Supply Chain Leaders and Practitioners are eagerly looking out for ways to adopt new age technological solutions into specific supply chain problems, which can address specific issues and bring real value additions into the balance sheet of the organization. Through this article, Raniprasad Palod, General Manager – Global SC Strategy & Analytics, Sun Pharmaceutical Industries Ltd., offers a direction to all supply chain practitioners in answering ‘Where can I apply data analytics in my supply chain?’
Demanding customers, emerging technologies, global supply bases and ever-changing market landscapes require companies to continuously adjust their supply chains to stay competitive. Relying on traditional supply chain execution systems is becoming increasingly difficult, with a mix of global operating systems, pricing pressures and ever increasing customer expectations. Recent economic impacts such as rising fuel costs, the global recession, supplier bases that have shrunk or moved offshore, as well as increased competition from low-cost outsourcers add to the complexities. All these challenges potentially create some or the other kind of waste in the supply chain.
With the advent of technology in the past few years, we are hearing more and more about the use of new advanced analytics machine-learning platforms and technology enabled solutions in supply chain and logistics which are aimed at reducing cost and improving service levels. Supply chain is a great place to use analytic tools to look for a competitive advantage, because of its complexity and also because of the prominent role supply chain plays in the cost structure and profitability of a company.
Let’s look at the generics pharma supply chains, which are complex and almost vertically integrated. As against FMCG industry wherein planning, sourcing, manufacturing and distribution is under the umbrella of supply chain, generics supply chain doesn’t have control on the manufacturing of formulation plants. Formulations plants are governed by regulatory standards, quality compliance and so the manufacturing vertical has its own identity and ownership. These formulation plants are global plants; they manufacture formulations for all the markets in which products are being sold. They are, in turn, dependent on various vendors to seek required ingredients to produce formulation drugs. Pharma companies prefer to have their own API factories, which is a vital source of raw material to their formulation plants and additional business to drive top line. Non-core formulations are outsourced and manufactured from Contract Manufacturing Sites. Procurement organization plays vital role in collaborating with the contract manufacturing sites and collaborating with the vendors to supply all ingredients required for the manufacturing of formulations and APIs.
Supply Chains for Generics face various challenges, to name a few:
Demand Variability & Penalties: Forecasting of formulation demand is a greater challenge due to little or no visibility of the distributors’ inventories. Distributors are customers for almost all generic pharma companies. In developed countries like the US, distributors like McKesson and Cardinal charge penalties to parent pharma companies for untimely delivery of formulations. These penalties are huge and parent pharma companies supply chain needs to be agile to mitigate this risk.
Cost of Manufacturing: Manufacturing plants are designed for large scale production. These plants manufacture volumes for various markets. Volumes of orders from the distributors are far less than a single batch of manufacture. Many a times, pharma companies decide not to manufacture a batch due to its non-profitability.
Agility in Manufacturing: Ability of manufacturing plants to meet demand variability is a big challenge. Manufacturing constraints like higher batch sizes, product robustness and higher quality checking times lead to delayed delivery to customers.
Additional Logistics Cost: Due to operational delays, supply chains incur significant air freight charges, in ideal situations such consignments should travel via the cheapest sea route.
Availability of Ingredients: On the procurement side, availability of all ingredients at right plant in right quantity is a challenge. Non-availability of any ingredients delays timely manufacturing of formulations which, in turn, results into delayed delivery to distributors and additional burden of penalties from distributors for lower service level.
Complexity in Contract Manufacturing Sites Network: Many non-core products are manufactured by contract manufacturing sites. Ensuring timely manufacturing from these contract manufacturing sites is an uphill task and a significant bandwidth of procurement teams goes into managing urgent formulations.
Innovate to Reduce Cost: In addition to all above challenges, there is an additional expectation to find newer and better ways to reduce supply chain cost without compromising on service levels.
We spoke about various challenges earlier, let’s see how analytics can play an enabling part in addressing these challenges:
Demand Variability & Penalties: To mitigate this concern requires usage of deep learning techniques to forecast products, which are high volume and high variability in nature. One can expect significant forecast accuracy improvement using this nontraditional method of forecasting. Using specific machine-learning models to predict sales loss for each business, and then integrating them in S&OP planning cycles will provide visibility to manufacturing plants. This will help prioritize the manufacturing of products wherein penalties are expected from the distributors in the future.
Cost of Manufacturing: To address manufacturing challenges, there is an urgent need to develop long range planning models, which will provide visibility to bottlenecks expected in the future for all manufacturing plants. These bottlenecks could be then resolved by taking appropriate actions like outsourcing or capacity expansion. On the operational front, simple tools like total SC cost v/s Sales loss can be provided to plant planning teams whenever they are facing a dilemma over producing a particular batch. These tools can be integrated in planning systems.
Agility in Manufacturing: There is a need for MES systems for capacity constraints in all manufacturing plants. This MES system will provide tracking of critical metrics such as system reliability, system utilization similar to FMCG industries and will bring rigor on identifying significant root causes, contributing to major productivity loss. Additionally, cost bene.t tools, which will compare sales loss v/s incremental cost in quality teams, cost to reduce batch size extra, have to be developed. These tools will provide operational decision-making support to planning teams.
Additional Logistics Cost: Cost benefit tools like additional air freight cost v/s sales loss are to be developed here and handed over to logistics planning team. This will provide decision makers adequate information and rationale to send a consignment via sea route or air route.
Availability of Ingredients: On procurement side, there is a need to define scientific inventory norms for raw material, packaging material and other ingredients. These norms will take into consideration all kinds of variabilities and will stock the items as per criticality to sales loss. Items, which are critical to sales loss, will have to be assigned higher service levels.
Complexity in Contract Manufacturing Sites Network: To reduce complexity in Contract Manufacturing Sites Network, there is a need to develop supply chain network models, which will enable scenario planning. Scenarios such as closing contract manufacturing sites and transferring few products to parent company manufacturing plant, reassessing the overall network for better service levels, etc., can be responded to once these models are built.
Innovate to Reduce Cost: To reduce overall cost, supply chains need to be modeled for total delivered cost. These models will provide avenues to carry out scenario planning and provide levers to focus on in reducing the overall supply chain cost vis-à-vis incremental improvements in pockets. There is also a need to develop strategic supply chain analytical capabilities like cost- to- serve analysis. This analysis will help supply chains segment products based on net contribution margin and then assign differential service levels to these segments. Applying scientific inventory norms to these segments will define right inventory levels in the supply chain and will improve service levels and reduce penalties raised by distributors.
In the end, there is an immense opportunity to use technological advancements in any supply chain. Availability of data in the organization, having easy access to it and also having dedicated analytics / data science teams is a prerequisite. Within a span of 2-3 years, these teams will evolve and will start adding value, which is well beyond one could even imagine.