Books, laughter, and beginning the job search in earnest
I've been learning non-stop.
Garrett Mayock posted 2019-02-20 21:40:01 UTC
I have been reading a lot recently, finishing four non-fiction books since my last blog post. Two of them are relevant to my job search:
The former gives a history of the five main schools of thought on machine learning, as well as postulating an algorithm which unifies them all, which the author calls the "master" algorithm. It's an interesting treatise on the different schools of thought, as well as the benefits and disadvantages of the algorithms of each.
Since different machine learning algorithms perform better in different situations, I'm sure I'll be referring back to this book when approaching new problems and deciding how to attack them.
The latter is an exploration of the ways sourcing and supply chain organizations can and do contribute to competitive advantages in the real world. The author draws from famous case studies such as Boeing's struggles delivering the 787 Dreamliner as well as his own experience from 20 years of being a supply chain and sourcing consultant. It's written at a high level, focused on strategy rather than tactical execution, and painted an exciting picture of what may not typically be considered a very sexy industry (a quick Google search reveals a lot of consternation on the topic).
While I took courses on supply chain management while studying at the Foster School of Business, after reading The Supply Chain Revolution I thought a more practical refresher would be helpful. In pursuit of that, I just received Managing Supply Chain Operations, a textbook-style book (with case studies and end-of-chapter exercises, etc) written by three professors from Rutgers Business School (Lei Lei, Yao Zhao, and Rosa Oppenheim) in collaboration with Len DeCandia, Chief Procurement Officer for Johnson & Johnson. It should help me "translate fundamental supply chain concepts and challenges into quantitative and qualitative opportunities", if the preface is to be believed. I translate this as "take what I read in the book and put it to use in real life".
There's also a lot of opportunity to use data science within supply chain management. The next book on my list, Data Science for Supply Chain Forecast will give me more ideas, but just spitballing, one could use:
- Regression models to predict price to find the best times to buy materials (maybe your suppliers give the highest discounts at the end of the month when they're trying to make quota)
- Constrained optimization algorithms to create a more efficient distribution network, making major reductions in cost without affecting service
- Neural networks to perform optical character recognition to machine-encode the text of supplier contracts
- This could enable them to be searched with queries instead of by hand (which of my Gulf of Mexico drilling contracts with Vendor X have "Force Majeure" clauses in them?)
- This could provide the necessary information for standardizing contract terms (given a set of contract terms and their performance against some metric, which terms were "best"?)
- Historical supplier data analysis to recommend purchasing with extra lead time if their deliveries always arrive late
- Predictive analysis to fully automate your commodity purchases or trading using machine learning as the predictive backbone - similar to how Wealthfront delivers its automated investment management service
- Clustering algorithms to segment your suppliers or customers to optimize based on any number of criteria
- For example, if you're a retailer, you could provide better service (more frequent deliveries at optimal delivery times) to high-volume retail locations while providing less expensive service (fewer deliveries at less expensive delivery times) to low-volume locations
And so on. It's definitely opened my mind to a career in supply chain - it looks like an industry that machine learning will disrupt frequently within my lifetime.
I've also recently completed Level 3 of improv training at Station Theater; we had our recital on January 19th, so now I'm in Level 4. I also took the first level of sketch writing class there, but level 2 isn't starting up right away so I'll be continuing practice with a group of friends on weekends.
And excitingly, I just auditioned for and made the "Launchpad" improv troupe also at Station. We begin rehearsal this Thursday, and will be performing every other Thursday at Station Theater in March and April. I'll post which Thursdays it will be when it's been decided.
Haven't gotten up for an open mic in almost two months, though, just because of all the time I've been putting into studying data science and trying to find a better direction for my career. I've got a bunch of ideas which should be good for some fun, relatively clean, non-offensive laughter, though, so I'm excited to get back to it whenever I make time to get a good story that puts those ideas together. Mostly all about the best thing in my life right now, King Mayock. Once I get five good minutes together I'm going to try to go as often as possible (~30 times in a month) to see how much I can improve it and what that whole process is like. It's thrilling!
The job search
Well, I've actually begun my job search in earnest this month, looking for companies and positions that match my current skills and long-term interests. If you know anything about what I've been writing about here, or someone who does, please reach out to me on LinkedIn - no pressure, I'm just excited to learn as much as I can as I move forward with my career. I'd love to talk about different companies,
Otherwise, not much to say here that I haven't said in my last two posts, other than now I'm also looking into supply chain management positions with forward thinking companies (no matter how far along they are in the process).
I'm excited to work through Managing Supply Chain Operations and get back to speed on the topic - I'll start as soon as I post this blog. I'll do their exercises in whatever medium they recommend (it looks like it's Excel heavy) as well as duplicating them in Python, so I can get applied Python experience. Simultaneously, I'll be doing the Kaggle House Price competition; it involves using regression models to predict future prices of goods (houses) based on historical attributes (size, location, etc). This is one thing that could be directly applicable to a position in procurement and supply chain management, so I'm excited to get started on that as well.
I'll keep you posted!contact me