Raw Materials Optimization for Food Manufacturing with Python
Use linear programming to create an optimal recipe for a cheap meal bar that meets specific nutritional requirements.
Ensuring that products are cost-effective and meet strict nutritional standards is a constant challenge in food manufacturing.
Creating a meal bar that satisfies budget constraints while providing essential nutrients requires carefully selecting raw materials.
This process involves balancing various factors, from ingredient cost to nutritional content, to achieve the desired outcome.
Can advanced tools like Python and linear programming simplify this complex task?
This article will explore how to leverage Python and linear programming to design an optimal recipe.
Summary
I. Optimize Raw Materials for Food Manufacturing with Python
II. Problem Statement for Raw Materials Optimization
1. Available Ingredients and Nutritional Requirements
2. Optimal Recipes for Meeting Nutritional Requirements
3. Using Data-Driven Models for Better Business Decisions
III. Building Your Raw Materials Optimization Model
1. Available Ingredients and Nutritional Requirements
2. Declare your variables and parameters for your model
3. Define the objective and add constraints
4. Solve your model and analyze the results
IV. Conclusions
1. Next Steps
2. Generative AI: GPT for Process OptimizationOptimize Raw Materials for Food Manufacturing with Python
As a data scientist, you need to support the R&D manager of a large food manufacturer.
She must design a recipe for a new cheap and healthy meal bar.
What do we want to achieve?
Meal bars must function as a meal rather than just a snack to fill in the cracks.
We want to minimize the cost of production while respecting nutritional constraints.
Can we use analytics tools to automate the design of this recipe?
This is a Linear Programming problem statement with objectives and constraints.
This article will show how to build a model to design this optimal recipe.
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Problem Statement: Raw Materials Optimization
Before moving to the model design, we must clearly state the problem.
The factory can source raw materials from multiple suppliers; our R&D manager has shortlisted seven ingredients.
Parameters: Ingredients and Nutritional Requirements
The seven available ingredients are
- Meat: Chicken, Beef, Mutton
- Non-Meat: Rice, Corn, Wheat bran, Peanuts
These ingredients have different nutrition facts (in grams) per gram
What about costs?
They have also different costs ($/gram)
Let’s formulate the problem now.
Objective
Minimize the total cost per bar (Weight: 120g).
Constraints
Minimal values for protein and fibre
- Protein: 20g
- Fibre: 6g
Maximum values for fat, salt and sugar
- Fat: 22g
- Salt: 3g
- Sugar: 30g
Simulate Scenarios with Linear Programming
Now that we have the objective and the constraints, we can test our optimization model with multiple scenarios.
What if we want to minimize the unit cost of production?
- Scenario 1
Cost per Bar = 7.91 $Qty_Beef = 48.56 g
Qty_Chicken = 0.0 g
Qty_Corn = 0.0 g
Qty_Mutton = 0.0 g
Qty_Peanuts = 34.09 g
Qty_Rice = 0.0 g
Qty_Wheat_bran = 37.36 g
You only need to add beef, peanuts, and wheat bran for $7.91 per bar (120 g).
What is the main cost driver?
Beef drives the costs up to meet the protein constraint.
What if we reduce this constraint?
- Scenario 2: reduce the protein level to 12 g
Cost per Bar = 0.78 $Status: Optimal
Qty_Beef = 0.0 g
Qty_Chicken = 0.0 g
Qty_Corn = 0.0 g
Qty_Mutton = 0.0 g
Qty_Peanuts = 43.15 g
Qty_Rice = 55.19 g
Qty_Wheat_bran = 21.66 g
Your bar is now purely vegan and relatively cheap.
However, the marketing did not validate this recipe.
They suggested keeping the same level of protein and reducing sugar.
Would it be more expensive?
- Scenario 3: reduce the sugar to 20 g
Cost per Bar = 10.32 $Status: Optimal
Qty_Beef = 65.32 g
Qty_Chicken = 0.0 g
Qty_Corn = 0.0 g
Qty_Mutton = 0.0 g
Qty_Peanuts = 30.96 g
Qty_Rice = 0.0 g
Qty_Wheat_bran = 23.72 g
Yes.
You must use more meat to maintain protein consistency and ensure a low sugar level.
Therefore, you will see an increase in your cost per bar.
Using Data-Driven Models for Better Business Decisions
This simulation model shows how such tools can enhance strategic discussions and support data-driven decision-making.
As we will explore in the next section, complex simulations can be executed with a single click, enabling you to answer critical business questions with data.
Remember, figures never lie, but people do.
This kind of model can support any type of strategy discussion by comparing different scenarios using business KPIs.
For example, I introduced a model designed to help a friend maximize its revenue in another article.
What is the best pricing approach to maximize profit?
He had harsh discussions with his business partner, who had different opinions on improving their pricing to increase sales.
This model, developed with Python, can run multiple scenarios with different pricing strategies.
This led to a smoother decision-making process and facilitated the collaboration between associates.
For more details about it, 👇
Now that the value convinces you of this kind of model, let’s see how to build it.
If you prefer watching, have a look at the video version of this article
Building Your Raw Materials Optimization Model
Pulp is a Python modelling framework for linear (LP) and Integer Programming (IP) problems.
It is very easy to use and can help you solve many problems.
How can we load the parameters?
Available Ingredients and Nutritional Requirements
You can also find these datasets in my GitHub repository.
Nutrition facts
Ingredients Costs
Let’s start by declaring the variables of the model.
Declare your variables and parameters for your model
- LpMinimize: your objective is to minimize the cost of your bar
- lowBound =0: you cannot have a negative value of ingredient quantity
We want to minimize the cost while respecting nutritional constraints.
Define the objective and add constraints
Solve your model and analyze the results
You have the entire workflow with the results printed in this script.
Find the complete code in this GitHub repository,
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Conclusions
This solution could be better; you can quickly improve it.
What if your customers want corn?
You can add corn to this recipe by constraining the minimum quantity of corn.
Do we have infinite possibilities?
Let us try to change the quantity from 120 g to 100 g. What is the result?
Cost per Bar = 11.08 $Status: Infeasible
Qty_Beef = 71.27 g
Qty_Chicken = 0.0 g
Qty_Corn = 0.0 g
Qty_Mutton = 0.0 g
Qty_Peanuts = 30.26 g
Qty_Rice = 0.0 g
Qty_Wheat_bran = -1.53 gFor this combination of constraints, there is no optimal solution for a bar of 100g.
Have you heard about Generative AI?
Generative AI: Optimization Models x GPT
When I saw the trend of Generative AI with large language models (LLMs), I took the opportunity to experiment with their impact on user experience.
What’s more boring than a static dashboard?
My first exercise was designing a LangChain Agent powered by GPT and connected to a TMS to act as a Supply Chain Control Tower.
This solution was quite satisfying as it could answer operational questions autonomously by querying a database and extracting the results.
User: What if we want a minimum of 10g of corn?
GPT: This increases the price by 15%.
This can be implemented with this model to improve the user experience.
Instead of relying on a data scientist, users can ask the agent to modify the model based on the scenario they want to test.
For more details on how to implement this kind of solution, 👇
About Me
Let’s connect on Linkedin and Twitter. I am a Supply Chain Engineer who uses data analytics to improve logistics operations and reduce costs.
If you need consulting or advice on analytics and sustainable supply chain transformation, feel free to contact me through Logigreen Consulting.
If you are interested in Data Analytics and Supply Chain, look at my website.
References
- Computational Infrastructure for Operations Research, Optimization with PuLP (Documentation), Link
