Pharmaceutical and Life Sciences (PLS) companies are under tremendous pressure to improve their supply chains and operations efficiency. Market headwinds include a staggering number of mergers and acquisitions; additionally, big Pharma continues to face an increase of drug patent expirations and downward pressure on profit margins. Aggressive competition from generic pharmaceutical manufacturers, as well as increasing costs to develop and launch new products also factor in—mandating improvements in supply chain and operations. Supply chain analytics is the strategic differentiator for PLS companies to excel in the face of adversity, and will ultimately help these companies become world-class drug or medical device suppliers.
Supply chain analytics are used to measure, monitor, and improve individual business processes as well as the overall performance and health of the supply chain. PLS companies struggle with suboptimized Sales & Operations Planning (S&OP) processes due to a variety of factors, including lack of effective governance, fragmented decision-making, misaligned key performance indicators (KPIs), and a general distrust in sales forecast accuracy. Historically, drugmakers have not been as focused as other industries on working capital. However, the topic has received greater attention in recent years due to expiry of patents, and the squeeze the pricing pressures have put on profit margins. Today, pharma supply chains hold massive buckets of data; this makes it a rich place to look for and establish analytical advantages and for PLS companies to develop a comprehensive, analytical approach to optimize their supply chain and operational efficiencies.
Why supply chain analytics?
For years, conventional wisdom in the pharma industry had it that research and development was the strategic business driver while mainline manufacturing and supply chain strategies were overlooked. Cost of goods sold as a percentage of sales was low and blockbuster drugs commanded premium pricing and market exclusivity. Pharma executives had settled into a mantra of “do no harm” to other strategies. However, in recent years, the dynamics of the drug industry have begun to change. FDA approvals have been slow, and, consequently, revenue growth has lagged. Sales have declined and government involvement in the healthcare industry has put downward pressure on drug prices. Adding to this volatile environment is the fact that many of the market-making drugs are going off patent protection. In light of this, governments have encouraged direct competition by enabling generics. In conjunction, there have been several changes in the regulatory approach that has put a huge emphasis on quality.
So where does this leave big Pharma?
The industry has responded in bits and pieces, but executives are beginning to realize that the complex web of supply chain holds the key to competitive advantage and potential innovation. According to a research article by HFS in 20111, a majority of organizations were planning to increase investments targeted toward supply chain analytics. Insights from such initiatives will help PLS companies optimize their supply chain functions and enable them to manage market pressure, while robustly managing financial performance.
To meet the current industry challenges and increasing supply chain complexity, supply chain analytics must evolve in a rapid way around three areas—enablement, effectiveness, and earnings.
Rapid globalization for drug companies has resulted in an outburst of various types of world-class suppliers, near-shore and outsourced manufacturing plants, far-flung distribution centers, and increased number of drug retailers. Rapid growth in emerging markets has spawned a rising trend to introduce a wide variety of established drug products. The upshot has been the creation of many complex and extended global supply chains that need to be carefully managed.
Before pharma companies can analyze how effective their supply chain really is, they will need to have clear, end-to-end visibility of their global supply chains. At a simple glance, this begins with basic metrics and reporting, as these tools provide the backbone of data for performance measurements. However, in the current state, there is no single source of truth for supply chain visibility. The industry lacks a unified physical and financial view of the supply chain domains that links supply chain strategy, performance management, and risk. To achieve maximum effect, an advanced analytic “control tower” can enable real-time decisions and KPI’s through the cloud.
Enablement in pharma supply chain analytics can be broadly classified into three areas:
» Demand visibility
» Inventory visibility
» Freight analytics
Demand visibility — Poor forecast accuracy and demand volatility continue to challenge drug companies due to sub-optimized sales and operations planning (S&OP) processes. The S&OP process is fragmented due to a lack of effective governance and disconnected decision-making, with misaligned KPIs leading to a lack of confidence in sales forecasts. In addition, the pharma industry faces important quality and health implications due to the fact that the product not only needs to be in the right place at the right time but also with the right quality.
Counterfeit drugs are on the rise. Product authenticity will need to be monitored through increased visibility, especially during a time where there is a intense regulatory scrutiny. There is need to have basic forecasting metrics more visible to enable replenishment; KPIs such as forecast accuracy, forecast bias, and On Time-In-Full can be very quickly deployed and will help measure the pulse in the supply chain planning domain.
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.
Freight analytics — According to a Seabury trade database2, over the past decade, pharmaceutical tonnage has grown in both air and ocean modes to the tune of four million tons per year. More valuable drugs travel by air, with air freight accounting for the majority of the total value shipped. Out of a total value of $269 billion, air freight cost a whopping $213 billion in 2012 alone. Fluctuating demand and global logistics have driven drug companies to continuously reflect on their distribution network design and strategy. Drug companies can realize immense performance improvement opportunities through analytics driven activities such as improved visibility to in-transit goods, freight lane reporting, fleet sizing, load planning, and freight cost consolidation. A quick win here would be to target freight lanes that create imbalances over time and identifying which route supports the business and which ones do not.
Another basic analysis would be to use the Pareto principle; determining the percentage of the network that constitutes 80% of the cost and refining those to maximize yield. An agile approach here would be healthy in monitoring and continually fine-tuning network designs for efficient logistics management. With the recent wave of consolidations in the pharma domain, there is a huge advantage in determining network synergies to drive considerable reduction in overall transportation costs.
An effective supply chain is characterized by the appropriate, consistent movement of data up and down the supply chain. Decisions can only be as good as the data coming through. In the previous section, we discussed how supply chain visibility can be enabled to empower drug companies to make insightful decisions. In this section, we take a look at two advanced analytic capabilities that can power pharma to remain competitive for the future.
Reliability engineering / manufacturing analytics — Big Pharma’s main challenge is that its information is spread across multiple data systems, maintained by different organizations across the globe. Bringing data under a single umbrella becomes a powerhouse for driving process improvement in the manufacturing space in conjunction with compliance to the ever-changing regulatory guidelines. The industry has taken various routes to ensure manufacturing reliability. Genentech and Baxter, for example have embraced lean while Novartis has developed its own six sigma internal tool kit. However, none of these have been entirely successful in maintaining a predictable operational performance. An integrated manufacturing, holistic system that embraces process methods, toolsets, along with analytical models could help pharma with anemic product launches and manufacturing performance.
Real-time measurements of process parameters allow drug manufacturers to leverage advanced statistical analytical methods to monitor and correct process conditions before a potential quality failure occurs. Meaningful, metrics such as overall equipment effectiveness and asset utilization can be used to measure performance trends over time. Loss-tracking analytics can then be activated to ensure that variations to target can be evaluated. Another area of analytics would be to do with critical process constraints. Regulatory initiatives such as design to quality require drug companies to identify critical to quality (CTQ) parameters to ensure that their end products are safe. When batch data, process, and product quality data come together, then analysis can be done to identify these CTQ parameters and their acceptable range.
Network optimization — 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.
Drug companies have long been the envy of other industries, with their strong balance sheets, attractive operating margins, and hordes of cash—not to mention single-product lines that generate billion-dollar annual revenues. The consequence was that little attention was paid to working capital, which is typically defined as the difference between a company’s current assets and liabilities. A positive working capital ratio is essential for a firm to be able to operate profitably, service its debt and fund upcoming operational expenses.
A recent EY report3 on the performance of large pharma companies, has found that there is an aggregate total of $20 billion to $43 billion in cash unnecessarily tied up in working capital, equivalent to between 3.6% and 7.7% of sales. The analysis also showed that the cash opportunity is distributed across each working capital component—40% coming from inventories, 35% from payables, and 25% from receivables. This is where supply chain analytics can play a huge role in the active management of working capital. The focus on achieving a high-quality balance sheet requires granular level cost information at every point in the supply chain—by product, by distribution channel, and by customer. Quantifying these cost differences can help a company discontinue an unprofitable product, alter a distribution network to increase profitability, and then redeploy the freed up capital towards new drug research or towards other innovations.
Once the basic ERP and other legacy system data have been enabled on a common platform, a “total delivered cost” analytic approach can be used to illuminate the granularity of cost variation across the supply chain. This approach works by overlapping additional logic to the data from the company’s various information technology domains. Then, the analytical approach will connect the dots between what the company is selling and to which customers, while highlighting the various levels of costing across the supply chain. Specifically, the first step in this would be to engage in a cost-to-serve as is analytics exercise. This baseline analysis will identify actual costs in comparison to theoretical costs; in addition, this will also build visibility of historical cost to serve at a product and customer level. The second phase in this analytical approach will be based on trade terms efficiency. A trade model can be developed that addresses incentives and discounts based on true cost. This will then be integrated into the commercial and supply chain functions to deliver changes with the trade terms and ultimately, their successful adoption. A third and a complex step to complete the exercise would be to simulate cost-to-serve scenarios across both the internal and the customer network that can help promote joint value creation.
Another 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.
The end game: Maintaining supply chain efficiency
Integration 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.
1. HFS Analytics Market Landscape Report, 2011, Phil Fersht, Reetika Joshi (www.hfsresearch.com)
2. Pharmaceuticals market overview, Global Seabury trade database, 2013, Derek Brand (www.seaburygroup.com)
3. Cash on prescription – Pharmaceutical companies and working capital management, 2011, Ernst and Young LLP
Continue at: http://www.pharmexec.com/supply-chain-analytics-pharma-s-next-big-bet
The text above is owned by the site above referred.
Here is only a small part of the article, for more please follow the link