As the block buster drug pipeline decreases; strategy consulting and analysts are advising Life Sciences companies to focus on core competencies like complex Biologics manufacturing, that have high potential for growth with Biosimilars to retain market share. These manufacturers have already outsourced IT shared services like Travel & Expenses, HR, Call-center Support etc., as they transition to cloud with Capex/Opex consideration, thereby improving margins. Today, in this digital transformation age, BioPharmaceutical companies are evaluating feasibility of Robotics in Accounting using artificial intelligence software that lives virtually in the cloud and leverages two types of technology.
The first is Machine Learning (ML) which helps to identify hidden patterns in knowledge intensive processes and learns from the enterprise data without being explicitly programmed. SAP offers a ML Cash Application, which intelligently learns matching criteria from historical data like AR/AP invoices and automatically clears payments. Business benefits include the processing of incoming payments faster to reduce Days Sales Outstanding (DSO), increased liquidity, maximum use of discount strategies and improved customer service.
The second is Robotics Process Automation (RPA), which helps to run repetitive, rule-based monotonous tasks to perform a series of events. Just like robots have transitioned discrete manufacturing High Tech, Industrial Machines & Components, and Automotive industries in the past, with quality mass production on automated assembly lines, the same concepts will be considered soon in next generation accounting. Smart deep learning algorithms will guide RPA with a proper analysis of accounting data and take relevant action with support of artificial intelligence to automate the tasks. Business benefits include completing repetitive activities quickly and accurately such as bank reconciliation and product cost variance analysis, etc. However, choosing right use case for RPA in Life Sciences will be key in enabling the industry to reimagine a proven industry topic called Tolling as a potential candidate for business transformation.
Toll processing is an arrangement where an organization and/or company will process raw materials or partly completed goods for another company. Also, commonly known in Bio-Pharmaceuticals as toll manufacturing, the processing organization or company could be a third party or an internal manufacturing site and will charge “tolls” for each operational step in the process to the parent requesting company.
Traditional demand-driven supply chains were designed for make vs. buy decisions as Life Sciences companies focused on capacity constraint challenges in late nineties. In the last decade, Toll manufacturing business processes were set up to manage manufacturing inside different plant sites, across global supply chain networks, focusing on cost, with inter/intra company procurement and sales transactions inside ERP systems. The product title owner is called as Principal or “Tollee” typically based in a head quarter company country or regional hub where complete financial transparency is expected for inventory, and product costing during the toll manufacturing process. Plant sites called “Toller” offer manufacturing services, but are not exposed to sales and profit views. New business models emerged in the industry based on country tax situations and focus has shifted from cost to profit, leveraging efficient tax structure this decade. The challenge for IT was to separate material flow from financial flow so that Tollers can focus on the drug supply based on demand from end customers, whereas Tollee can focus on transfer pricing aspects which is supported by SAP. Transfer price affects profit measure for both the selling and the buying division, as multi-national companies pass goods or services between their legal entities for these inter-company transactions. SAP also provides sophisticated analytics tools for profititability and cost management to manage what-if analysis, and simulation scenarios that can help with optimization, thereby reimagining process.
In Tolling, the parent company purchases costly raw material ingredients called Active Pharmaceutical Ingredients (API) from one of its divisional country sites; called internal subcontractors, leveraging inter-company stock transfer orders and ships it to Toller for production of semi-finished drug substance and drug products. Transfer prices are involved during this make-buy relationship as the Tollee then provides an inter-company sub-contracting purchase order to the Toller with the tolling relationship to execute toll manufacturing. The toller performs its services and submits charges, along with profit margins back to the Tollee who records actual consumption with back flushing. Then, the Toller sends the finished goods to the warehouse of the parent company. Transfer prices are involved in this late stage make-buy relationship as the Toller has intercompany stock transfer orders with Distribution centers. Sales transactions then involve flash/spot sales to end customers, that can either be a wholesaler or a hospital; or on behalf of sales involving 3rd party logistics service providers. Predictive Analytics for stock-in-transit can also play a role in improving downstream distribution in the value chain.
During this complex tolling supply chain process, financial accounting plays a key role with various steps involved leveraging a new material ledger from SAP Digital Core. Group cost estimates show the cost and profit contributions from buy-sell relationship parties. Parallel valuation adds transparency to costs and inter-company profits throughout the group internal supply chain. Actual value flow might differ from planned values in each part of supply chain and capturing actual cost is critical. Finally, transfer prices between profit centers may be maintained to treat independent profit shares of units, other than companies. Challenges in Tolling can be process automation, real-time financial reporting, transparency, visibility, and reconciliation which in turn are the potential sweet spots for RPA. However, allowing flexibility for supply chain process modeling in the event of a merger or acquisition, and still leveraging RPA for streamlining and automating only the financial aspect of internal Supply Chain will be key consideration for adoption of this use case.
RPA can be smart and intelligent to support management-by-exception circumstances based on real-time financial and tax information. This can help the financial staff to focus on resolving discrepancies, identify suspicious fluctuations, tax implications and perform the audit function to ensure data integrity and prevent manual error. Toll manufacturing seems to be a promising candidate as RPA can help reimagine work and business processes, but will industry leaders adopt this use case in near future? Time will tell. I am looking forward to your comments, feedback and suggestions for Life Sciences co-innovation ideas are always welcome.