Pharmaceuticals

Case: Improving the competitiveness of pharmaceutical production

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Client

Pharma is a leading manufacturer of pharmaceuticals and medical devices. Annual production volume is 2 mln packs of products worth $50 mln.

Situation Description

The company faced the challenges of rising costs, outdated production processes and increased competition. This led to declining profitability and loss of market share.

The company set an ambitious task for Marrbery – to develop a comprehensive strategy for transforming operations to achieve a sustainable competitive advantage in the pharmaceutical market.

Our team started to thoroughly analyze the situation and develop innovative solutions for optimization, automation and digitalization of PharmEco’s production and business processes. The goal was to achieve a qualitative leap in the company’s efficiency and competitiveness.

Supplemental Data:

  • Production volume – 5 mln packs of pharmaceuticals per year

  • Revenue – $45 million a year

  • Number of personnel – 560 persons

  • Production capacities – 3 plants in different regions of the country

  • Export share – 20% of total volume

  • Main markets – Ukraine, EU and Middle East countries

  • Key competitors – PharmTech, PharmEffect, Ramefarm

  • Logistics costs account for 15% of revenue

  • Warehouse and equipment maintenance costs $2 million per year

  • 60% of equipment has a service life of more than 10 years

                                          Exhibit 1

Problem

  • High operating costs due to outdated production assets and inefficient processes

  • Insufficient level of automation and digitalization of production

  • Long cycle of production and bringing new products to market

  • Ineffective supply chain planning and management system

  • Lack of a comprehensive approach to product quality management

  • Insufficient investment in R&D for the development of innovative drugs

  • Loss of market share due to uncompetitive pricing policy

  • Outdated personnel motivation system hindering productivity improvement

Dictionary
Key Terms
 
Forecasting and planning system: a system that enables a company to forecast demand for its products and services and to plan production, distribution and marketing.
Forecasting: the process of estimating future values of variables.
Planning: the process of developing the actions to be taken to achieve desired results.
Forecast accuracy: the degree of closeness of the predicted value to the actual value.
Investment payback period: the period of time over which an investment is recouped.
Sales conversion: the ratio of successful sales to total sales.
Data analysis: the process of collecting, processing and interpreting data.
Additional terms
Artificial Intelligence: the field of computer science that deals with the development of intelligent agents that can perform tasks normally performed by humans.
Machine learning: a field of artificial intelligence that is concerned with developing algorithms that can learn from data without explicit programming.
Deep learning: a section of machine learning that uses artificial neural networks to solve complex problems.
Case study: a method of learning that involves learning from case studies or examples.
 
Abbreviation
 

  • RMSE: Root Mean Square Error.KPI: Key Performance Indicator.AI: Artificial Intelligence.ML: Machine Learning.DL: Deep Learning.CMS: Customer Relationship Management.CRM: Customer Relationship Management.ERP: Enterprise Resource Planning.MRP: Material Requirements Planning.SCM: Supply Chain Management.

Notes
This glossary summarizes the basic terms and abbreviations used in the case study.
For a more detailed understanding of the case, it is recommended to read additional literature on forecasting and planning systems, machine learning and data analysis. 

Marrbery Research

During the research phase of the study, our team conducted:

  • Benchmarking of best automation practices at pharmaceutical production facilities

  • Audit of Pharma’s production facilities to identify bottlenecks

  • Analysis of raw materials consumption rates for optimization

  • Assessment of the potential for digitalization and adoption of advanced technologies

  • Mapping current business processes and identifying inefficient operations

  • Supply chain analysis and optimization opportunities

  • Study of the best HR practices for increasing staff motivation

  • Comparative analysis of the new product development cycle with competitors

  • Study of supply and demand trends in the pharmaceutical market

Additional technical details of the solution

A multivariate machine learning model based on linear regression was used for demand forecasting.

The factors included in the model were: product price, marketing costs, seasonality, competitor sales volume, etc.

  • Gradient descent was used to train the model with adaptive training coefficients to accelerate convergence.
  • Для оценки качества модели использовались метрики MSE, MAE, MAPE на тестовой выборке данных. MSE, MAE, and MAPE metrics were used to evaluate the quality of the model on a test sample of data.The values of these metrics were compared to the baseline forecast.
  • The neural network architecture for prediction included 2 hidden layers of 100 neurons each with ReLU activation function.
  • Optimization was performed using the Adam algorithm.

Purpose

  • Reduction of production costs by 20%

  • Reduction of TIME-TO-MARKET by 30%

  • Increase in labor productivity by 25%

  • Market share growth by 5%

Solution development

Our team proposed a comprehensive transformation strategy that included the following key initiatives:

  • Modernization of production assets by purchasing modern equipment and automated lines. This will increase productivity, reduce rejects and equipment maintenance costs.

  • Implementation of MES and IIoT to automate data collection, quality control and TP optimization. Best practices in machine learning and predictive analytics will be applied.

  • Supply chain optimization based on modeling and data analysis using the SCOR platform.

  • Re-engineering of warehousing and transportation processes.

  • Deploy Lean Manufacturing and TRM program to identify and eliminate inefficient operations, reduce inventory and downtime.

  • Implement CRM system and digital tools to automate marketing, sales and service.

  • Increasing customer loyalty.

We utilize advanced change implementation methodologies, risk management and KPIs to successfully implement strategy and achieve targeted results.

Decision tree

Evaluation Criteria:

  • Labor productivity

  • Flexible production

  • Implementation costs

  • Payback time

  • GMP compliance

Alternatives:

  • Mechatronic modular lines

  • Classical automation based on PLCs and SCADA

  • Hybrid solution using industrial robots

  • Integrated automation with MES and IIoT

Analysis:

  • High productivity but limited flexibility, high costs

  • Low start-up costs, but limited scalability

  • Flexibility due to robots, but high cost of implementation

  • Maximum return, but high initial investment and complexity

Solution: Complex automation (4) with phased implementation and pilot projects.

appendix 1

Our analysis shows that given the high material intensity of these areas and the volatility of raw material prices, companies will have to optimize costs, implement advanced technologies, and automate processes to improve efficiency.

Calculation of the effect of implementation

Initial data:

  • Production volume – 5 mln packs per year

  • Unit cost is $5

  • The selling price is $10

  • Modernization costs – $2 million

Calculation:

  • 20% cost reduction to $4 per unit

  • Additional profit on sales: 5 million * ($10 – $4) = $30 million

  • Cost savings: 5 million * ( $5 – $4) = $5 million

  • The cumulative effect is $35 million.

  • Payback period: $2 mln / $35 mln = 0.057 years

Conclusion: Thanks to the comprehensive modernization of production facilities, Pharma will receive $35 million in additional profit and will recoup its investment in 0.057 years.

Details

Description of the implementation process:

Technological details:

  • Detailed description of the selected equipment, its technical characteristics

  • Features of integration of new equipment with existing systems

  • Software and algorithm specifications for MES and IoT

  • Sensor and controller network architecture in a manufacturing facility

Organizational details:

  • Project team structure, distribution of roles and areas of responsibility

  • Stages of solution implementation and project implementation schedule

  • Personnel training plan for new equipment and software

  • Risk minimization and change management activities

Financial Details:

  • Sources of project financing and investment structure

  • Investment performance indicators: NPV, IRR, ROI

  • Projected cash flow from project realization

Conclusion

Based on the results of a comprehensive study and analysis of Farmi’s operations, Marrbury specialists developed a strategy to modernize production facilities and optimize business processes.

The proposed solutions cover:

  • Renovation of fixed assets and automation of production

  • Implementation of MES, IoT and quality control systems

  • Supply chain and logistics optimization

  • Deployment of a lean manufacturing program

  • Marketing and sales automation

  • The implementation of these initiatives will enable Pharma to achieve its strategic goals of reducing costs, accelerating new product launches, improving productivity and increasing market share.

Based on our analysis of other companies’ experience in Pharma, we believe that implementing these solutions can enable the company to achieve these results:

  • 20% reduction in production costs

  • Reduce time to market for new products by 50%

  • Increase in market share by 10%

These results will be achieved due to the following factors:

  • Renovation of fixed assets and automation of production will allow the company to increase productivity and production efficiency.

  • The implementation of MES, IoT and quality control systems will enable the company to improve product quality and control costs.

  • Optimizing the supply chain and logistics will allow the company to reduce transportation and storage costs.

  • Deploying a lean manufacturing program will allow the company to eliminate unnecessary costs and processes.

  • Marketing and sales automation will allow a company to more effectively attract and retain customers.

We recommended that Pharma make the opportunity to implement these decisions to achieve its strategic objectives.

Result

Business effect

With wise leadership and strategic thinking, we have achieved impressive results. A 20% increase in productivity is evidence of our management efficiency. Optimizing production costs by 15% is proof of our prudence and economic courage. A 10% increase in sales is evidence of our dexterity in marketing strategies. These are the results of our masterful management and prudent planning, giving us a decisive business advantage

Recommendations

To consolidate the results achieved by the company, we recommend:

  • Continue to improve production processes and implement new technologies to increase productivity.

  • Improve product quality control systems and implement MES, IoT to optimize production processes.

  • Thoroughly analyze supply chain and logistics to optimize and reduce costs.

  • Strengthen lean manufacturing measures to reduce negative environmental impact.

  • Support marketing and sales programs to increase brand awareness and market presence.

                                          appendix 3

Forecasting based on 3 scenarios

Scenario 1 (pessimistic):

  • Cost growth of 5-7% annually

  • Market share reduction by 3-5%

  • Decrease in profitability to 7%

Scenario 2 (most likely):

  • Cost optimization by 10-15%

  • Maintaining market share at the current levelе

  • Profitability 10-12%

Scenario 3 (optimistic):

  • 20%+ cost reduction

  • Market share growth by 5-7%

  • Profitability 15%+

Recommendations:

  1. Equipment modernization

  2. Process Automation

  3. Logistics optimization

  4. New product development

  5. Entering new markets

The bottom line of the work we did was:

The implementation of these initiatives will enable Pharma to achieve its strategic goals of reducing costs, accelerating new product launches, improving productivity and increasing market share.

Our team developed and implemented a transformation strategy that included:

  • Renovation of production equipment

  • Automation of key processes

  • Optimization of logistics operations

  • Launch of innovative products

  • Entering promising foreign markets

Project Outcomes:

  • Reduction of operating expenses by 18%

  • 25% increase in productivity

  • Profitability growth to the target level of 12%

The implemented solutions allowed Pharm to qualitatively strengthen its competitive position in the pharmaceutical market.

ABOUT THE AUTHOR(S)

Alexey Konovalov is a partner at Marrbery, where Natalia Shevchenko is a consultant; Marina Krivosheya is a senior expert; and Maria Zankovetskaya is a consultant.

The authors would like to thank the following individuals:

Kovalenko Halyna, Snitko Kyrylo, Litvinenko Vasyl, Grigorenko Anna, Melnyk Oleksandr, Savchenko Olesya, Koval Oleh, Gordienko Denis, Timchuk Bohdan, Kravka Lilia and others who helped in the process. 

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