Mathematical modelling is a powerful way to understand and solve problems from the real world using the language and tools of mathematics.
Let's start with an intuitive understanding. Imagine you want to predict how long it will take you to travel from your home to your school. This is a real-world problem. How would you approach it?
You'd probably think about:
The distance: How far is your school from your home? Let's say it's 10 km.
Your speed: How fast do you usually travel? If you walk, maybe 5 km/h. If you cycle, maybe 15 km/h. Let's assume you cycle at a constant speed of 15 km/h.
Assumptions: You're assuming you travel at a constant speed, without stopping, and the path is direct.
Now, you can use a simple mathematical relationship:
Time=SpeedDistance
Plugging in your values:
Time=15 km/h10 km=32 hours
Converting this to minutes:
32×60 minutes=40 minutes
So, you predict it will take you 40 minutes.
What you just did is a basic form of mathematical modelling. You took a real-world situation (travel to school), identified key factors (distance, speed), made some simplifying assumptions (constant speed, no stops), translated these into a mathematical formula, solved the formula, and then interpreted the mathematical answer back into a real-world prediction.
Now, for a precise statement:
Mathematical modelling is the process of representing real-world situations or problems using mathematical concepts, techniques, and language. It involves translating a problem from its real-world context into a mathematical formulation, solving the resulting mathematical problem, and then interpreting the mathematical solution back into the context of the original real-world problem.
The goal of mathematical modelling is to gain insights, make predictions, or aid in decision-making regarding the real-world phenomenon being studied.
The process of mathematical modelling typically involves several key steps:
Formulation of the Problem:
Clearly understand the real-world problem.
Identify the relevant variables (quantities that can change) and parameters (quantities that are fixed for a given problem).
Make simplifying assumptions to make the problem manageable. For example, in the travel problem, we assumed constant speed and no stops.
Define the objectives of the model (what do we want to predict or understand?).
Development of the Mathematical Model:
Translate the identified variables, parameters, and relationships into mathematical terms. This often involves using equations, inequalities, functions, graphs, or other mathematical structures.
For instance, if we are modelling population growth, we might use a differential equation like dtdP=rP, where P is population, t is time, and r is the growth rate.
Solving the Mathematical Model:
Use appropriate mathematical techniques to solve the formulated mathematical problem. This could involve algebraic manipulation, calculus, numerical methods, statistical analysis, or computational tools.
The solution provides mathematical results, such as values for variables, optimal conditions, or predictions.
Interpretation of the Solution:
Translate the mathematical solution back into the context of the original real-world problem.
Explain what the mathematical results mean in practical terms. For example, if the model predicts a certain value, what does that value represent in the real world?
Validation and Refinement:
Compare the model's predictions or outcomes with real-world data or observations.
If the model's predictions are accurate and consistent with reality, it is considered valid.
If there are significant discrepancies, the model needs to be refined. This might involve revisiting the initial assumptions, adding more variables, or using a different mathematical approach. This step often leads back to step 1 or 2, making modelling an iterative process.
Important
Mathematical modelling is an iterative process. It's rarely perfect on the first attempt. Models are often refined and improved as more data becomes available or as our understanding of the real-world system deepens.
Mathematical modelling is used across almost all fields, from predicting weather patterns and the spread of diseases to designing aircraft, managing financial markets, and optimizing logistics. It provides a structured way to approach complex problems and make informed decisions.
Compute AB; its column sums exceed stock, so the orders cannot be met as placed.
Column sums (total demand): R1=335, R2=467, R3=147 — each above the stock 330,455,140. Trimming to A1=(910122060) gives sums 311,436,138, within stock.
✓Final answer
AB=(1651702472208760); demand 335,467,147 exceeds stock 330,455,140, so the orders cannot be met as placed (reduced orders A1 need only 311,436,138).
Multiplying AB gives (1651702472208760). The two orders together demand 335 units of R1, 467 of R2 and 147 of R3 — more than the 330,455,140 in stock — so the orders cannot be met as placed. Reducing the orders (e.g. to A1) brings the demand within stock.
Step 1 — Identify. Substitute the given numbers into the Example 2 model to see whether the firm's stock can fill both orders.
Step 2 — Set up. Here row Fi of A is client i's order in products P1,P2,P3; B gives the raw material R1,R2,R3 per unit of each product; stock is R1=330,R2=455,R3=140.
Step 3 — Formulate. The raw material required is AB (clients × raw materials).
Step 5 — Interpret and validate. The demand 335,467,147 exceeds the stock 330,455,140 in every raw material, so both orders cannot be filled as they stand. Since the recipe B is fixed, the firm must either buy more raw material or have the clients cut their orders. For instance, replacing A by the reduced orders
A1=(910122060)
gives A1B=(1411702162207860), whose column sums 311,436,138 lie comfortably below stock — so the trimmed orders can be supplied.
✓Final answer
AB=(1651702472208760); the orders need 335 units of R1, 467 of R2 and 147 of R3, which exceed the available 330,455,140, so they cannot be met as placed. With reduced orders A1 the requirement drops to 311,436,138 — within stock.
Method: Computing and Interpreting a Matrix Product Against a Resource Constraint
This method applies once the general orders-times-recipe model has been set up and specific numerical matrices are given.
Steps
Step 1: Confirm the matrices multiply in the intended order
Check the given A (clients × products) and B (products × raw materials) share the "products" dimension, so AB is defined and gives a clients × raw-materials matrix.
Step 2: Compute each entry of AB by row-times-column
For each client row i and raw-material column k, compute ∑jAijBjk — multiply matching entries and add. Do this systematically, one row of A at a time.
Step 3: Sum the columns of AB to get total demand
Add down each column of the resulting matrix to get the total amount of each raw material needed across all clients combined — this is what must be compared against stock.
Step 4: Compare total demand with the available stock
Check each raw material's total demand against the number given as available. If any demand figure exceeds its stock, the orders as placed cannot be fulfilled.
Step 5: If needed, test a reduced order against stock
If the original orders exceed stock, repeat Steps 2-3 with a trimmed orders matrix to see whether the reduced demand now fits within the available stock.
Applying to this problem: compute AB=(1651702472208760), sum columns to get demand 335,467,147, compare against stock 330,455,140 — every raw material is short, so the orders cannot be met as placed; the reduced order A1 brings demand down to 311,436,138, within stock.
Common Mistakes
Mistake 1: Arithmetic slip in one entry of the matrix product
Why it's wrong: Each entry of AB requires three separate multiplications added together (since the shared dimension is 3); a single multiplication or addition error in one entry throws off both that client's requirement and the column total used for the stock comparison. Correct approach: compute each entry of AB as a clearly written sum (e.g. 10⋅3+15⋅7+6⋅5) rather than mental arithmetic, and double check each term.
Mistake 2: Comparing a row total instead of a column total against stock
Why it's wrong: A row of AB gives one client's requirement for all three raw materials, but the stock is a single pool shared by both clients — so it's the column sums (total demand per raw material, across all clients) that must be compared to stock, not any individual row. Correct approach: sum each column of AB first, then compare that sum to the stock figure for that raw material.
Mistake 3: Concluding "orders can be met" from only some raw materials being sufficient
Why it's wrong: All required raw materials must be available simultaneously — if even one raw material's demand exceeds its stock, the orders as a whole cannot be fulfilled, regardless of whether the other two are within stock. Correct approach: check every raw material's demand against its stock individually, and the orders fail if any one of them is insufficient.