How to fill a resource gap in your MVP with operations research

Oct 4, 2024

6 minutes

Contents

1. What is operations research?
2. Core methods of operations research
3. How to conduct the operations research
4. FAQ

For pre-seed startups, several challenges consistently emerge, such as optimising resources, managing limited capital, and improving operational efficiency. A 2023 report by Beauhurst highlights that 21% of UK startups face resource allocation and scaling issues. We’ve already learned what types of market research can help you build a valuable MVP (minimum value product).

But what about the decisions and improvements you can make to go to market without spending extra money? Here are operations research steps.

What is operations research?

Operations research (OR), often shortened to ‘Initialism OR’, is a discipline that deals with the development and application of analytical methods to improve decision-making. Occasionally, people use the term management science interchangeably.

For startups, OR is a problem-solving technique that uses data, analytics, and mathematical models to make better decisions. It optimises everything from product development to supply chain management, helping startups make more informed choices.

If you're reading this article and are only at the MVP stage, start with market research first. Read about the importance of this stage and choose the right type of research for your product.

Types of market research

Conducting market research

The importance of operations research

The main objective of conducting operations research is to rely on intuition rather than data. For example, upgrading your servers will improve the customer experience, but with OR, you will know if it's the most cost-effective solution. Similarly, young businesses often overspend on resources or overlook risks. Companies can avoid these pitfalls by using operations research models to predict various outcomes and associated costs.

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Here are the key characteristics of operations research:

Optimisation. The goal is always to find the best solution given the constraints. Example: Airlines like Delta use OR to optimise flight schedules while minimising fuel costs.
Simulation. OR uses simulations to test out solutions in a controlled environment. For example, Amazon simulates warehouse layouts to improve inventory management.
Probability and statistics. Algorithms predict future trends and manage risks. Example: Insurance companies rely on OR to predict customer claims and set premiums accordingly.

While standard analytics tools give you historical data, operations research goes further by offering predictive models and simulations that help you understand past performance and forecast potential outcomes and risks. For example, OR enables you to account for various scenarios, like how your MVP will perform during a high influx of customers or what might happen if a supply chain disruption occurs. This level of insight is invaluable for startups navigating unpredictable markets.

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Uses of operations research in business

Here are the most common directions and processes that businesses can improve by conducting operations research:

Scheduling and time management. Google optimises staff schedules across its global offices by allocating tasks and resources efficiently through operations research.
Supply chain management. Improves the flow of goods and reduces delivery times. Walmart uses OR to ensure that products reach stores on time.
Inventory management. It helps maintain the right balance between overstock and understock. Zara uses OR to predict demand and avoid overproduction.

Don’t confuse operations research with research ops. While OR focuses on mathematical and analytical techniques to solve problems, research ops deals with the organisation, management, and infrastructure of conducting research. Think of it as the behind-the-scenes setup that allows your team to gather and analyse data efficiently.

Ops research ensures you have the right processes and tools to manage ongoing user research or feedback loops for your MVP. If you need help to choose them, we are ready to help.

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Core methods of operations research

That’s how operations research can help you optimise your business strategy:

Linear Programming: Determines the best possible outcome, such as maximising profits or minimising costs, given certain constraints. For example, if you’re a startup juggling limited resources, linear programming assists in deciding the best way to allocate them.
Statistical Modeling: Analyses data trends to make predictions. Amazon, for instance, uses statistical modelling to predict customer behaviour and optimise delivery routes.
Sensitivity Analysis: Enables understanding of how different variables affect outcomes, allowing for testing of ‘what if’ scenarios. For example, you could use sensitivity analysis to assess the impact of price changes on customer acquisition and gain clarity on the effects.

Common start-up problems and OR solutions

Operations research provides solutions through mathematical models, simulations, and optimisation techniques, enabling startups from the following industries to solve their problems:

E-commerce – optimises supply chain management, inventory, and customer delivery logistics using network analysis and simulation models to improve profitability and efficiency;
Foodtech – reduces food waste, improves distribution channels, optimises ingredient sourcing, and forecasts demand using supply chain optimisation and predictive analytics;
Artificial Intelligence (AI) & Machine Learning optimise algorithm efficiency, data processing, and resource allocation with statistical analysis and optimisation techniques.
Logistics & supply chain – improves route optimisation, inventory management, and operational efficiency using simulation models and linear programming;
Fintech – manages financial risk assessments, customer acquisition strategies, and resource allocation through simulations and statistical models;
Health Tech – improves healthcare logistics, staffing, and resource utilisation using queuing theory and optimisation algorithms.

Here are some case studies of implementing OR in business development:

As technology advances, OR will evolve alongside AI and Big Data, offering even more precise solutions for complex business challenges. Such advancements will provide startups with increased opportunities to streamline operations and stay ahead of the competition.

How to conduct the operations research

Here’s the step-by-step guide essential for startups to make informed decisions on the example of launching the SaaS platform:

Identify the problem: First, pinpoint the exact issue that your startup needs to solve. Potential problems range from maximising profit with limited resources to minimising customer wait times. For example, if you notice that users experience delays in loading times, focus on optimising the user experience without increasing costs.
Build a model: Once you identify the problem, create a mathematical model that mimics real-life scenarios, incorporating all relevant variables. For example, model the interaction between your SaaS platform's server load, user traffic, and cloud infrastructure costs.
Derive solutions: Test different server configurations to determine which setup offers the best performance for the lowest cost.
Test solutions: Analyse performance in various scenarios and run simulations of peak user traffic to ensure your chosen server setup can handle it without lag.
Implement the solution: After testing, apply the solution to the real-world problem and monitor the results. Upgrade your servers based on the best-performing model and track user satisfaction.

Duration depends on the scope of research your product requires:

Small-scale research (e.g., optimisation of simple processes or specific decision-making) can take anywhere from a few weeks to 2-3 months;
Large-scale research involving complex models, multiple variables, or numerous simulations (such as supply chain optimisation or risk management) may take 3 to 12 months or more.

Complexity also affects costs. A small one can cost between £5,000 and £20,000. For more significant problems involving advanced simulations, data collection, and sophisticated software, costs could rise to £50,000 to £200,000 or more.

The ideal time to conduct operations research is after you have defined the MVP's core features but before full-scale development begins. This research will help you identify potential bottlenecks, determine the best ways to allocate resources and assess whether your operating model efficiently supports growth without wasting time or money.