A great many contradictory predictions about the future of pharma manufacturing have been put out into the universe. Some forecasting focuses on looming serialization compliance requirements and their potentially negative impact on packaging and supply chain operations. Others highlight the bright side and focus on the potential impact of emerging technologies, such as the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML) and blockchain.

Of course, the future will vary company to company. Much will depend on the operational strategies chosen — whether these decisions be conscious or subconscious.

Digital vs. Traditional Factory

Let’s explore the differing results that may ensue if companies take a digital versus traditional approach to problem solving for various business challenges. We’ll call these approaches “Digital Factory” and “Traditional Factory,” and imagine five scenarios played out five years into the future, in 2023.

I will highlight the vastly improved results that I predict for those who choose a digital approach. It is my firm belief that with the accelerated rate of change in digital technologies, and the enablement of track and trace infrastructure, a “Digital Factory” approach will result in massive improvements in data availability, visibility and decision-making mechanisms.

Scenario 1: Bidding on a New Business Opportunity
Case Study: A new RFP is received from a major customer looking to select a new CMO partner to produce a high-volume product portfolio, which could increase current demand volume by 30 percent.

Traditional Factory: Management assumes it can fulfill demand without a realistic capacity or cost analysis. Previous RFP submissions are leveraged to benchmark the best price quote, and a final offer is submitted with adjustments to these benchmarks based on management’s subjective comments on the negotiation power of the customer and his expectations. If the business is won, the management team struggles to meet demand at all costs.

Digital Factory: Product and volume mix of new business is used in the existing digital capacity model to precisely know the capability to supply, together with implications on internal costs such as additional staff needs, shift restructuring, batch-size driven OEE assumptions, etc. After a realistic cost/supply-side analysis is performed, a reasonable price proposal is estimated based on target gross/net margin assumptions. Multiple scenario analysis is conducted on various volume vs. price points to determine the right negotiation strategy. If the business is won, the management team knows exactly what to do to adjust operational parameters.

Scenario 2: Handling a Crisis — Facing Potential Loss of a Major Customer
Case Study: CMO’s largest customer is approached by an alternative supplier and must make a major decision regarding whether or not to transfer 50 percent of its business.

Traditional Factory: Management tries to keep the customer’s volume by offering price discounts and other incentives. If the loss is final, management takes reactive actions to introduce severe “across-the-board” cost-cutting to direct and indirect labor and spending to manage this existential threat.

Digital Factory: Using its digital toolkit, management analyzes various “what if?” scenarios of lost volume/revenue in real-time, and quickly ascertains the most suitable strategic operational response. Sales and Operations Planning executives utilize data analytics to oversee the decision making process, calculating outcomes to key metrics such as profitability, and determining the appropriate set of cost-cutting initiatives based on root-cause-analysis and the estimated extent of impact to various business segments.

Scenario 3: Capital Expenditure and Investment
Decisions for Capacity Expansion
Case Study: After two years of steady growth in business volume, management decides to expand capacity by building a new facility or expanding current facility capacity.

Traditional Factory: Management decides to buy additional equipment immediately, based on the expectation that the volume growth will sustain and require new capacity in the long term. If the capital expenditure (CAPEX) is limited, management will try to maximize overtime labor, creating a high-cost operational setting to meet the capacity needs.

Digital Factory: Management analyzes the need to add new equipment (or a new site) under various business volume and efficiency scenarios, maximizing the throughput on existing equipment under various shift schedules, and undertakes new CAPEX investment only if truly necessary. Digital analyses highlight bottlenecks on capacity; based on this data, investmest is made in solutions to target these bottlenecks, minimizing capital expenditures while maximizing capacity for a balanced cost/capacity framework.

Scenario 4: Demand/Supply Balancing
Using Scheduling
Case Study: High volatility of customer orders requires significant over-time labor cost, so management decides to look at staffing levels and contingent labor strategy.

Traditional Factory: Management hires more labor on a contingent basis and/or asks current staff to work overtime during third shift or weekend shifts. Both scenarios create a high-cost structure, and while the overall utilization stays low, costs go up to manage volatility of customer orders.