials needs to be extended to account for variations in standard outcomes, e.g. very successful trials resulting in short-circuiting of the approvals process in the case of life-saving drugs. Generally speaking, the development of integrated models of the life-cycle, from discovery through to consumption would greatly facilitate strategic decision-making.

Future scenarios Companies have recently moved away from product diversification and locally adapted products. The common packaging and labelling standards in the European Union, for example, have supported this. However, there has been much development in the field of “pharmacoeconomics” which might generate pressures to reverse this trend. This discipline helps to make choices in treatment options by consideration of costs and outcomes (clinical, economic and humanistic). An important outcome of this type of analysis will be the insistence on local solutions to local problems (Thwaites & Townsend, 1998). Another trend, somewhat further down the line, will come out of genetic research and which will identify target sub-populations for different treatment regimes (the so-called “designer drugs”). These two drivers will give rise to considerable product and supply chain complexity. The current manufacturing processes and supply chains are not well configured to cope with this. Primary manufacturing tends to operate in campaigns; and secondary manufacturing batchsizes are typically 1–4 million tablets. Clearly, the manufacturing processes and supply chains will have to be re-designed if product customisation is to increase in line with these trends. The manufacturers will have to track supply chain performance measures very carefully in order to understand the cost-to-serve for a diverse customer base. An emerging area is that of rapid response vaccines and other treatments arising out of possible emergencies (e.g. bioterrorism or very fast developing epidemics). Again the traditional supply chain (particularly for vaccines) is very slow and unresponsive. If national governments are to implement emergency preparedness programmes, the entire infrastructure must be well designed and tested through simulation. Decisions such as where to manufacture, in what quantities, where to hold stocks,

where people should report, etc. need to be taken in a robust fashion. The issue of how well the supply chain measures up to emergency preparedness concerns is being raised in various fora at the present (see, e.g. Anon, 2002). Companies are investing in the development of crops that are designed in some way to produce pharmaceuticals; this will give rise to new research activities in process (in particular recovery steps) innovation, novel equipment design (e.g. supercritical separations) and of course in a new type of supply chain to optimize. There are also some changes afoot in the industry structure as well—the growth in medicinal chemistry, biotechnology and genomics will spread IP around and result in looser, virtual enterprises of joint ventures, alliances, etc. Including as well the general tendency towards outsourcing of manufacturing, this will give rise to complex extended supply chain co-ordination. Lessons may be learnt from the automotive, PC and consumer electronics world, where such supply chains already operate.

Conclusions The pharmaceutical supply chain used to be seen as a tool to supply products to market in an effective way, where the emphasis was on security of supply. Recent changes in the operating environment mean that companies are revisiting the components of their supply chains and identifying ways of extracting additional benefits from them. In this sector in particular, the supply chain of interest is not simply the physical processes of conversion and distribution of materials. Equally important is the “value-chain” perspective of managing the innovation and development processes through to capacity and production planning. There are still several exciting research challenges in this value chain, many of which the process engineering/process systems engineering community are well placed to address.

From –

“Supply Chain Analytics: Pharma’s Next Big Bet”; Jacob Snapp, Srihari Rangarajan; 2015

Diagram -

http://images.alfresco.advanstar.com/alfresco_images/pharma/2015/01/09/9b4c5d27-ae40-4f56-b122-528055bbda33/i1.jpg

Inventory visibility — Pharma company inventory metrics have been used for a while; however, there has been a lack of focus on inventory accountability within big Pharma due to business process and information disconnects and commercial vs. supply chain inventory responsibility. Pharma lags in inventory KPIs primarily due to no stock-out policies and complexity created by market specific product expiration requirements. The result includes increasing product discards year over year due to lack of coordination, thereby leading to inventory shortages and overages worldwide.

A KPI dashboard with trend analysis for inventory at a market level and with a multi-dimensional drilldown will be a welcome solution for pharma companies to increase visibility and insight. With incremental detailed data, further analytics can enable near real-time inventory tracking and tracing for controlled drugs. Enabling such metrics will ensure that commercial opportunities are realized for at-risk inventory. Once inventory becomes visible, all other supply chain parties such as vendors and customers, both internal and external, can share information and be in a better position to manage inventory. In the research world, this inventory visibility will form the backbone in managing high-dollar laboratory materials, track supplies in real time and reduce the negative impact it can have on research productivity. Analytics in this space include inventory variance analysis, inventory revaluation, slow-moving inventory reporting, tolled inventory reports, gross-to-net inventory bridge, days on hand, as well as inventory usability for obsolescence purposes.

Network Visibility - With industry acquisitions and consolidation, it is widely understood that network design is the most fundamental decision that will impact customer service levels. This will be the key profitability driver because networks directly affect both supply chain costs and customer satisfaction. The huge investment cost of buying assets and building facilities makes it essential to design a supply chain network which performs well over a long period of time. Because drug and medicine are globally considered a strategic commodity, achieving a stable yet flexible design while optimizing cost becomes the end goal.

Increasingly, supply chain networks have become disparate. As pharma companies seek tax efficiency in European countries, a variety of factors ranging from cost structure, tax laws, materials, and new product launches have driven drug companies to reconsider their supply chain networks. Network design is a powerful analytic driven, modeling approach proven to deliver significant reduction in supply costs and customer-service-level improvements.

A targeted multi-stage network optimization approach will immediately help the pharma landscape. Most PLS companies are constantly evaluating a multitude of product launches, emerging markets, process and real estate investment, as well as divestitures to drive their future state. At this juncture, it is vital to do a quick cost-based analytics exercise focused on KPI's such as investment cost; return on investment; sunk costs; stock costs; transportation costs; etc. Once these KPIs are established, they can then be leveraged to choose between a centralized and a decentralized network design model. Once the right model has been established to optimize the network, a series of "what-if" analyses can be

performed. These scenarios would be based on demand and supply fluctuations, cost inflation, currency exchange risk, fiscal and economic policies, market volatility, tax and tariffs, social sentiments, and so on. The simulation structure would then enable a new, rationalized network with significantly less operating expenses and no impact to customer service levels.

nother key area of working capital that analytics can address is inventory optimization. An end-to-end methodology uses several analytical steps to optimize inventory. The leading step is to develop a supply chain network map that captures the product flow across the network. This is crucial as this will identify the involved sites, the relative volume, and the value of product flows, inventory holding points, the supplier base, and the key markets for the network products. Simultaneously, the lead times across the network are also established and, then, segmented into release, production, quality, and logistics buckets. Once all the information is enabled, statistical analytical models can be built to determine inventory target levels based on observed demand and supply variability and the desired service levels.

The supply chain is globalized in a typical pharmaceutical company. The pharma supply chain is also complex, so inventory optimization should be carefully managed using a two-step approach.

Step 1: Highly strategic inventory locations are analyzed and optimized individually at single stages. This will secure quick wins and allow inventory to be released within a few weeks to months without decreasing customer service levels. The output of this model should be target cycle and safety stock quantities calculated at each node, including factors such as minimum order quantities, production costs, and vendor managed inventory among others.Step 2: The second phase will be to optimize inventory at a multi-echelon level, which is typically done with the help of an advanced optimization tool. A true multi-echelon approach should take into account the following factors:» Elimination of demand signals from the next node in the supply chain.

» Account for all the lead-time variations from suppliers, both internal and external.

» Harmonize order cycles throughout the distribution network.

» Segmented customer service levels based on product demand and supply.

CONCLUSION - The end game: Maintaining supply chain efficiencyIntegration of global supply chains and establishing end-to-end supply chain visibility in the pharmaceutical industry have spawned a variety of challenges;

this includes growth in emerging markets, competitive generic equivalents, and volatile customer demand—all of which add to pharmaceutical supply chain complexity. Supply chain analytics represent a systematic approach to ease the brunt of these challenges. Enablement of basic supply chain metrics and standardized operational reporting are important stepping stones, as these capabilities will pave the way to increasing supply chain effectiveness. Advanced analytics, inventory optimization engines, and network design models will support supply chain executives with the necessary tools to meet the ever-changing needs of the pharma supply chain landscape and enable them to maintain a competitive advantage.