Managing the Complexities of a Pharma Supply Chain

| By Karthik Ravikumar | Senior Manager, Supply Chain Design | and | Vikas Argod | Senior Manager, Supply Chain Operations | Chainalytics |

What makes a supply chain complex? It is a simple sounding, yet complex question. While we will not seek to answer this age old question now, we would like to list some factors influencing the complexity. Nature of demand, extent of government regulations, diversity and range of SKUs, importance of products to customers, need for traceability…the list can be extended further, but most of those factors are related to the aforementioned items. There are few supply chains that score high on many of these factors, but the Pharmaceuticals and Life Sciences supply chain is up there, so understanding and handling these complexities is absolutely necessary to design an efficient and optimal supply chain network.

Unfortunately, there is no shortage of supply chain network designers who will vouch that pharma is no different than CPG or Spare Parts. Their assertions usually lead you to believe that the same network optimisation models can be applied to pharma as well. Run away from them as far as possible because they are wrong, and there is ultimately no point in wasting valuable time disproving their claims. Your time will be much better spent discussing modeling scenarios with those who understand the challenges associated with these supply chains.

Talking of people who understand pharma supply chain design, we have distilled the uniqueness to three areas that we would like to discuss in further detail: Dangerous Goods (DG) handling and licensing, efficient reverse flow of products and transportation constraints.

Dangerous Goods (DG or HAZMAT in a few countries) have some ‘dangerous’ looking regulations and safety standards. DG regulations are administrative area specific, and country, state or county can have different rules as well. A regulation that applies in Beijing may not apply in Shenzhen. Furthermore, most global companies have their own internal DG rules. Why is that important for a network designer, you ask? The storage and handling costs can be 1.5 to 2.5 times less if DG goods did not have such rules. The storage, handling, special training and certifications required for these products are expensive. In some countries, certain DG categories, like DG-3, must be stored in seperate buildings. Additional regulations such as types of products that can be stored together, the minimum distances between products when stored nearby, emergency exit proximity from certain types of DG SKUs – each influence warehouse storage and handling cost to a greater extent. If costs are not calculated properly, the optimisation model will not reflect the reality and scenarios are only good for powerpoint slides.

The licensing timelines are the other specialty of DG world. Multiple (and often confusing) licensing requirements can delay the network implementation for years. However, the delay can be used to implement other changes in the network if they have already been identified. In a recent engagement with a global life sciences and pharma company, we modeled these delays and recommended a different sequence of network implementation. This sequence looks counter-intuitive for someone without much idea of DG licensing. 

Reverse Flow of Products: Most network designs are focused on creating efficient forward OR reverse flow, but pharma requires forward AND reverse flow. The ability to recall and get the products back to company locations as fast as possible is important. For this, the efficient network flow from demand areas to distribution centers must be already known. Without this crucial piece of information, supply chains will scramble to get the products back resulting in high costs, not to mention, the PR costs of recalls. Hence, a network modeling exercise should consider rare but high volume reverse flow. Including this step doesn’t mean you give it more importance than forward logistics. Of course not. But once-in-a-year recalls should not wipe out all the savings of forward logistics and are merely part of the the total cost!

Transportation Constraints: The third and the final area to consider is rather a simple one, or at least appears that way: transportation of pharma and life science products. Strict temperature control requirements, SKU-mix constraints of DG classes, and ability to reach demand area as fast as possible decide transportation costs and does not break down into a simple per ton per lane cost. Also, some countries do not have cold supply chain on the road at all. Some explosive class materials needs to be shipped one per pallet or one per truck. SKU-mix rules can result in 50% to 75% times more transportation cost than those without these strict rules. Each of these costs must be input to the optimisation model at appropriate levels. Otherwise, the scenario costs will not be real.

Our experience from multiple successful network design engagements with pharma companies shows that handling these unique factors is critical to success. Out of cost categories in a network design model, warehousing, and transportation costs are, often, the high cost buckets. Calculating these costs by considering the right activity drivers, and allocation levels, is key to modeling the reality. Only then can scenarios reflect true cost and optimal networks can be designed.

After all, getting the supply chain right for pharma is not just about handling complexity, it is the question of life or death. So, it’s better to see real “supply chain doctors” – who have the expertise and experience required to implement a successful pharma supply chain network model.

Karthik Ravikumar is Senior Manager of the Supply Chain Design at Chainalytics. Karthik’s expertise is in modeling complex supply chains for recommending optimal networks. He has worked with diverse industries like pharma, retail, appliances, food, agri products etc.

Vikas Argod is Senior manager of the Supply Chain Operations competency at Chainalytics. Vikas specializes in warehouse operations, transformation program design, and service delivery processes in project-based business environments.

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