diff --git a/cases/case_1/case_1_description.md b/cases/case_1/case_1_description.md new file mode 100644 index 0000000..004aab3 --- /dev/null +++ b/cases/case_1/case_1_description.md @@ -0,0 +1,190 @@ +# Case 1: Simulating Stock Policies + +- Title: Choosing stock policies under uncertainty +- Description: Students role-play their participation as consultants in a + project for Beanie Limited, a coffee beans roasting company. Elisa, the + regional manager for the italian region, is not happy with their inventory + policies for raw beans. The students are asked to analyse the problems posed + by Elisa and apply simulation techniques, together with real data, to + recommend a stock policy for the company's warehouse in the italian region. + Python notebooks with some helpful prepared functions are provided to the + students. The final delivery is a report with their recommendation to the + client company, along with the used code. + +Stuff I want them to understand: + +- The model/hypothesis/validate +- That in a simulation you set parameters, and you observe results +- To write in a problem-solving manner. +- That there are trade-offs and it's not trivial to find optimal solutions. + +Elements of the simulation: + +- Demand behavior +- Lead time and standard deviation of provider (or providers) +- Service level +- Punishment for sales lost + +Observable effects of policies: + +- Mean inventory at hand +- Service level +- Warehousing/Capital Cost +- Lost sales cost + +# Case 1: Choosing ordering goods under uncertainty + +You are part of an expert simulation team in SimiUPF SL. You have been assigned +to a new project with a client company, Beanie Limited. Beanie Limited is a +coffee roasting company and also distributes raw coffee beans through Europe +and Middle East. + +Specifically, you will be working for Elisa Bolzano, the Director of Beanie +Limited's warehouse located in Caserta, near Naples. Elisa is the full +responsible for all the operations in the warehouse. She has requested the help +of the SimiUPF team because she is worried about how certain things are managed +in the warehouse and wants your help. + +The Caserta warehouse serves the raw coffee beans distribution business of +Beanie Limited in southern europe and the mediterranean. The warehouse and its +team are responsible for serving clients and also other smaller regional +warehouses from Beanie Limited in this geography. From the warehouse point of +view, they are usually just called "the clients". Whenever one of the clients +needes raw beans, they arrange a transport truck that goes to the warehouse to +pick up a certain amount of goods. Elisa's team fill up the truck with the +requested goods, and then the clients take care of receiving that in their own +locations. + +The Caserta warehouse itself has only one way to source coffee beans to store +in their warehouse: requesting them to the Beanie Limited central offices in +Diemen, near Amsterdam. Whenever Elisa's team considers that more stock is +needed, they post a sourcing order to the central office for a certain amount +of beans. The central office arranges the goods and the delivery and, after a +few days, the goods reach Caserta and are stored. The central office tries to +ensure a lead time of 7 days (lead time is the time that passess between an +order being placed and the goods reaching their destination), but the reality +is they do what they can and this time is not always respected. + +Stock is a necessary evil (it implies a lot of cost), but Elisa's warehouse +plays a key role in serving the clients in their region properly. Having too +little stock means the clients need to wait long times to get their goods, +which is risky for the business. On the other hand, having a lot of stock means +high warehouse costs and financial opportunity cost (if Beanie Limited has 1 +million € in coffee beans in a warehouse, that is 1 million € they can't invest +somewhere else to improve their business). Thus, Elisa needs the stock to be as +small as possible, without disappointing clients. + +Elisa is calling you because 2021 was a terrible year for the warehouse. The +year was a chaotic one, and Elisa's team was not able to run operations +smoothly. Although Elisa is not providing exact numbers, she is very well aware +that the warehouse stock was unnecessarily high at times, and that there were +too many periods were the warehouse was out of stock and clients had to wait to +get their goods. + +Elisa thinks that the main reason for this is the lack of a clear policy for +when to order and how much to order from Diemen. Her team decides independently +when to do it, and Elisa has a feeling that they are not approaching these +decisions the right way. This means that sometimes they order when there is no +need to, sometimes they don't order when they should be, and that the amounts +being ordered might not be the best ones. + +Here is where you come in. As simulation experts, Elisa expects from you that +you can help design an ordering policy to fix these issues. Doing this implies +examining data from last year, building a proper simulation to examine the +different factors being involved, and deciding when and how should Elisa's team +order more goods from Diemen. + +Elisa expects a report where you share your findings and recommendations in a +clear way that can help her team. Also, Elisa does not trust you blindly: you +need to motivate the reasoning behind your recommendations. Otherwise, she will +not feel comfortable implementing your recommendations and the bosses at +SimiUPF will be mad at you... + +## Detailed task definition + +- Below you will find four levels of questions. Levels 1 to 3 are compulsory. + Level 4 is optional. +- You need to write a report document where you answer the questions of the + different levels. This report should be directed towards Elisa, should give + her clear recommendations and should justify these recommendations. +- Each level is worth 2 points out of a total of 10. The 2 missing points will + grade the clearness and structure of your report. +- You need to use Python notebooks to solve all levels. A helper notebook is + provided. For each level, please attach a notebook that shows your + solution/proposal/analysis. + +## Data + +- You are provided with three tables that contain real data from 2021. + - demand_events: this table shows how many beans left the Caserta warehouse + to serve clients. There is some amount leaving every day because the + warehouse serves many small orders from small clients, so there is always + some order being fulfilled. The amount is measured in kilograms, and + represents the total amount that left during that day. + - sourcing_events: this table shows the beans orders that Elisa's team + placed to Diemen. For each order, there are two dates: the date when + Elisa's team placed the order, and the date where the beans actually + reached the Caserta warehouse. The amount is measured in kilograms. + - stock_state: this table shows the stock at the warehouse at the end of + each date. As you can guess, the stock for a certain date is the stock of + the previous day, plus the goods that reached Caserta coming from Diemen, + minus the goods that left the warehouse to serve client orders. A + negative stock is not a challenge to the laws of physics: it means + clients are waiting for their requested beans. If one row shows -1.000, + it means that the warehouse is empty, and clients are awaiting for a + total amount of 1.000 kgs of beans. If next morning, a 1.000 kgs reach + Caserta from Diemen, those will be used immediately to satisfy those + waiting clients, and the warehouse stock will become 0. + +## Notebook + +A notebook with some helping code has been provided. The code contains a small +simulation engine that can help you simulate a year of activity for the +warehouse. The instructions on how to use the code are in the notebook itself. + +## Levels + +- Level 1 + - Elisa wants you to measure the performance of the last year, providing + quantitative metrics. She knows it was a bad year, but hasn't looked at + the real data to summarize how bad it was. Remember that there is a + trade-off: + too much stock, is not desired, but running out of stock and making + clients wait is also negative. + - Going one step further, Elisa wants to know: what was done wrong? +- Level 2 + - Elisa wants you to propose an ordering policy. This means, that you need + to define a rule that, once each day, should answer the questions: should + be place an order to request material today? If yes, how much should we + order? + - Use simulation to present metrics on what is the expected performance + with the policy you are proposing. Remember, you need to convince Elisa + that this is better than what happens today. + - As a specific constraint, Elisa explains that she wants that the + probability of a stockout is at most of 5%. + - +- Level 3 + - Right after you finished designing your policy for level 2, Elisa called + with some news: she has just been informed by the management in Diemen + that a new Minimum Order Quantity (MOQ) rule will begin soon. This rule + means that, when the Caserta warehouse places an order to request + material from, the order should be of at least 500,000 kgs of beans, and + not less than that. + - Elisa wants you to take this into account. Does it affect the policy you + proposed for level 2? If so, you need to come up with a new one that + adapts to this rule. +- Level 4 + - Elisa briefly discussed with you in one meeting that there is an option + to come to an agreement with the team in Diemen to improve the lead time + stability. The proposal from Diemen is that, if the target lead time was + set to something higher that the current 7 days target, providing a more + stable delivery would be feasible. + - The specific proposal from Diemen is: if the lead time target is changed + to 15 days, they provide a 100% guarantee that orders will be delivered + in exactly 15 days. + - Elisa would love if you could take some additional time to study this + proposal. What is better for Caserta? The current 7 days target + lead-time, with unstable deliveries? Or a fixed, 15-day lead time? + - The MOQ rule of level 3 still applies. + + diff --git a/cases/case_1/demand_events.csv b/cases/case_1/demand_events.csv new file mode 100644 index 0000000..c85dc86 --- /dev/null +++ b/cases/case_1/demand_events.csv @@ -0,0 +1,366 @@ +date,demand_quantity +2021-01-01,54609.49281314914 +2021-01-02,36208.63648649295 +2021-01-03,77784.17276763407 +2021-01-04,76481.81360421646 +2021-01-05,52305.87658918292 +2021-01-06,57098.56436860317 +2021-01-07,41565.68706138541 +2021-01-08,81995.500619844 +2021-01-09,71041.91466404148 +2021-01-10,31787.17080818402 +2021-01-11,32735.09633866546 +2021-01-12,32855.44553254065 +2021-01-13,55420.934082626205 +2021-01-14,48883.311263507494 +2021-01-15,48368.597773147136 +2021-01-16,40225.99478591274 +2021-01-17,69003.66723779934 +2021-01-18,67378.93368511106 +2021-01-19,59444.432628854185 +2021-01-20,54441.80415596864 +2021-01-21,52796.814721541414 +2021-01-22,30193.150803735854 +2021-01-23,62328.53756562836 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of this course is to provide an introduction to simulation, +optimization and machine learning techniques to students with a background in +social sciences, with an approach biased towards practical work. The expected +outcome is that students that have passed this course know a variety of modern +and useful techniques that can be applied in real-life business contexts. With +this knowledge and experience, the students understand what are the right +techniques for different problems, which are the main steps and requirements to +apply each of these techniques and how to judge the successful application of +them. + +Many of the techniques taught in this course are usually taught to engineering +and technical profiles. This course does not aim to bring students to the same +level of technical expertise as their engineering counterparts, but rather to +provide enough background so that the students can successfully interact with +such profiles. Having said that, this course can also be a first introduction +for students that are willing to pursue a more thorough learning of the +techniques discussed in the course, after or during itself. + +With the knowledge and skills obtained in this course, students become fit for +tasks such as: + +- Applying simulation, optimization and machine learning techniques to simple + cases. +- Planning and designing simulation, optimization and machine learning + initiatives. +- Leading simulation, optimization and machine learning projects from a + managerial point of view. +- Acting as a liaison between management and technical profiles in business + contexts. + +## Pre-requisities + +The course assumes the student has covered Mathematics I, II, III courses and +the Probability & Statistics course. Passing this course is not impossible if +that is not the case, but the student should expect a non-trivial challenge +ahead. + +Knowledge of the following topics will help students better leverage this +course, but is not strictly required: + +- Basic programming, specially in data oriented languages such as Python or R. +- Operations research + +## Teaching method and contents + +The course will have lecture classes and practical seminars. Classes start on +April 7th. + +There will be 20 lecture classes and 6 practical seminars. For the practical +seminars, students will be divided into two groups with independent sessions to +reduce the class size. The practical seminars will be used to assist students +in their work in the three mandatory case assignments that students will do +throughout the course. + +Students are expected to attend all the activities in the course. Beyond +lectures and practical seminars, additional reading resources will be provided +to students. For students that need to level up their Python skills, self-paced +materials will be suggested. + +Lectures will have the following contents: + +| Week | Classes | Student work | +|------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| 1 | - L1: Introduction and motivation of the course
- L2: Simulation, Optimization and Machine Learning in companies | - Python prep | +| 2 | - L3: Introduction to simulation: What is it, When do we use it, Types of simulation
- L4: Simulation examples in Python. Introduction to case 1. | - Python prep
- View [Primer: Simulating a pandemic](https://www.youtube.com/watch?v=7OLpKqTriio)
- Read [Agent-based modeling: Methods and techniques for simulating human systems](https://www.pnas.org/content/99/suppl_3/7280)
- Read case 1. | +| 3 | - L5: Simulation methodology.
- L6: Simulation-based optimization I. Challenges and issues with simulation. Where to go from here
- S1: Workshop for case 1 | - Work on case 1
- Review [HASH model market simulation](https://hash.ai/@hash/model-market-python)
- Review [HASH warehouse simulation](https://hash.ai/@hash/warehouse-logistics) | +| 4 | - L7: Introduction to optimization
- L8: Modeling optimization problems
- S2: Workshop for case 1 | - Work on case 1
- Read Gurobi's [Modelling Basics](https://www.gurobi.com/resource/modeling-basics/)
- Read Neos [taxonomy of optimization problems](https://neos-guide.org/optimization-tree)
- View this video on the [Simplex algorithm](https://www.youtube.com/watch?v=RO5477EKlXE) | +| 5 | - L9: Taxonomy of optimization techniques
- L10: Simulation-based optimization II. Introduction to case 2 | - Deliver case 1
- Read case 2
- Enjoy watching [simulation-based race car training](https://www.youtube.com/watch?v=-sg-GgoFCP0)
- Read how the [4th most popular database software in the world uses GAs to access data faster.](https://www.postgresql.org/docs/8.0/geqo-intro2.html) | +| 6 | - L11: Challenges in real-world usage. Simulation vs Optimization
- L12: Introduction to Machine Learning
- S3: Workshop for case 2 | - Work on case 2
- Read this [review on simulation optimization techniques and softwares](https://arxiv.org/pdf/1706.08591.pdf) | +| 7 | - L13: Supervised Machine Learning (SML): NIPS
- L14: Typical SML workflow. Introduction to case 3
- S4: Workshop for case 2 | - Work on case 2
- Read case 3 | +| 8 | - L15: Algorithm deep dive: Decision trees
- L16: Feature Engineering and Model Evaluation
- S5: Workshop for case 3 | - Deliver case 2
- View this [intro to neural networks](https://www.youtube.com/watch?v=aircAruvnKk&t=10s) and this [intro to random forests](https://www.youtube.com/watch?v=J4Wdy0Wc_xQ) | +| 9 | - L17: Deployment of Models
- L18: Stories from the trenches: applying all of this in the real world
- S6: Workshop for case 3 | - Work on case 3
- View this video on [why businesses fail at ML](https://www.youtube.com/watch?v=dRJGyhS6gA0) | +| 10 | - L19: Where to go from here: further learning and carreer advice
- L20: Final Q&A, exam preparation | - Work on case 3 | +| 11 | - Exam | - Deliver case 3 | | | + +- Lecture 1 INTRO + - Introduction to the course + - Citizenship rules + - Won't force you to come, but I advice you to. + - I'll always try to start 5min late, finish 5min late, and stop + for 5min. + - You can come and go, just please be respectful. + + - Calendar + - Contents + - Expectations + - The teacher + - Evaluation + - Contact + - Questions? + - The relevance of math and computers in management + - Examples: pricing, logistics, staffing. + - The skills and profiles required + - The tools used +- Lecture 2 INTRO + - The techniques we will see in the course + - Simulation + - Optimization + - Supervised machine learning (aka "prediction") + - Why this stuff is important +- Lecture 3 SIM + - A humbling example + - What is simulation and when do we use it + - Different types of simulations +- Lecture 4 SIM + - Toy simulations in Python + - How to approach simulation in practical terms + - Tools in industry +- Lecture 5 SIM + - Theoretical background on simulation + - Present case 1 +- Lecture 6 SIM + - Simulation-based optimization + - Where to go from here +- Lecture 7 OPT + - What is optimization + - A trivial example +- Lecture 8 OPT + - Different optimization techniques + - Present case 3 +- Lecture 9 OPT + - How to model optimization problems (target functions, decision variables + and constraints) +- Lecture 10 OPT + - Simulation-based optimization: Genetic algorithms +- Lecture 11 OPT + - Real world challenges and optimization deployment +- Lecture 12 ML + - Good news, you already know Machine Learning + - Different branches of Machine Learning + - Real world examples of applications +- Lecture 13 ML + - How does Supervised Machine Learning work? + - Present case 2 +- Lecture 14 ML + - The Machine Learning workflow (EDA, Feature Engineering, Model + Evaluation, Deployment) +- Lecture 15 ML + - Feature Engineering +- Lecture 16 ML + - Model evaluation +- Lecture 17 ML + - Deployment and real world challenges +- Lecture 18 Real life stories from the trenches +- Lecture 19 Real life stories from the trenches +- Lecture 20 + - Q&A pre-exam + - Feedback on the course + +## Case details + +Case 1 + +- Title: Choosing stock policies under uncertainty +- Description: Students role-play their participation as consultants in a + project for Beanie Limited, a coffee beans roasting company. Elisa, the + regional manager for the italian region, is not happy with their inventory + policies for raw beans. The students are asked to analyse the problems posed + by Elisa and apply simulation techniques, together with real data, to + recommend a stock policy for the company's warehouse in the italian region. + Python notebooks with some helpful prepared functions are provided to the + students. The final delivery is a report with their recommendation to the + client company, along with the used code. + +Case 2 candidate + +- Title: ? +- Description: ? +- Sample idea: https://www.gurobi.com/resource/facility-location-problem/ + +Case 3 + +- Title: Improving last-mile scheduling with Machine Learning +- Description: Students role-play their participation as consultants in a + project for Beanie Limited, a coffee beans roasting company. Pieter, the + director of secondary transportation, has requested help from the student + consultants. One of the key activities in Pieter's team is the daily + scheduling, where the different trucks get assigned which deliveries and + routes will perform. The students are asked to develop a machine-learning + algorithm to predict the drop-time for each delivery (the drop-time is the + time a driver takes in unloading the goods in a a client location. More + informally, the time that passes since he removes the key from the truck + until he starts the engine again). The goal is to provide more advanced + information for Pieter's schedulers so they can better plan the routes of + their drivers. The students are asked to build and deliver a Machine Learning + algorithm that predicts this time. The students will be provided a labelled + dataset. The final delivery is the working prediction model, along with a + report explaining their methodology in building it, and answering some + business questions to the client company. + +## Grading + +The following items compose the final grade: + +- Case assignments: 50% of the grade. There will be three assignments, each + with the same weight. The average grade of the assignments must be of 5 or + more to pass the course. +- Final exam: 40% of the grade. There will be a final exam at the end of the + course. The grade must be of 5 or more to pass the course. +- Something else? 10%. + +A final grade is calculated as: + + + +```python +if avg(case1_grade, case2_grade, case3_grade) < 5: + passed_course = False +if final_exam_grade < 5: + passed course = False + +passed_course = True +final_grade = (avg(case1_grade, case2_grade, case3_grade) + final_exam_grade) / 2 +``` + + + +## Bibliography + +All compulsory and required materials will be provided during the course. + +A good book that follows the approach of this course is "Guttag, John. +Introduction to Computation and Programming Using Python: With Application to +Understanding Data. 2nd ed. MIT Press, 2016. ISBN: 9780262529624", used in the +homonymous course at MIT. It is not compulsory to use this book, but some +students might find it helpful. + +Additional specific readings will be provided throughout the course. Students +will be requested to read some of these materials in advance of some sessions. + +For students that want to dive deeper in the topics covered in the course, the +following books are recommended: + +- On simulation: Louis G. Birta Gilbert Arbez, Modelling and Simulation. + Springer 2019 ISBN: 978-3-030-18869-6 or Law A., Kelton D., Simulation and + Modelling Analysis, Second Edition, McGraw-Hill, ISBN: 978-0071165372 +- On optimization: pedir recomendación a Helena. +- On machine learning: Hastie T., Tibshirani R., Friedman J., The Elements Of + Statistical Learning: Data Mining, Inference, And Prediction, Second Edition + ISBN: 978-0387848570 + +## Cool ideas & notes + +- Hold a Kaggle competition with the students. Winners come to spend a morning + in Accenture. +- Start every lecture with a fun fact. +- Let them choose the challenge for one of the practical labs. +- Should I have office hours? +- Will the classes be recorded? +- What's are the policies on: + - Late deliveries + - Not attending exam + - Re-takes +- https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016 \ No newline at end of file diff --git a/random_notes.md b/random_notes.md new file mode 100644 index 0000000..00cda9c --- /dev/null +++ b/random_notes.md @@ -0,0 +1,19 @@ +Classroom for lectures -> 20.047 +Classroom for seminars -> 40.501 +Despachos asociados -> 40.171 i 40.173 + +# Before starting doubts + +- How do I build the course plan? How do I upload it? +- What should I expect from Sira? +- Office hours? +- Metodos para evaluarme a mi? +- Sobre las clases + - Portatil propio o usar el PC del aula? + - Microfono? + - Grabar? + - Mascarilla? + +Case 1 +Python colab resources +Primer deck \ No newline at end of file diff --git a/upf_systems.md b/upf_systems.md new file mode 100644 index 0000000..c51fd8b --- /dev/null +++ b/upf_systems.md @@ -0,0 +1,6 @@ +u208070 +el telefono con la colilla corporativa +pablo.martinc@upf.edu + + +