by Douglas Hubbard | Mar 15, 2024 | Case Studies, Client Focus
- Client: A Global Leader in Agricultural Sciences
- Industry: Crop Science
- Objective: To forecast and prioritize new corn varieties to maximize future product success.
Executive Summary
Our client, a trailblazer in agricultural sciences, sought to gain a predictive edge in the crop science arena by being able to accurately forecast which crop varieties from their diverse portfolio would yield the most success in the upcoming years. With the help of HDR’s robust Risk Return Analysis (RRA) model, they could optimize their selection process and set the stage for groundbreaking efficiency in crop production.
Challenge:
In the dynamic field of crop science, the challenge was multi-faceted: predicting agricultural product success in an environment fraught with uncertainties such as climate change, market demand, and regulatory shifts. Our client needed a measurement and a prioritization system that could sift through the complexities and forecast the performance of prospective products in their pipeline.
Solution:
The HDR team crafted a comprehensive RRA model that integrated historical data, current market trends, and expert insights. The model enabled a data-driven approach to evaluate the myriad of potential corn varieties and isolate those with the highest potential returns, ensuring that the client’s resources were allocated to products most likely to succeed.
Results:
Our predictive model served as a crystal ball for the client, providing highly accurate forecasts that the client was able to verify against actual market data. As a result, the client was empowered to make informed decisions that enhanced their portfolio performance substantially.
Conclusion:
By leveraging the RRA model developed by HDR, our client achieved a phenomenal leap in their ability to forecast and prioritize future corn varieties, marking a new era in agricultural productivity. This strategic advantage not only propelled their research and development efforts but also reinforced their position as a visionary leader in crop science.
by Douglas Hubbard | Mar 15, 2024 | Case Studies, Client Focus
- Client: An Established Midwestern Financial Institution
- Industry: Banking
- Objective: To develop the bank’s cybersecurity framework through in-depth workshops, empowering internal teams to manage and improve their cyber risk analysis using HDR’s models.
Executive Summary
With cyber-attacks becoming increasingly sophisticated, a prominent financial institution recognized the urgent need to elevate their cybersecurity posture. They partnered with HDR to enhance their internal capabilities in identifying, assessing, and managing cyber risks. HDR’s model provided a structured and consistent framework, while their coaching ensured the bank’s team fully grasped the complexities of cybersecurity risk analysis, enabling them to independently handle their defense strategies effectively.
Challenge:
Despite having an existing cybersecurity protocol, the financial institution’s approach to risk analysis was inadequate for the increasingly dynamic threats they faced. There was a significant need to refine their strategy to quantify and manage cyber risks more effectively. The challenge lay in adopting a method that was both comprehensive and could be seamlessly integrated into their day-to-day operations.
Solution:
HDR addressed this challenge head-on by providing expert-led cybersecurity workshops tailored to the institution’s context. An HDR-crafted template version of their advanced cybersecurity model was shared, along with strategic training sessions. This enabled the bank’s team to extensively train and eventually take full ownership of their cyber risk analysis. Furthermore, HDR furnished the team with additional tools like the Lens modeling method and various estimation techniques, thoroughly equipping them to maintain robust cybersecurity independently.
Results:
The intensive training and the practical adoption of HDR’s models yielded remarkable results. The financial institution’s internal security team could now effectively identify potential threats, assess their impact, and prioritize mitigation efforts profoundly. This strategic transformation empowered the institution to safeguard its assets, customer data, and reputation more robustly than ever before.
Conclusion:
The advanced workshops and model implementation orchestrated by HDR culminated in a comprehensive boon to the financial institution’s cybersecurity measures. The strengthened defenses, coupled with the ability to conduct in-depth internal risk analyses, established the bank as a paragon of digital safety within the industry, ready to take on the future’s challenges.
by Douglas Hubbard | Mar 15, 2024 | Case Studies, Client Focus
- Client: A Global Leader in the Insurance Marketplace
- Industry: Insurance
- Objective: To enhance cybersecurity risk management by developing a comprehensive risk and control model tailored for the insurance industry.
Executive Summary
In a world where cyber threats are rapidly evolving, a pioneering insurance company sought to fortify their cybersecurity risk posture. The organization recognized the need for a robust cybersecurity risk model that would cater to their unique industry requirements. In collaboration with HDR, they embarked on a journey to dissect and categorize their cybersecurity risks into high-level macro risks and specific threats to business-critical applications, culminating in the creation of an innovative likelihood model and a NIST-based control model.
Challenge:
The insurance company grappled with categorizing and assessing cybersecurity risks in an industry plagued by sophisticated threats. The task at hand was to identify and stratify the potential risks associated with high-level macro variables and business-critical systems, and determine the probable impact on the organization, such as the number of records that could be compromised in a breach. Additionally, there was a pressing need to establish a baseline for cybersecurity measures that aligned with recognized standards.
Solution:
Addressing the complex challenge, HDR adopted a holistic approach that mapped out the insurance company’s cyber threat landscape. A detailed risk model was constructed, outlining macro risks, vulnerable business-critical applications, and establishing a likelihood of incidents. Every application was examined to estimate the potential loss of records in the event of a cyber incident. Furthermore, a foundational control model was created, drawing from NIST guidelines, to enhance the client’s cybersecurity protocols and safeguard against imminent cyber threats.
Results:
The engagement with HDR delivered a tailored cybersecurity analysis that empowered the insurance company with a nuanced understanding of their risks and provided robust mechanisms for risk management. The risk and control models developed not only met but exceeded industry standards, positioning the client to proactively tackle cybersecurity threats and protect their vast repository of sensitive information.
Conclusion:
The strategic partnership with HDR was instrumental in equipping the insurance provider with advanced tools for identifying and mitigating cybersecurity risks. The project outcomes have substantially uplifted the client’s resilience against cyberattacks, showcasing a significant leap forward in securing the company’s digital assets and maintaining their industry-leading position.
by Douglas Hubbard | Mar 15, 2024 | Case Studies, Client Focus
- Client: A Leading Silicon Valley-Based Financial Institution
- Industry: Banking
- Objective: To conduct a cost-benefit analysis for consolidating data center operations, which involves advanced Monte Carlo models and data-driven dashboards
Executive Summary
A top-tier financial institution in the competitive Silicon Valley landscape faced the challenge of updating its IT infrastructure for improved performance and cost efficiency. The project involved a detailed cost-benefit analysis of data center consolidation that leveraged Monte Carlo simulations to generate innovative data-driven dashboards.
Challenge:
Amidst an evolving digital banking landscape, the client struggled with inefficient data center operations that led to unnecessary costs and operational delays. The lack of a robust framework for financial analysis hindered their ability to forecast and quantify the benefits of IT infrastructure upgrades.
Solution:
To tackle this, a top consultancy firm crafted a detailed plan that encompassed modernizing the client’s data analysis techniques through Monte Carlo simulations and sophisticated dashboards. This enabled a holistic view of prospective costs, benefits, and risks associated with data center consolidation.
Results:
The elegant solution provided transparency and data-driven insights into the decision-making process. Post-implementation, the firm recorded a substantial improvement in operational efficiency and a marked reduction in costs, propelled by better capacity planning and resource utilization.
Conclusion:
With the new analytics framework in place, the financial institution can now make strategic decisions regarding IT investments and data center operations with greater confidence and accuracy, securing a competitive advantage in the technological forefront of the banking industry.
by Robert Weant | Mar 7, 2024 | Case Studies
Summary: By measuring how their experts price projects and price elasticity, HDR was able to build our client a pricing model that tripled their operating income.
A few years ago, a medium-sized manufacturing firm came to HDR to help redefine how they quote projects. This firm was in a business environment where their experts would give out unique quotes for custom projects of varying scale and scope. Due to the unique nature of each project, the experts at the firm lacked generalizable data to inform consistent pricing decisions. The firm instead relied on a combination of expert intuition and project cost estimates when quoting prices for a project.
As a senior quantitative analyst at HDR, I was tasked with modeling their existing pricing structure to identify areas for improved consistency and optimization. Using the following high-level steps resulted in margins increasing by 48% and operating income tripling within 18 months of implementing our models.
- Model How they Currently Price Project Quotes
- Model Price Elasticity
- Model Constraints and Optimize Portfolio
Creating a Baseline Using the Lens Method
At HDR we often hear “We simply don’t have enough data.” or” We face too many unique factors.” from clients when describing why they have difficulty quantifying fundamental aspects of their business. Yet there are certain methods we routinely use that can address these perceived limitations, all of which are illusions. One method that addresses these limitations is the Lens Method.
The Lens Method is a regression-based approach that has been used in different industries for decades and has been shown to measurably reduce inconsistency in forecasting models. To develop a lens Model, HDR works with clients to define and identify potential factors that may affect key output metrics. After the factors (or attributes) have been identified, we then generate hundreds of hypothetical scenarios leveraging different combinations of variables for experts to review and estimate the key metric we are trying to measure. In this client’s case, the key metrics we were attempting to measure were the price they would quote to the client and the probability of winning the project. We created 150 hypothetical scenarios that contained factors that our client would consider when quoting a project.
After gathering their responses to these scenarios, we were able to generate a model that would predict how the expert would price a potential project and what probability of winning the project they would assign. What’s more, the model of the experts would outperform the experts themselves. This is due to human judgment being very susceptible to noise. These lens models were used as a basis for our client when pricing potential projects and estimating the probability of winning the project.
Measuring Price Elasticity
The next phase of the work was focused on measuring price elasticity or bid-price sensitivity. In other words, to answer the question: How does a 10% increase in quoted price affect the probability of winning the project P(Win)?
To accomplish this, we created a controlled experiment where our client would purposely deviate from the lens model price by a fixed percentage. This allowed us to create models that measured price elasticity for different markets. Certain markets may face different competitive environments which leads to different price elasticities. A 10% increase in the bid price in a market with limited competition will have a smaller decline in P(Win) than in a market with many competitors.
Measuring the relationship between price and P(Win) or quantity sets up a classic economics problem that every economics student learns through college. “Find the price that maximizes the expected profit.” Expected profit in my client’s case is simply profit from the project times the P(Win). However, for our client, and most firms in the real world, there exist extra constraints to consider such as regulatory restrictions, reputation damage, and relationship to other projects in the portfolio.
Figure 1: Illustration of classic price optimization problem.
Modeling Constraints and Optimizing Prices for the Entire Portfolio of Projects
Like many firms in the manufacturing industry, our client cannot rapidly increase production based on short-term increases in market demand. It takes significant investments in capital and human labor to expand their capacity. Simply optimizing each price for each quote, as illustrated in Figure 1, may lead to issues of overcapacity.
While having “too much” business is a good problem to have, in our client’s case it would lead to increased labor costs as they are forced to pay for overtime and may incur longer timescales for delivery which would hurt their reputation and lead to less revenue in the future.
To account for this, we not only had to consider the direct cost of a project but also the opportunity cost of a project. Different projects can have different margins. We wanted to use pricing as a tool to ensure our client’s limited capacity would be filled with the most profitable projects first and then filled up with the less profitable projects to fill up the remaining capacity.
Taking the opportunity cost into consideration, can affect what the optimal price should be for a project. If a project is less profitable than most others in a portfolio, then the optimal price will shift to the right compared to what it should be when measured in isolation. (Figure 2).
Figure 2: Optimal price with and without opportunity cost. Note this chart is purely for illustrative purposes and was not based on actual data from our client.
Ends Results and Applications for Other Organizations
Near the beginning of this year, this client contacted HDR to inform us of how satisfied they were with the models we developed and their performance. The client estimated optimization models had increased their margins by 48% and “tripled their operating income in 2023.” This resulted in a very high ROI on hiring HDR.
As impressive as these results were from this client, they are certainly not unique. Time and time again, we have found that quantifying all aspects of important decisions leads to different decisions being made and, ultimately, better financial outcomes It is not uncommon for us to see the models we develop for our clients cause them to make different decisions or prioritize different measurements that end up saving or earning them a magnitude more than the amount they hired us for.
Some of the projects and methods used with this client we routinely use for projects involving cybersecurity, enterprise risk, military fuel costs, goodwill investments, ESG factors, and many others. If your organization is having difficulty quantifying essential items that affect your decision-making, please feel free to contact us.
