In Part One, we covered current supply chain performance benchmarks and key issues prevalent in the Pharmaceutical industry.
This post is structured across the following topics:
- Long Range Planning
- Short / Mid-Range Planning
- Special Demand Situations: Tenders
- Demand Sensing & Inventory Optimization: How it helps, Hard Benefits
- Integrated Business Planning IBP: How it helps, Hard Benefits
- Sales & Operations Planning S&OP vs. Integrated Business Planning IBP
- IBP: Solution Capabilities Required
- IBP in Pharma
- Closing Remarks
Pharmaceutical industry product and market mix is changing when it comes to growth opportunities. The industry will continue to grow 4-7% overall given the following growth drivers: emerging markets which are growing nicely 8-11%, niche products, and biotechnology treatments, etc. Diabetes, Oncology, and Immunology remain high-growth areas, while a major chunk of recent new drug approvals are focused on niche disease areas with lower market size potential, but higher pricing power.
Western demographic trends point to an aging society with resulting higher pharmaceutical consumption, though the governments in these countries and the world over are applying pressures on pricing through regulation which mandates price caps or forces doctors to prescribe generics over branded medicine. This has resulted in the highest growth opportunities for generic pharma companies, while the branded counterparts chase innovative products and reduce prices of patent expired medicine in order to retain market share. This increases cost pressures on supply chain to make the same product at much lower cost and support the low-volume high margin niche product business.
The emerging markets growth brings in volumes, but not as much revenue due to lower prices given buying power is not at the same level as Western Europe or US markets. Hence, the imperative to pursue this growth profitably and to price products economically implies costs have to be squeezed out holistically while preserving service levels.
Declining R&D productivity and pricing pressures have resulted in extensive Mergers & Acquisitions as the first response. Biotech startups are routinely being bought out by the larger players to shore up biotech portfolios. This has increased complexity in the supply chains, which need to act as a key lever of profitable growth going forward.
Uncertainty about future demand is increasing given uncertain economic outlook globally and given the fast emerging preventive care through digital health portals, biotechnology, products and growing trend of herbal / ayurvedic medicine in large markets like India, Middle East, and Africa.
In the context of changing competitive and growth landscape, let's explore supply chain strategies and advanced analytics based solution capabilities that Pharmaceutical companies can deploy to deliver breakthrough profitability while pursuing growth.
Long Range Planning
Long Range Planning LRP across a 2-5 year period is key. Business planners need to generate a range of likely demand scenarios that factor in the life cycle impact of existing portfolio, new product introductions, and health economics trends. This is required to get a clear sense of uncertainty / risk and best case / worst case capacity requirements given the long lead time to put capacity expansion plans into practice.
Emerging markets need to be fully understood to come up with differentiated strategies based on market specific reimbursement practices, healthcare structure, demographics trends, price affordability, and so on to come up with the right product portfolio and pricing strategy to grow profitably in a specific emerging market. Quite often, a range of generic pharma companies all go in the same emerging market with lofty projections of market share only to find out first year level volumes persisting in third or fourth year. Given the market team is loathe to admit defeat, the lofty forecasts continue resulting in SKU/Market portfolio complexity for the supply chain and inevitable waste in the supply chain as market specific shipments are rounded up to get manufacturing and logistics cost efficiencies.
There are multiple value leakage points in the Long Range Planning LRP process supported by spreadsheets:
- Spreadsheets offer limited end to end scenario capability to convert demand projections into capacity requirements.
- There are also challenges with market specific spreadsheet formats and varying degrees of detail across marketing teams in individual markets.
- Spreadsheets are cumbersome from data / integration standpoint when it comes to modeling the full SKU portfolio in a granular way.
- Spreadsheets cannot offer end to end enterprise level financial optimization or provide visibility to understand supply chain optionality across scenarios in terms of ranges of demand (risk of USFDA approvals or timing).
- Spreadsheets also offer limited support to generate an optimal supply response: where to source the demand for optimal SKU/Plant mix (balancing supply network to optimize capacity utilization), or to optimize demand-capacity dynamic in the context of cost to serve / profitability / risk.
At GitaCloud, we bring advanced enterprise modeling & optimization platforms through our partners like River Logic to create a range of demand scenarios factoring in USFDA approval risk, economic outlook, audit risk for supply facilities, etc. We also generate full end to end enterprise scenarios across demand, supply, and financials to model local presence in emerging markets, outsourcing manufacturing with associated quality risk modeling, and rebalancing of the supply network to optimally use current capacity assets before identifying the net capacity gap along with supply chain design recommendations for when and where to add what level of additional capacity.
Short / Mid Range Planning
In the Short / Mid Range Planning, Pharmaceutical companies need granular and automated Demand Sensing / Inventory Optimization capabilities to scale the entire SKU portfolio across all markets including markets where steady state has not been achieved to reduce inventory oriented costs and free up working capital, while preserving the service levels.
A more accurate SKU/Market/Daily level signal reduces safety stocks required in markets, and also mitigate bull-whip effect upstream in Dosage Formulation DF and Active Pharma Ingredients API supply chains. Generic Pharma companies need to be able to model the demand drop post the initial 180-day exclusivity period as other generic drug makers come in to take market share away. This fractures the trend and renders forecasts from traditional demand planning tools ineffective.
As the SKU/Market level portfolio explodes with M&A, innovation, and global expansion strategies to enter new markets, the traditional human judgment centric demand & inventory planning approaches scale poorly, add waste, hamper agility, and reduce profitability.
Demand forecasting performance needs to improve significantly across the full SKU portfolio with Demand Modeling automation at SKU/Market/Customer/Daily level granularity and daily frequency to eliminate demand latency and deliver the best possible signal to supply chain with clear understanding of risks.
Forecast granularity and pushing forecast signal generation point as far downstream as possible are keys to accuracy as secondary (wholesaler to pharmacy) or tertiary sales (pharmacy to patient) Point Of Sale POS data becomes available. As signal generation point travels downstream, bullwhip effect reduces, and a more consistent signal emerges resulting into more accurate demand forecasts.
Special Demand Situations: Tenders
Demand modeling/forecasting automation through demand sensing helps as Pharmaceutical companies need to forecast new SKU/Market combinations and new demand channels in terms of tenders, spot sales, etc.
Demand scenarios need to be stochastic risk modeling based given increasing uncertainties as the demand mix changes, e.g., as tenders become a demand vehicle of choice in many European and other countries. Tenders have more stringent requirements in terms of order lead time and remaining shelf life. Pharmaceutical companies are pushed to provide the goods based on quoted lead time from the date when their tender application comes in, not when the tender is awarded. The inherent all-or-nothing nature of tenders makes it harder for supply chain planners to manage upstream supply activities and requires explicit risk modeling of short term bids.
Market teams must be able to provide a probability of winning the tender. We also need an ability to separate the past history in terms of base demand and past won tender demand for more accurate statistical forecasting purposes. Companies need pattern recognition and stochastic forecasting to assess the likelihood of winning certain tenders and make responsible calls in the supply chain to reserve capacity or procure API accordingly.
Demand Sensing and Inventory Optimization: How it helps
Demand Sensing (demand forecasting automation) is increasingly proving to be a superior choice over traditional human judgment centric demand planning process given the scalability challenges of forecasting ever growing SKU/Country portfolios in a bias-free manner.
Market teams take lost sales to be a much greater sin than inventory waste, which leads to inflated forecasts to protect service levels. The problem gets further compounded with large batch sizes in Dosage Formulation which were put in place to drive manufacturing efficiencies, but end up generating a lot of excess inventory.
Demand & inventory optimization can help by preserving or improving service levels while simultaneously reducing inventory significantly which leads to delivering same or higher revenue at a higher profit margin and reduced working capital requirements. This automated signal can then be provided to market teams for them to enrich the signal based on market intelligence.
We recommend to track Forecast Value Add (FVA) over Demand Sensing raw signal to identify value-dilution scenarios where human edits introduce error. Companies should take the best of raw statistical forecast and market intelligence provided by humans through an automated cognitive self-learning mechanism, which provides appropriate weight towards raw demand sensing signal and human enriched value based on past FVA performance at SKU/Market/Customer level.
The old school traditional demand planning mindset is to forecast at aggregated levels when it comes to the tactical horizon (3 months to 24 months). This is flawed thinking. We recommend applying Long Term Demand Sensing capabilities to generate a granular signal that can then be aggregated up for human enrichment as opposed to an aggregated 'stable' signal which generates noise when it is disaggregated down for execution at Carrying & Forwarding agent (CFA, which is equivalent to a spoke DC in the hub-and-spoke distribution network).
Demand Sensing also dynamically segments SKU/Country into groups using pattern recognition capabilities to be able to forecast a set of similarly behaving SKU/market combinations as a group to reduce noise. This dynamic segmentation is a much superior approach compared to the regular speed (fast moving/slow moving) and value (high margin / low margin) based static segmentation approaches.
Demand Sensing removes demand latency from the chain. Supply chain can see demand changes much sooner and react in time to preserve service levels. This level of daily granularity - daily frequency signal improves visibility and reduces the market teams' need for inflating demand to cover for risk of inventory shortages. Also, in cases of M&A, as new additions to SKU portfolio are typically accompanied by best case forecasts, Demand Sensing can quickly learn from actual sales data at daily granularity to course correct back to a reasonable forecast. This gets accuracy back in the forecast quickly even in the absence of past historical data.
Demand Sensing helps with NPI/EOL scenarios and long tail of the SKU/Market portfolio which the traditional demand planning systems struggle with given lack of stable and sufficient historical data.
Demand Sensing & Inventory Optimization: Hard Benefits
GitaCloud has been able to deliver 38% forecast error reduction on average across several large global organizations. When we take this significantly improved demand signal and further optimize inventory levels needed to cover for the much reduced error, we are able to provide 20-30% inventory reductions in the safety stock across the multiple echelons in the supply chain: wholesaler/distributor/stockiest inventory, Carrying & Forwarding Agent CFA (spoke DC) inventory, Regional Warehouse (hub DC) inventory, and Stock at Factory. Our experience is that companies tolerate high levels of inventory to preserve On Time in Full Error Free (OTIF-EF) or similar fill rate / service level goals given high forecast error and poorly optimized inventory levels. This is a significant opportunity to free up working capital as well as reduced costs from storage, transportation, and eventual scrapping of inventory as it expires.
Integrated Business Planning: how it helps
Let's turn our attention to the supply response side of the equation and the ability to plan end to end across DF, API manufacturing, as well as supporting procurement, inventory storage, and transportation logistics.
Pharmaceutical supply chains are geographically dispersed with Active Pharma Ingredient API coming in from internal factory or a vendor. Supply chain complexity is only increasing in the wake of continued M&A activity, collaboration and sharing of supply chain assets across enterprises, outsourced manufacturing, need to be closer to emerging markets with local presence, and serialization imperative to track unique serial numbers from factory to patient to fight counterfeit drugs. As companies looking to reduce costs have let go of manufacturing assets and outsourced legacy product demand to third party manufacturers, this strategy has resulted into complex supply chains and higher quality risk.
Integrated Business Planning solutions from our partners like River Logic evaluate marginal profit and hence can prioritize low-volume, high margin niche products over high-volume, lower margin mainstream products to ensure optimal demand-capacity matching and allocation in case of capacity shortages. River Logic IBP solution can add 2-5% of current revenues in terms of additional profits as enterprises solve with end to end demand, supply, and finance constraints with an objective of optimizing financial or commercial objectives (market share, growth, revenue, net margin, return on capital, and so on.
Sales & Operations Planning S&OP vs. Integrated Business Planning IBP
Most pharma organizations have adopted some sort of a Sales & Operations Planning process based on either spreadsheets or basic data entry / basic analytics centric applications that focus on getting it done through an inordinate amount of human collaboration across functional silos and organization levels from front line sales, supply planners, and executives.
We are observing much hype in the traditional S&OP software provider market, where many ERP / S&OP providers are replatforming their legacy Supply Chain Planning applications as they move from on-premise solutions to cloud, sometimes with a better visualization / front-end, and call the new replatformed offering an Integrated Business Planning IBP solution! The functional silo mindset is still prevalent as we see functional modules like IBP for Demand, IBP for Inventory, IBP for Supply, etc. leading us to question how integrated the solve really is as supply efficiencies do not lead to optimal inventories and vice versa. It does not help that the solve is not modeling financial constraints (working capital constraints, quarter-end inventory level constraints, etc.) into the solve, which leaves Finance function out from the ‘Integrated Business Planning’ scope.
There is considerable confusion in the customer community as to what IBP really means, how is it different from S&OP, and so on. The traditionalist S&OP or Advanced Planning APS solution providers retain the functional silo solutions mindset, solve for supply chain with limited constraints modeled, and only with basic heuristic type approaches that do not factor in non-linearity. These solutions do not solve for financial optimal performance and do not generate the best possible end to end plan which optimizes financial outcomes given the sequential solve in individual functional modules for Inventory, Supply, etc.
Given the abuse of the 'IBP' tag by several well-established traditional established software providers, we at GitaCloud feel it's important to outline key IBP capabilities required before we review how a true IBP capability can help the Pharmaceutical industry.
Integrated Business Planning IBP Capabilities Required
Many questions executives ask from a strategic or financial perspective are answered poorly by traditional S&OP or now IBP solutions in the marketplace today.
- What is the true profitability of an SKU in a specific market?
- Where should funds and capacity be allocated to drive best possible additional growth or profit?
- What are the risks and confidence levels with current demand signals and supply plans and how do we optimally mitigate them?
- If we have a capacity shortage and demand needs to be shorted, then which SKUs for which markets to optimize overall net profit?
- When should inventory policies be relaxed to build ahead?
- How do I evaluate various scenarios modeling supply chain as well as financial constraints and see the financial impact of supply chain plans in real-time?
- Margin Optimization: For example, how do I chase net margin as a goal, but put 97% service level as a minimum that the solve needs to respect. How does this solve differ when I chase revenue or cost optimization instead of margin?
With exploding SKU/Market portfolios and need to be more agile in planning given a volatile environment, the traditional S&OP human centric planning processes do not scale or provide anywhere close to optimal profit margin as companies are unable to evaluate the true financial impact of their supply chain decisions. The traditionalist ‘IBP’ solutions do not generate a full financial forecast in terms of Forecasted Balanced Sheet, Profit & Loss Statement, and Cash Flow. Even when they convert units to dollars, it is often a one- way street post generating supply plans which factored neither financial constraints (max inventory $$ value allowed at end of quarter) nor financial objectives: e.g., a solve for Net Margin compared to another scenario which solves the same demand & capacity for Return on Capital. Finance can see the forecasted revenue, but they can do precious little to bake in financial constraints or optimize for financial performance in one integrated business solve.
Modern IBP platforms define a full end to end enterprise (or multi-enterprise) model across demand, supply, and finance components of the value network that factors non-supply chain drivers like taxation structure as a variable, not an afterthought. This is especially relevant as large markets like India are streamlining their taxation policies through the new Goods and Services Tax GST.
Modern IBP solution acts as an Executive Boardroom strategy simulator platform which brings together Strategic Planning, Financial Planning, and Sales & Operations Planning disciplines to prescribe optimal way forward that balances risk and profits across a range of business scenarios. True IBP capability explicitly links supply chain and financial constraints with end to end integrated business plans that solve across demand, inventory, production, logistics, and procurement simultaneously, not in silos given trade-offs and non-linear relationships.
Modern IBP solutions not only solve optimally, but also provide full set of financial forecasts, projected financial metrics and enables traditional S&OP to fully align with corporate strategy and financial goals. Traditional S&OP mindset driven ‘IBP’ solutions solve for volume and units that they then convert to revenue using Average Selling Prices. They provide no ability to optimize the product mix, understand forecast cannibalization, optimize promotional spend, and optimize profit by shorting the least profitable demands based on a fully granular Cost To Serve data set by SKU/Market.
Modern IBP solutions provide Integration across many frontiers:
- Integrated capability to solve for Supply Chain Design, Supply Chain Planning, and Tracking / Response Management with a single consistent solve.
- Integrated capability to solve for Strategic to Tactical to Operational horizon
- Integration across Strategic Planning, Financial Planning, and Sales & Operations Planning
- Integration across Commercial, Operations, and Financial teams across all levels of the enterprise
Hope this clears some of the confusion around the full range of capabilities that IBP represents vs. a subset of capabilities that traditionalist ERP / S&OP vendors are able to deliver, which they are happy to brand as IBP given the hype in the marketplace. Now let’s turn our attention to how IBP can help in the Pharmaceutical industry.
How Integrated Business Planning IBP Helps in Pharma
Traditional thinking in Pharma organizations has been to generate demand signal from decentralized marketing teams in individual markets, then consolidate it into total SKU level forecasts for second guessing by central supply planning team in supply chain, followed by Dosage Form Production Planning, followed by Active Pharma Ingredient API Production Planning. Given the large batch sizes and aggregations in the process, the sequential siloed nature of calculations introduce a high whiplash at API production planning stage month to month resulting in API team insisting on 4-6 month frozen period to plan their long API production campaigns. Some companies are even taking the approach of building API to annual budget and treating any outside budget demand for API to be a 3-4 month service additional lead time demand over regular lead times. This hampers supply chain agility and risks lost sales in the market if demand cannot be serviced in the required time frame.
Integrated Business Planning IBP can optimize operations across the entire enterprise from market facing teams providing demand intelligence to DF and API supply teams providing supply response in a way that maximizes profitability.
Integrated Business Planning plays a key role in balancing competing pulls of high service levels in markets and low supply chain cost and inventory waste in the supply chain.
It enables robust scenario management capability to model new drug approvals, NPI/EOL, new market entry, etc.
It can model regulatory approval lead time variability for new capacity expansions to be able to assess risk in terms of best case / worst case demand and capacity projections in order to make prescriptive and optimal calls for capacity reservations and API sourcing planning.
Capacity Optimization and Inventory Optimization have to be approached in an integrated manner for the enterprise to improve Return on Capital and Net Margin.
Scenario management capability is essential for new products or markets where regulatory approval risk, capacity shortages, and/or the market economics can impact time to market and pricing considerably, leading to a need to optimally determine ramp-up volumes.
Integrated Business Planning IBP does away with the sequential and decentralized approach in favor of a centralized demand sensing engine to generate a granular and accurate signal across the whole portfolio. Helped by this accurate demand signal, which is enriched for market intelligence with demonstrated pattern of adding value to forecast, IBP can solve for inventory, production DF, production API, raw material procurement, distribution / logistics, etc. as one integrated business planning exercise that seeks to solve for demand and generate supply response with an eye on optimizing financial outcomes, e.g., net margin or return on capital. A mix of financial objectives by SKU/Market is possible in terms of market share, revenue, profit, or asset utilization (return on capital) focus. This approach requires far less human effort and produces both accurate forecasts and financially optimal supply response.
Pharmaceutical industries have a volatile demand & supply landscape that is only getting more intense. The current traditional planning approaches leave a lot of money on the table by not optimizing for financial performance. The need for better planning is apparent to executives, but the way out of the jungle is foggy given most software providers and management consultants still pedaling traditional processes and solution which require a high degree of human alignment and coordination in essentially glorified spreadsheet type data entry views in the cloud. These processes lose steam rapidly once the consulting engagement ends and significantly under-deliver on value in the business case. Business planners quickly revert back to off-line spreadsheets within 6-12 months after the big strategic transformation program wraps up.
At GitaCloud, we believe in enabling business planners across operations, commercials, and financial teams with a truly Integrated Business Planning platform that can model a wide range of supply chain and non-supply chain constraints; optimize continuously across strategic, tactical, and operational horizons; take advantage of advanced predictive/prescriptive/cognitive analytics to generate far more accurate, granular, and optimal demand and supply plans, which optimize financial outcomes. We firmly believe the IBP solution needs to be a platform that is business owned and grows smoothly with the evolving maturity of the enterprise and its planners as opposed to a tightly defined point-in-time solution delivered by an IBP application that require considerable IT or Consulting effort to make changes.
At GitaCloud, we are for Business friendly Global Planning, where business planners make sound decisions as they have an ability to understand and optimize the impact of their decisions across a business unit, product portfolio, or an emerging market. Executives as well as business planners from all functions should be able to query the same enterprise model from any angle and each time get an optimal, feasible plan in return.
Ashutosh Bansal is the Founder & CEO of GitaCloud.
Incorporated in Delaware in 2015, GitaCloud is on a mission to improve integrated business planning and decision making competencies at it clients. GitaCloud Principals come from a rich background of helping dozens of leading Fortune 500 companies through their business transformation in Sales & Operations Planning, Demand Planning, Supply Chain Planning & Optimization domains. GitaCloud offers a full range of services: reselling best of breed cloud software, business transformation engagements, Systems Integration engagements, and supply chain planning managed services. GitaCloud clients range from High-Tech, Pharmaceutical, Government, Automotive, Consumer Goods, and Retail verticals across North America and Asia Pacific markets. For more information, please visit www.GitaCloud.com.