Program Evaluation Model 9 Step Process: Fill & Download for Free

Here are some amazing and completely free resources online that you can use to teach yourself data science.Besides this page, I would highly recommend following the Quora Data Science topic if you haven't already to get updates on new questions and answers!Step 1. Fulfill your prerequisitesBefore you begin, you need Multivariable Calculus, Linear Algebra, and Python. If your math background is up to multivariable calculus and linear algebra, you'll have enough background to understand almost all of the probability / statistics / machine learning for the job.Multivariate Calculus: What are the best resources for mastering multivariable calculus?Numerical Linear Algebra / Computational Linear Algebra / Matrix Algebra: Linear Algebra, Introduction to Linear Models and Matrix Algebra. Avoid linear algebra classes that are too theoretical, you need a linear algebra class that works with real matrices.Multivariate calculus is useful for some parts of machine learning and a lot of probability. Linear / Matrix algebra is absolutely necessary for a lot of concepts in machine learning.You also need some programming background to begin, preferably in Python. Most other things on this guide can be learned on the job (like random forests, pandas, A/B testing), but you can't get away without knowing how to program!Python is the most important language for a data scientist to learn. To learn to code, more about Python, and why Python is so important, check outHow do I learn to code?How do I learn Python?Why is Python a language of choice for data scientists?Is Python the most important programming language to learn for aspiring data scientists and data miners?R is the second most important language for a data scientist to learn. I’m saying this as someone with a statistics background and who went through undergrad mainly only using R. While R is powerful for dedicated statistical tasks, Python is more versatile as it will connect you more to production-level work.If you're currently in school, take statistics and computer science classes. Check out What classes should I take if I want to become a data scientist?Step 2. Plug Yourself Into the CommunityCheck out Meetup to find some that interest you! Attend an interesting talk, learn about data science live, and meet data scientists and other aspirational data scientists. Start reading data science blogs and following influential data scientists:What are the best, insightful blogs about data, including how businesses are using data?What is your source of machine learning and data science news? Why?What are some best data science accounts to follow on Twitter, Facebook, G+, and LinkedIn?What are the best Twitter accounts about data?Step 3. Setup and Learn to use your toolsPythonInstall Python, iPython, and related libraries (guide)How do I learn Python?RInstall R and RStudio (It's good to know both Python and R)Learn R with swirlSublime TextInstall Sublime TextWhat's the best way to learn to use Sublime Text?SQLHow do I learn SQL? What are some good online resources, like websites, blogs, or videos? (You can practice it using the sqlite package in Python)Step 4. Learn Probability and StatisticsBe sure to go through a course that involves heavy application in R or Python. Knowing probability and statistics will only really be helpful if you can implement what you learn.Python Application: Think Stats (free pdf) (Python focus)R Applications: An Introduction to Statistical Learning (free pdf)(MOOC) (R focus)Print out a copy of Probability CheatsheetStep 5. Complete Harvard's Data Science CourseAs of Fall 2015, the course is currently in its third year and strives to be as applicable and helpful as possible for students who are interested in becoming data scientists. An example of how is this happening is the introduction of Spark and SQL starting this year.I'd recommend doing the labs and lectures from 2015 and the homeworks from 2013 (2015 homeworks are not available to the public, and the 2014 homeworks are written under a different instructor than the original instructors).This course is developed in part by a fellow Quora user, Professor Joe Blitzstein. Here are all of the materials!Intro to the classWhat is it like to design a data science class? In particular, what was it like to design Harvard's new data science class, taught by professors Joe Blitzstein and Hanspeter Pfister?What is it like to take CS 109/Statistics 121 (Data Science) at Harvard?Course MaterialsClass main page: CS109 Data ScienceLectures, Slides, and Labs: Class MaterialAssignmentsIntro to Python, Numpy, Matplotlib (Homework 0) (Solutions)Poll Aggregation, Web Scraping, Plotting, Model Evaluation, and Forecasting (Homework 1) (Solutions)Data Prediction, Manipulation, and Evaluation (Homework 2) (Solutions)Predictive Modeling, Model Calibration, Sentiment Analysis (Homework 3) (Solutions)Recommendation Engines, Using Mapreduce (Homework 4) (Solutions)Network Visualization and Analysis (Homework 5) (Solutions)Labs(these are the 2013 labs. For the 2015 labs, check out Class Material)Lab 2: Web ScrapingLab 3: EDA, Pandas, MatplotlibLab 4: Scikit-Learn, Regression, PCALab 5: Bias, Variance, Cross-ValidationLab 6: Bayes, Linear Regression, and Metropolis SamplingLab 7: Gibbs SamplingLab 8: MapReduceLab 9: NetworksLab 10: Support Vector MachinesStep 6. Do all of Kaggle's Getting Started and Playground CompetitionsI would NOT recommend doing any of the prize-money competitions. They usually have datasets that are too large, complicated, or annoying, and are not good for learning. The competitions are available at Competitions | KaggleStart by learning scikit-learn, playing around, reading through tutorials and forums on the competitions that you’re doing. Next, play around some more and check out the tutorials for Titanic: Machine Learning from Disaster for a binary classification task (with categorical variables, missing values, etc.)Afterwards, try some multi-class classification with Forest Cover Type Prediction. Now, try a regression task House Prices: Advanced Regression Techniques. Try out some natural language processing with Quora Question Pairs | Kaggle. Finally, try out any of the other knowledge-based competitions that interest you!Step 7. Learn Some Data Science ElectivesData science is an incredibly large and interdisciplinary field, and different jobs will require different skillsets. Here are some of the more common ones:Product Metrics will teach you about what companies track, what metrics they find important, and how companies measure their success: The 27 Metrics in Pinterest’s Internal Growth DashboardMachine Learning How do I learn machine learning? This is an extremely rich area with massive amounts of potential, and likely the “sexiest” area of data science today. Andrew Ng's Machine Learning course on Coursera is one of the most popular MOOCs, and a great way to start! Andrew Ng's Machine Learning MOOCA/B Testing is incredibly important to help inform product decisions for consumer applications. Learn more about A/B testing here: How do I learn about A/B testing?Visualization - I would recommend picking up ggplot2 in R to make simple yet beautiful graphics and just browsing DataIsBeautiful • /r/dataisbeautiful and FlowingData for ideas and inspiration.User Behavior - This set of blogs posts looks useful and interesting - This Explains Everything " User BehaviorFeature Engineering - Check out What are some best practices in Feature Engineering? and this great example: http://nbviewer.ipython.org/github/aguschin/kaggle/blob/master/forestCoverType_featuresEngineering.ipynbBig Data Technologies - These are tools and frameworks developed specifically to deal with massive amounts of data. How do I learn big data technologies?Optimization will help you with understanding statistics and machine learning: Convex Optimization - Boyd and VandenbergheNatural Language Processing - This is the practice of turning text data into numerical data whilst still preserving the "meaning". Learning this will let you analyze new, exciting forms of data. How do I learn Natural Language Processing (NLP)?Time Series Analysis - How do I learn about time series analysis?Step 8. Do a Capstone Product / Side ProjectUse your new data science and software engineering skills to build something that will make other people say wow! This can be a website, new way of looking at a dataset, cool visualization, or anything!What are some good toy problems (can be done over a weekend by a single coder) in data science? I'm studying machine learning and statistics, and looking for something socially relevant using publicly available datasets/APIs.How can I start building a recommendation engine? Where can I find an interesting data set? What tools/technologies/algorithms are best to build the engine with? How do I check the effectiveness of recommendations?What are some ideas for a quick weekend Python project? I am looking to gain some experience.What is a good measure of the influence of a Twitter user?Where can I find large datasets open to the public?What are some good algorithms for a prioritized inbox?What are some good data science projects?Create public github repositories, make a blog, and post your work, side projects, Kaggle solutions, insights, and thoughts! This helps you gain visibility, build a portfolio for your resume, and connect with other people working on the same tasks.Step 9. Get a Data Science Internship or JobHow do I prepare for a data scientist interview?How should I prepare for statistics questions for a data science interviewWhat kind of A/B testing questions should I expect in a data scientist interview and how should I prepare for such questions?What companies have data science internships for undergraduates?What are some tips to choose whether I want to apply for a Data Science or Software Engineering internship?When is the best time to apply for data science summer internships?Check out The Official Quora Data Science FAQ for more discussion on internships, jobs, and data science interview processes! The data science FAQ also links to more specific versions of this question, like How do I become a data scientist without a PhD? or the counterpart, How do I become a data scientist as a PhD student?Step 10. Share your Wisdom Back with the Data Science CommunityIf you’ve made it this far, congratulations on becoming a data scientist! I’d encourage you to share your knowledge and what you’ve learned back with the data science community. Data Science as a nascent field depends on knowledge-sharing!Think like a Data ScientistIn addition to the concrete steps I listed above to develop the skill set of a data scientist, I include seven challenges below so you can learn to think like a data scientist and develop the right attitude to become one.(1) Satiate your curiosity through dataAs a data scientist you write your own questions and answers. Data scientists are naturally curious about the data that they're looking at, and are creative with ways to approach and solve whatever problem needs to be solved.Much of data science is not the analysis itself, but discovering an interesting question and figuring out how to answer it.Here are two great examples:Hilary: the most poisoned baby name in US historyA Look at Fire Response DataChallenge: Think of a problem or topic you're interested in and answer it with data!(2) Read news with a skeptical eyeMuch of the contribution of a data scientist (and why it's really hard to replace a data scientist with a machine), is that a data scientist will tell you what's important and what's spurious. This persistent skepticism is healthy in all sciences, and is especially necessarily in a fast-paced environment where it's too easy to let a spurious result be misinterpreted.You can adopt this mindset yourself by reading news with a critical eye. Many news articles have inherently flawed main premises. Try these two articles. Sample answers are available in the comments.Easier: You Love Your iPhone. Literally.Harder: Who predicted Russia’s military intervention?Challenge: Do this every day when you encounter a news article. Comment on the article and point out the flaws.(3) See data as a tool to improve consumer productsVisit a consumer internet product (probably that you know doesn't do extensive A/B testing already), and then think about their main funnel. Do they have a checkout funnel? Do they have a signup funnel? Do they have a virility mechanism? Do they have an engagement funnel?Go through the funnel multiple times and hypothesize about different ways it could do better to increase a core metric (conversion rate, shares, signups, etc.). Design an experiment to verify if your suggested change can actually change the core metric.Challenge: Share it with the feedback email for the consumer internet site!(4) Think like a BayesianTo think like a Bayesian, avoid the Base rate fallacy. This means to form new beliefs you must incorporate both newly observed information AND prior information formed through intuition and experience.Checking your dashboard, user engagement numbers are significantly down today. Which of the following is most likely?1. Users are suddenly less engaged2. Feature of site broke3. Logging feature brokeEven though explanation #1 completely explains the drop, #2 and #3 should be more likely because they have a much higher prior probability.You're in senior management at Tesla, and five of Tesla's Model S's have caught fire in the last five months. Which is more likely?1. Manufacturing quality has decreased and Teslas should now be deemed unsafe.2. Safety has not changed and fires in Tesla Model S's are still much rarer than their counterparts in gasoline cars.While #1 is an easy explanation (and great for media coverage), your prior should be strong on #2 because of your regular quality testing. However, you should still be seeking information that can update your beliefs on #1 versus #2 (and still find ways to improve safety). Question for thought: what information should you seek?Challenge: Identify the last time you committed the Base Rate Fallacy. Avoid committing the fallacy from now on.(5) Know the limitations of your tools“Knowledge is knowing that a tomato is a fruit, wisdom is not putting it in a fruit salad.” - Miles KingtonKnowledge is knowing how to perform a ordinary linear regression, wisdom is realizing how rare it applies cleanly in practice.Knowledge is knowing five different variations of K-means clustering, wisdom is realizing how rarely actual data can be cleanly clustered, and how poorly K-means clustering can work with too many features.Knowledge is knowing a vast range of sophisticated techniques, but wisdom is being able to choose the one that will provide the most amount of impact for the company in a reasonable amount of time.You may develop a vast range of tools while you go through your Coursera or EdX courses, but your toolbox is not useful until you know which tools to use.Challenge: Apply several tools to a real dataset and discover the tradeoffs and limitations of each tools. Which tools worked best, and can you figure out why?(6) Teach a complicated conceptHow does Richard Feynman distinguish which concepts he understands and which concepts he doesn't?Feynman was a truly great teacher. He prided himself on being able to devise ways to explain even the most profound ideas to beginning students. Once, I said to him, "Dick, explain to me, so that I can understand it, why spin one-half particles obey Fermi-Dirac statistics." Sizing up his audience perfectly, Feynman said, "I'll prepare a freshman lecture on it." But he came back a few days later to say, "I couldn't do it. I couldn't reduce it to the freshman level. That means we don't really understand it." - David L. Goodstein, Feynman's Lost Lecture: The Motion of Planets Around the SunWhat distinguished Richard Feynman was his ability to distill complex concepts into comprehendible ideas. Similarly, what distinguishes top data scientists is their ability to cogently share their ideas and explain their analyses.Check out https://www.quora.com/Edwin-Chen-1/answers for examples of cogently-explained technical concepts.Challenge: Teach a technical concept to a friend or on a public forum, like Quora or YouTube.(7) Convince others about what's importantPerhaps even more important than a data scientist's ability to explain their analysis is their ability to communicate the value and potential impact of the actionable insights.Certain tasks of data science will be commoditized as data science tools become better and better. New tools will make obsolete certain tasks such as writing dashboards, unnecessary data wrangling, and even specific kinds of predictive modeling.However, the need for a data scientist to extract out and communicate what's important will never be made obsolete. With increasing amounts of data and potential insights, companies will always need data scientists (or people in data science-like roles), to triage all that can be done and prioritize tasks based on impact.The data scientist's role in the company is the serve as the ambassador between the data and the company. The success of a data scientist is measured by how well he/she can tell a story and make an impact. Every other skill is amplified by this ability.Challenge: Tell a story with statistics. Communicate the important findings in a dataset. Make a convincing presentation that your audience cares about.Good luck and best wishes on your journey to becoming a data scientist! For more resources check out Quora’s official Quora Data Science FAQ

A2A.I. THE BIG PICTURE:Problem we are trying to solve: Given some data, the goal of machine learning is to find pattern in the data. There are various settings, like supervised learning, unsupervised learning, reinforcement learning, etc. But the most common one is supervised learning; so we’re going to focus only on that in the big picture. Here, you are given labelled data [called the “training data”], and you want to infer labels on new data [called the “test data”]. For instance, consider self-driving cars. Labelled data would include the image of the road ahead at a particular instance as seen from the car, and the corresponding label would be the steering angle [let’s assume the speed is controlled manually, for simplicity]. The goal of self-driving car is, given a new image of the road ahead, the system should be able to figure out the optimal steering angle.How to solve: Most of supervised machine learning can be looked at using the following framework — You are given training data points [math](x_1, y_1), \ldots, (x_n, y_n)[/math], where [math]x_i[/math] is the data [e.g. road image in the example above], and [math]y_i[/math] is the corresponding label. You want to find a function [math]f[/math] that fits the data well, that is, given [math]x_i[/math], it outputs something close enough to [math]y_i[/math]. Now where do you get this function [math]f[/math] from? One way, which is the most common in ML, is to define a class of functions [math]\mathcal{F}[/math], and search in this class the function that best fits the data. For example, if you want to predict the price of an apartment based on features like number of bedrooms, number of bathrooms, covered area, etc. you can reasonably assume that the price is a linear combination of all these features, in which case, the function class [math]\mathcal{F}[/math] is defined to be the class of all linear functions. For self-driving cars, the function class [math]\mathcal{F}[/math] you need will be much more complex.How to evaluate: Note that just fitting the training data is not enough. Data are noisy — for instance, every apartment with the same number of bedrooms, same number of bathrooms and same covered area are not priced equally. Similarly, if you label data for self-driving cars, you can expect some randomness due to the human driver. What you need is that your framework should be able to extract out the pattern, and ignore the random noise. In other words, it should do well on unseen data. Therefore, the way to evaluate models is to hold out a part of the training data [called “validation set”], and predict on this held out data to measure how good your model is.Now whatever you study in machine learning, you should try to relate the topics to the above big picture. For instance, in linear regression, the function class is linear and the evaluation method is square loss, in linear SVM, the function class is linear and the evaluation method is hinge loss, and so on. First understand these algorithms at high-level. Then, go into the technical details. You will see that finding the best function [math]f[/math] in the function class [math]\mathcal{F}[/math] often results in an optimization problem, for which you use stochastic gradient descent.II. ROADMAP FOR LEARNING MACHINE LEARNING:To have a basic mathematical background, you need to have some knowledge of the following mathematical concepts:- Probability and statistics- Linear algebra- Optimization- Multivariable calculus- Functional analysis (not essential)- First-order logic (not essential)The Deep Learning book from Yoshua Bengio’s lab covers most of the important concepts concisely, so that’s a good starting point. You don't need to master all the math before starting with ML. You can come back to studying the math as and when required while learning ML.Then, for a quick overview of ML, you can follow the roadmap below (I’ve written about many of these topics in various answers on Quora; I linked the most relevant ones for quick reference. I’ve also created a YouTube channel to cover these topics in Hindi.)Day 1:Basic terminology:Most common settings: Supervised setting, Unsupervised setting, Semi-supervised setting, Reinforcement learning.Most common problems: Classification (binary & multiclass), Regression, Clustering.Preprocessing of data: Data normalization.Concepts of hypothesis sets, empirical error, true error, complexity of hypotheses sets, regularization, bias-variance trade-off, loss functions, cross-validation.Day 2:Optimization basics:Terminology & Basic concepts: Convex optimization, Lagrangian, Primal-dual problems, Gradients & subgradients, [math]\ell_1[/math] and [math]\ell_2[/math] regularized objective functions.Algorithms: Batch gradient descent & stochastic gradient descent, Coordinate gradient descent.Implementation: Write code for stochastic gradient descent for a simple objective function, tune the step size, and get an intuition of the algorithm.Day 3:Classification:Logistic RegressionSupport vector machines: Geometric intuition, primal-dual formulations, notion of support vectors, kernel trick, understanding of hyperparameters, grid search.Online tool for SVM: Play with this online SVM tool (scroll down to “Graphic Interface”) to get some intuition of the algorithm.Day 4:Regression:Ridge regressionClustering:k-means & Expectation-Maximization algorithm.Top-down and bottom-up hierarchical clustering.Day 5:Bayesian methods:Basic terminology: Priors, posteriors, likelihood, maximum likelihood estimation and maximum-a-posteriori inference.Gaussian Mixture ModelsLatent Dirichlet Allocation: The generative model and basic idea of parameter estimation.Day 6:Graphical models:Basic terminology: Bayesian networks, Markov networks / Markov random fields.Inference algorithms: Variable elimination, Belief propagation.Simple examples: Hidden Markov Models. Ising model.Days 7–8:Neural Networks:Basic terminology: Neuron, Activation function, Hidden layer.Convolutional neural networks: Convolutional layer, pooling layer, Backpropagation.Memory-based neural networks: Recurrent Neural Networks, Long-short term memory.Tutorials: I’m familiar with this Torch tutorial (you’ll want to look at [math]\texttt{1_supervised}[/math] directory). There might be other tutorials in other deep learning frameworks.Day 9:Miscellaneous topics:Ensemble methodsDecision treesRecommender systemsMarkov decision processesMulti-armed banditsDay 10: (Budget day)You can use the last day to catch up on anything left from previous days, or learn more about whatever topic you found most interesting / useful for your future work.Once you’ve gone through the above, you’ll want to start going through some standard online course or ML text. Andrew Ng's course on Coursera is a good starting point. An advanced version of the course is available on The Open Academy (Machine Learning | The Open Academy). The popular books that I have some experience with are the following:Pattern Recognition and Machine Learning: Christopher BishopMachine Learning: A Probabilistic Perspective: Kevin P. MurphyWhile Murphy's book is more current and is more elaborate, I find Bishop’s to be more accessible for beginners. You can choose one of them according to your level.At this point, you should have a working knowledge of machine learning. Beyond this, if you're interested in a particular topic, look for specific online resources on the topic, read seminal papers in the subfield, try finding some simpler problems and implement them.For deep learning, here’s a tutorial from Yoshua Bengio’s lab that was written in the initial days of deep learning : Deep Learning Tutorials. This explains the central ideas in deep learning, without going into a lot of detail.Because deep learning is a field that is more empirical than theoretical, it is important to code and experiment with models. Here is a tutorial in TensorFlow that gives implementations of many different deep learning tasks — aymericdamien/TensorFlow-Examples. Try running the algorithms, and play with the code to understand the underlying concepts better.Finally, you can refer to Deep Learning book, which explains deep learning in a much more systematic and detailed manner. For the latest algorithms that are not in the book, you’ll have to refer to the original papers.III. TIPS ON IMPLEMENTATION:There are different levels at which you can understand an algorithm.At the highest level, you know what an algorithm is trying to do and how. So for instance, gradient descent finds a local minimum by taking small steps along the negative gradient.Going slightly deeper, you will delve into the math. Again, taking gradient descent for example, you will learn about how to take gradient for vector quantities, norms, etc. At about the same level of depth, you’ll also have other variants of the algorithm, like handling constraints in gradient descent. This is also the level at which you learn how to use libraries to run your specific algorithm.Further deeper, you implement the algorithm from scratch, with minor optimization tricks. For instance, in Python, you will want to use vectorization. Consider the following two code snippets:# Version 1:  import numpy as np  N = 10000000 a = np.random.rand(N,1) b = np.random.rand(N,1)  for i in range(N):  s = s + a[i] * b[i]  print s  # Version 2:  import numpy as np  N = 10000000 a = np.random.rand(N,1) b = np.random.rand(N,1)  s = a * b  print s They both have the same functionality, but the second one is 20 times faster. Similarly, you will learn some other important implementation techniques, such as parallelizing code, profiling, etc. You will also learn some algorithm-specific details, like how to initialize your model for faster convergence, how to set the termination condition to trade-off accuracy and training time, how to handle corner cases [like saddle points in gradient descent], etc. Finally, you will learn techniques to debug machine learning code, which is often tricky for beginners.Finally, comes the depth at which libraries are written. This requires way more systems knowledge than the previous steps — knowing how to handle very large data, computational efficiency, effective memory management, writing GPU code, effective multi-threading, etc.Now, in how much detail do you need to know the algorithms? For the most part, you don’t need to know the algorithms at the depth of library-implementation, unless you are into systems programming. For most important algorithms in ML — like gradient descent, SVM, logistic regression, neural networks, etc. — you need to understand the math, and how to use libraries to run them. This would be sufficient if you are not an ML engineer, and only use ML as a black-box in your daily work.However, if you are going to be working as an ML engineer / data scientist / research scientist, you need to also implement some algorithms from scratch. Usually the ones covered in online courses are enough. This helps you learn many more nuances of different tools and algorithms. Also, this will help you with new algorithms that you might need to implement.

There are various stages in the business buying process. Buyers who face a newtask buying situation usually go through all the stages. Buyers making a modifiedor straight rebuy will skip some of the stages. The stages are:1. Problem recognition First step is to recognise a problem or need that can be met by acquiring a good or service. Recognition can be triggered by an internal or external stimuli2. General need description The buyers determine needed item’s general characteristics and required quantity3. Product specification Buying organizations develop the items technical specification.4. Product value analysis It is an approach to cost reduction that studies whether components can be redesigned or standardised or made by cheaper methods without impacting products performance5. Supplier search Buyer tries to identify the most appropriate suppliers through different modes Trade directories Trade shows Contacts with other companies Trade advertisement Internet6. Companies that purchase over internet are utilising electronic market places in several forms Catalog sites Buying alliances Barter markets Private exchanges Spot (exchange) market Vertical markets Pure play auction sites7. E-Procurement Websites are organised around two types of E-hub Functional hubs Logistic, media ,buying, advertising, energy management Vertical hubs Centred on industries8. Websites are organised around two types of E-hub Set up direct extranet links to major suppliers Set up company buying sites Form buying alliance9. Lead generation Marketing must work together with sales to define sales ready prospect and cooperate to send right message to customers searching for suppliers10. Proposal solicitation 5 The buyer invites qualified suppliers to submit proposals11. Proposals should be marketing documents that describe value and benefits in customer terms12. Oral presentation must inspire confidence and position the company’s capabilities and resources .13. Supplier selection 6 Buying center will specify and rank customer attributes often using a supplier evaluation model.14. Overcoming price pressure The buying center tries to negotiate with preferred suppliers for better prices and terms15. Some companies handle price oriented buyers by lowering price but establishing restricted condition Limited quantities No refunds No adjustments No services16. Solution selling can also elevate price pressure Solutions to enhance customer revenue Solutions to reduce customer cost Solutions to decrease customer risk17. A number of suppliers Companies are increasingly reducing the no. Of suppliers Multiple sourcing Often cite threat of labour strike Single sourcing Suppliers may become too comfortable and lose their competitive edge18. Order routine specification Buyers negotiate the final order, listing the technical specifications,the quantity needed, the expected time of delivery, return policies e.t.c19. Blanket contracts It establishes a long term relationship in which supplier promises to resupply the buyer as needed.At agreed upon prices, over a specified purchase plan Vendor management inventory Some companies shift the ordering responsibility to the suppliers. Suppliers take responsibility for replenishing automatically through continuous replenishment program20. Performance review The buyer periodically reviews the performance of chosen supplier By contacting end users and asking for their evaluation Aggregate the cost of poor performance to come up with adjusted costs of purchase Rate the supplier on various criteria using weighted score method