Customer Service in S&OP

Today I´m just bringing an article that has really resonated with me. How important you think Customer Service is for your S&OP process?

Worth spending the time reading it

Customer Service in S&OP

Continuous improvement - Improve 1% every day

The marginal gains philosophy is really easy to understand; it comes down to the idea that, if you just improve 1% each day, you're going to accumulate some pretty hefty results for minimal perceived effort.

However, who can get excited about a 1% daily improvement? Well, think that over a year, those daily 1% improvements will have stacked up to give a total improvement of over 3,700%!

Improving 1% each day can yield some BIG dividends, and the best example of that is probably the GB cycling Olympic team in London 2012 with Chris Brailsford at the helm.

The whole principle came from the idea that if you break down everything you could think of that goes into riding a bike, and then improved it by 1%, you will get a significant increase when you put them all together

No stone was left unturned; they even paid attention to things like always washing your hands thoroughly to help avoid getting sick or taking your own pillow when you travel so you're better rested.

Thanks to this "aggregation of marginal gains" the British cycling team dominated the competition.

However, this isn't just about professional athletes, the basic idea of accumulating small increments of progress can work for everything.

Healthcare, aviation, learning and even speed eating have benefited in the past by this philosophy, so…what can you do right now to improve by just 1%?

Artificial Intelligence, Machine Learning and Deep Learning, what is what?

In this post, I am hoping we will shed some light on these topical concepts, that although are usually used interchangeably, they do not quite refer to the same things.

The main idea behind these three concepts, lie in the image below:

As you can see, Deep Learning (DL) is a subset of Machine Learning (ML), which is also a subset of Artificial Intelligence (AI).

Let’s dig deeper so that we can understand better what each of them three concepts encompass.

Artificial Intelligence:

As the name suggests, artificial intelligence can be interpreted as incorporating human intelligence to machines.

Whenever a machine completes tasks based on a set of stipulated rules that solve problems (algorithms), that behaviour is what is called artificial intelligence.

We classify AI-powered machines into two groups; general and narrow.

The general artificial intelligence machines can intelligently solve problems, for example, moving or manipulating objects, recognizing whether someone has raised the hands, or solving a mathematic problem.

The narrow intelligence AI machines can perform specific tasks very well, sometimes better than humans can; however, they are limited in scope. The technology used for classifying images on Pinterest is an example of this.

Machine Learning:

As we already saw, ML is a subset of AI (in fact it is just a technique for realizing AI) and can be loosely described as ability of the computer systems to learn. 

The intention of ML is to train algorithms to enable machines to learn by themselves how to make decisions.

Training in machine learning entails giving a lot of data to the algorithm and allowing it to learn more about the processed information.

For example, below is a table that identifies the type of fruit based on certain characteristics:

As you can see on the table above, the fruits are differentiated based on their weight and texture.

However, the last row gives only the weight and texture, without the type of fruit. A machine-learning algorithm can be developed to try to identify whether the fruit is an orange or an apple.

After the algorithm is fed with the training data, it will learn the differing characteristics between an orange and an apple and predict accurately the type of fruit with those characteristics in the future.

Deep Learning:

As earlier mentioned, deep learning is a subset of ML; in fact, it’s simply a technique for realizing machine learning; DL is the next evolution of machine learning.

DL algorithms are inspired by the information processing patterns found in the human brain. Just like we use our brains to identify patterns and classify various types of information, deep learning algorithms can be taught to accomplish the same tasks for machines.

The brain usually tries to decipher the information it receives. It achieves this through labelling and assigning the items into various categories.

Whenever we receive a new information, the brain tries to compare it to a known item before making sense of it which is the same concept deep learning algorithms employ.

Comparing deep learning vs machine learning can help to understand their differences. While DL can automatically discover the features to be used for classification, ML requires these features to be provided manually.

Hopefully you are now slightly more clear about what each of these concepts mean and how they are interlinked!

Agile methodology: Kanban VS Scrum

After a very comprehensive intro on Project Management methodologies where we explored Agile VS Waterfall, in today's post we are going to deep dive on some of the differences between Kanban and Scrum, two of the frameworks within the Agile framework.

While there are some clear differences between both practices, the principles are largely the same.

Scrum is a tool used to organise work into small, manageable pieces that can be completed by a cross-functional team within a prescribed time period called a “sprint” (generally 2-4 weeks long). To plan, organise, administer, and optimise this process, Scrum relies on at least three prescribed roles: 

-  The Product Owner, responsible for initial planning, prioritising, and communication with the rest of the company

- The Scrum Master, responsible for overseeing the process during each sprint.

- The Team Members, responsible to carry out the purpose of each sprint, such as producing software code.

Kanban is also a tool used to organise work for the sake of efficiency, and like Scrum, Kanban encourages work to be broken down into manageable chunks, however, where Scrum limits the amount of time allowed to accomplish a particular amount of work, by means of sprints, Kanban limits the amount of work allowed in any one condition as only so many tasks can be ongoing.

 In the table below you can find the main differences between Scrum and Kanban:

Finally the video below illustrates very clearly the differences and points out how the Scrum and Kanban boards work.

Until next time!

Project Management: Waterfall VS Agile

In today´s post we are going to focus on Project Management. More and more, we see in Supply Chain the need to step back from the operational day to day task and focus on a specific project with a clear starting and end date. However there are several methodologies that can be followed to ensure the best outcome for any given project. We will be focusing on the two most common: Waterfall and Agile.

The Waterfall method is the most traditional project management approach that uses sequential phases to define, build, test, and release project deliverables. Each phase is completed and approved before the team moves on to the next phase. The project can't move backwards to previous phases.

On the other hand, Agile is an umbrella term covering several newer project management approaches that use iterative work cycles, called sprints. Each sprint uses 'mini-phases' to define, build, test, and release the project deliverables.

The main difference between waterfall and agile methods is in the goals; the waterfall method wants to get everything right the first time, and agile methods want to get things released quickly.

Pros of the waterfall method

- Potential issues that would have been found during development can be researched and bottomed out during the design phase.
- The development process tends to be better documented since this methodology places greater emphasis on documentation.
- Because the waterfall process is a linear one it is perhaps easier to understand. Often teams feel more comfortable with this approach.

Cons of the waterfall method

- Often it´s not known exactly what it´s needed up front or what’s possible with the technology available.
- Changes to requirements can’t easily be incorporated with the waterfall method and there are often laborious change control procedures to go through when this happens
- The process doesn’t have its own momentum

Pros of Agile methods

- Much more quicker, and successive iterations can be delivered frequently, at a consistent pace.
- There is closer collaboration between project managers and the business.
- Changes to requirements can be incorporated at any point of the process.
- It gives the opportunity for continuous improvement for live systems.
- It is highly transparent.

Cons of Agile methods

- Agile methodologies are often more difficult to understand than linear, sequential ones.
- Because of the emphasis on working software there can be a perception that documentation can sometimes be neglected.
- When implemented badly Agile can introduce extra inefficiencies in large organizations or can be working against long standing organizational processes.

7 Characteristics of the best Demand Planners

Without a shadow of a doubt, this is one of the best articles I have read recently, and it encompasses all the key traits that make a great Demand Planner.

Difficult to pick up just a few key points, as all of them are too valuable to be left out, but here I go:

- The difference between success and failure may not be dependent on intellect or even analytical ability, but on leadership skills.

- They are not afraid of voicing their opinions and lead from the front, bringing solutions, not problems.

- They will work on being able to express ideas or information clearly and if they do this while seeking to understand the other person’s needs and concerns.

- They follow less their gut instinct and try to find a quantitative basis for an idea.

- They demonstrate an interest in personal learning and development, seek feedback from multiple sources about how to improve and develop.

-  They are being able to communicate risk and uncertainty.

- They are creative in their approaches and are not afraid to try something new. They are not locked in on the way we used to do things, they do not work in a silo, they collaborate with others and finally, they are very customer-centric, focusing on adding value both internally and externally.

- They have the ability to stand strong and be wrong with confidence.  They are not afraid to take chances and learn from their setbacks and failed attempts.

http://demand-planning.com/2019/02/26/7-characteristics-of-demand-planning-rock-stars/

The U.K. is filling up with a lot of Brexit stock

Short and sweet article today, a re-post of an article from the website Supply Chain Brain, that summarizes the strategy most big, medium and small size companies are taking to prepare for a no-deal Brexit.

Stocking up seems to be the only alternative to minimize potential loss sales in case a tumultuous Brexit chokes just in time supply chains and creates backlogs at ports.

https://www.supplychainbrain.com/articles/29258-the-great-brexit-stockpile-how-corporate-britain-is-bracing-for-no-deal