demand forecasting this easter
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Using data to accurately forecast demand for chocolate this Easter

Easter is a significant holiday for many businesses especially within the UK. The holiday period brings an increase to food and drink sales, with Easter being the second most popular period of the year for chocolate consumption. Many Britons book holiday trips in this period as well, making it an important time for the leisure industry.

Data science enables businesses to effectively plan for this busy period. Analysis of historical sales data can be used to predict future demand, allowing businesses to accurately plan stock levels; real-time analysis provides visibility of the state of a product, enabling businesses to quickly resolve issues regarding the manufacturing of products like chocolate bunnies.

Accurate forecasting to match demand

Demand forecasting is a commonly used approach which allows businesses to effectively predict future sales, plan and schedule production, improve budget planning, and develop efficient pricing strategies. Predictive analysis is used to understand and forecast demand over time, helping businesses make well-informed decisions.

Adapting in line with the coronavirus pandemic

The COVID-19 pandemic has brought great challenges within businesses. Easter brings challenges itself with the date of the holiday moving each year, however in 2020, businesses were simply not prepared for the impact of the pandemic. Easter egg sales fell by £36 million with many retailers having to sell lots of eggs at discounted prices. Some retailers were also unprepared for the boom in online sales and were not able to meet demand. On the upside, many businesses are better prepared for this year’s Easter period, with many focussing on online operations. Through demand forecasting techniques and last year’s data, businesses have been able to better prepare for this year’s demand. Many, for example Hotel Chocolat, are offering a limited range of Easter eggs this year.

As well as benefitting the retail and leisure industries, demand forecasting is used by other organisations over Easter. The NHS use forecasting techniques to predict demand and capacity for their services. This has been particularly important during the pandemic. In the January peak, NHS hospitals were caring for over 34,000 COVID-19 patients in England, approximately 80% higher than the first peak in 2020. Demand forecasting and mathematical models are being used to predict hospital bed demands frequently, tailored to specific hospitals, to help the NHS and government plan for future holiday periods such as Easter.

Demand forecasting is an effective approach that is being used by many businesses to plan for this year’s Easter holiday. Data-driven businesses can make well-informed decisions for the future, and as a result many will be better prepared this Easter.

Can we help with any aspect of your demand forecasting? Read our case study to find out how we have helped other companies with this.

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If you were thinking about submitting an abstract for the Enterprise Applications of the R Language Conference – you have just one week left to do so!

The conference is dedicated to the real-world usage of R with some of the world’s leading practitioners. If you use R in your organisation, then the EARL Conference is for you and your team. Whether you’re coding, wrangling data, leading a team of R users, or making data-driven decisions, EARL offers insights into the application of R that you can action in your company.

EARL 2021 will be held online on Friday 10th of September UK time, but we are pleased to welcome abstracts from across the world.

If you would like to share your work in R, please submit an abstract before the 31st of March here.

world water day
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As we celebrate World Water Day, we consider the lack of access to safe and affordable water – a dangerous reality for billions across the world. It has a damaging effect on not only the health of billions of people but also on many aspects of their lives. With climate change comes droughts, floods and scarcity of water, bringing with it social and economic devastation. For example, clean water and sanitation is an important factor in reaching equity amongst gender.

By 2030, the UN aims to achieve universal and equitable access to safe and affordable drinking water and adequate sanitation and hygiene for all. To reach these targets, it is vital that businesses create and achieve their own targets for water consumption to help make a positive change. Unfortunately, many of these targets are not being met.

Using the power of data to make a positive change

In data-driven world, data science allows us to harness the power of data to make positive change. It can be used in several ways to improve water quality and water consumption around the world:

  • The innovation of machine learning can be used to improve the way in which water is collected and transported to reduce CO2 emissions. Moreover, it can be used to improve the treatment and utilization of water.
  • Real-time monitoring gives communities the power to ensure water is safe to drink while saving on resources.
  • Data analysis allows predictions to be made about the quality of water given a number of factors like weather and pollution. This allows for better planning when it comes to supplying clean drinking water to those in need.
  • Identification of water supply issues can be significant in preventing the spread of diseases through water supplies.

Mango realises the importance of using data to help other businesses bring positive change to the world. Data science can be used to inform businesses on their water targets and make steps towards reaching them. As well as harnessing data, it is equally important to involve stakeholders in decision making to identify, understand and overcome water challenges.

Reducing water waste

Water waste is a problem that many businesses face, with more than 25% of water wasted being due to leaks. This can be significantly reduced through the use of data science and in turn help businesses reduce their water consumption.

One company, i20, provides smart network solutions designed to help water companies reduce their leaks and bursts, energy use and CO2 emissions. The company recognised that it was collecting a large amount of data but were not harnessing it. Mango were able to not only help i20 realise their data capabilities but develop a solution that greatly improved the performance of their smart network solutions, leading them into the world of AI and data analytics. This has enabled water companies to shift to conditional based maintenance and reduce the number of water leaks. One client reduced leakage by 15% in the north of their city within 2 weeks of using the solution.

Data science is a powerful tool which can be used to inform businesses and improve their water consumption as well as having world-wide applications in reaching the UN targets of providing clean, accessible water to all.

Find out more about how Mango has helped i2o harness their data.

 

Global Recycling Day
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It’s Global Recycling Day today and a day to raise awareness of the importance of recycling and how crucial and lasting change can help preserve the future of our planet. Recognised in the UN’s Sustainable Development Goals 2030, we are already seeing many individuals, governments and organisations taking direct action to support the global green agenda.

Founded by the Global Recycling Foundation, this year’s theme is #recyclingheros and recognises people, places and organisations that inspire us – demonstrating positive action.

From plastic pledges and recycling,  to waste targets, businesses are positively impacting the environment, and data science is being used positively to reduce environmental impact and financial costs – aligned to their corporate responsibility. There are many examples of data analytics applications that can play just a small part in decelerating the process of climate change – the more focus that organisations place on this, the brighter the outlook for our planet. We decided to take a look at some of the positive use cases.

Food waste

Think about the waste problem in supermarket fresh food sales. Many businesses are using data science to help the UK meet its target of eliminating food waste to landfill by 2030. Analytics of weather patterns can help supermarkets ensure they have the right amount of seasonal produce to meet demand for a particular weather period without wastage; and enhanced analytics of customer weekly shopping habits would mean the store could ensure it has met demand without having surplus fresh food.

Gousto, a British meal kit retailer, implemented forecasting algorithms in an effort to reduce their food waste. They were able to predict demand and analyse seasonal trends to better manage their fresh food stock. Forecast modelling allowed the business to not only predict with a high degree of confidence the number of orders they would receive in future weeks, but also predict the performance of existing and new recipes.

Plastic waste

Plastic waste posing a considerable threat to our planet, with 8 million metric tons of plastic being added annually to the world’s oceans. That is why many businesses in the UK have come together to work towards having all plastic packaging recyclable, reusable or compostable by 2025. One such company is Tesco who are rolling out collection points for soft plastic packaging in their stores. Tesco’s efforts should help the public in their efforts to recycle as well their own.

Outside of the UK, many companies are also reducing their plastic waste and increasing their recycling, with many also helping the public do so. The Gringgo Indonesia Foundation, with the help of Google, have used AI and machine learning to create an app to help better classify waste items. It can be used by businesses and the public to help improve their recycling. With the use of data science, within a year of launching the app, recycling rates were increased by 35% in their first pilot village.

Space junk

Space junk poses a danger to astronauts in orbit, the world’s network of communication and weather satellites. Luckily, data science is here to help. NASA have been developing technology to remove space junk. Using machine learning algorithms, NASA are working towards improving the detection of space junk for removal.

Clinical waste

Since the start of the COVID-19 pandemic, there has been an increase in single use plastics and clinical waste. With only 15% of clinical waste being hazardous, there is a massive opportunity to reduce and properly manage clinical waste using data science. From reducing the number of unnecessary hospital appointments to the size of some healthcare equipment, a positive change can be made.

Reducing waste and recycling is vital for the future of our world. Data science provides many tools for creating and implementing solutions, and with data-driven businesses striving to reduce their waste, the future looks bright.

To discuss any use cases to align your recycling goals, contact us.

Author: Elizabeth Brown, Professional Placement Student at Mango

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The next LondonR session will be a workshop focused on ‘Text Analysis in R’.

The workshop will be hosted by Hannah Alexander and Elizabeth Brown on Tuesday 23rd March from 3.30pm-5.30pm (GMT).

In this introductory workshop, we will show you how to get started with analysing text data – from simple manipulation through to sentiment analysis. A good working knowledge of R programming is assumed and familiarity with basic analytic techniques and linear modelling is required.

Tickets to the session are free, if you would like to join, please register for a ticket here. 

If you’d like to find out more about our future events, click here.

Later this year The Enterprise Applications of the R Language Conference will be hosted online from 6-10th September 2021, to stay up-to-date join the maling list here.

British Science Week
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As a data science consultancy, we’d like to celebrate British Science Week #BSW21 and the innovation in science, technology, maths and engineering, as well as the diversity of these roles. Many of our graduate consultants join Mango from a Maths and Statistics background, but also equally from a science background where many of the data and analytic approaches, including R are introduced.

Can data science be classified as a Science? We asked our Consultants, and the answer was a resounding ‘yes’, as our Graduate Data Scientist, Elizabeth Brown explains. “In my opinion, Data Science is a Science. The goal of Science is to gain a better understanding of the world around us, to explain why things happen or to describe the relationship between concepts. A big part of science is taking this understanding and applying it to real world situations, whether that be making advancements in medicine as we have witnessed with the vaccine development, or a modern way of introducing scientific methods to automate processes and make more intelligent decisions – ‘innovating for the future’, just as the theme for British Science Week this year. Like a science, we make observations, come up with hypotheses and through experimentation, test our hypotheses”.

Rich Pugh, Mango’s Chief Data Scientist agrees, “When done right, data science should be based on, and resemble, the scientific method. We formulate a “hypothesis” (although the structure of this can vary across application), then we use data to test that hypothesis”.

“Data is everywhere and being able to use it effectively to improve our understanding of the world is very exciting – expanding data-driven decision making, scientific discovery and  automation”, explains Elizabeth.

The growing capabilities of AI and machine learning are paving the way for real world solutions such as self-driving cars, much needed fraud prevention and addressing climate change. Fundamentally, data science isn’t just solving business problems, as a career it can support initiatives to create a healthier, greener and kinder world.

If you are interested in a data science career with Mango, click here to find out more.

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It’s mostly preaching to the converted to say that ‘open-source is changing enterprises’. The 2020 Open Source Security and Risk Analysis (OSSRA) Report found that 99 per cent of enterprise codebases audited in 2019 contained open-source components, and that 70 per cent of all audited codebases were entirely open-source.

Hearts and minds have most certainly been won, then, but there are still a surprising number of enterprise outliers when it comes to adopting open-source tools and methods. It’s no surprise that regulated industries are one such open-source averse group.

It’s still difficult to shake off the reputation open-source resources can have for being badly-built, experimental, or put together by communities with less recognisable credentials than big players in software. When your industry exists on trust in your methods – be it protecting client finances in banking, or the health of your patients in pharma – it’s often easier just to make do, and plan something more adventurous ‘tomorrow’.

This approach made a certain amount of sense in years past, when embracing open-source was more a question of saving capex with ‘free’ software, and taking the risk.

Then, along comes something like Covid-19, and the CEO of Pfizer – who are now among those leading the way in a usable vaccine – singing the praises of open-source approaches back in March 2020. Months down the line, AstraZeneca and Oxford University’s 70 percent-efficacy Covid-19 vaccine emerged. AstraZeneca is having a public conversation around how it’s “embracing data science and AI across [the] organisation” while it continues to “push the boundaries of science to deliver life-changing medicines”.

Maybe tomorrow has finally arrived.

At Mango, our primary interest is in data science and analytics, but we also have a great interest in the open-source programming language R when we’re thinking about statistical programming. We’re not attached to R for any other reason than we find it hugely effective in overcoming the obstacles the pharmaceutical industry recognises implicitly – accessing better capabilities, and faster.

With a growing number of pharmaceutical companies starting to move towards R for clinical submissions, we thought it would be useful to find out why. Asking experts from Janssen, Roche, Bayer and more, we collected first-hand use cases, experiences and stories of challenges overcome, as well as finding out how these companies are breaking the deadlock of open-source’s reputation versus its huge potential for good in a world where everything needs to move faster, while performing exceptionally. Watch the full round table recording here.

If you’d like to find out more, please get in touch and we’d be happy to continue the conversation.

Author: Rich Pugh, Chief Data Scientist at Mango

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In 2014 we launched the EARL (Enterprise Application of the R Language) Conference aimed at connecting and inspiring business users of R, the increasingly popular open source statistical programming language. With the pre-eminence of data science, the adoption of data-driven approaches to commercial decision making and R’s versatility for modelling, machine learning, report generation and interactive visualisations, we have been fortunate in attracting some fascinating presentations in the past six years.

We are now planning for EARL 2021 and we are looking for an eighth conference-worth of inspiring and interesting presentations within any of the following categories:

  • Business use cases of R
  • Python and R
  • Green R – using R to better the environment
  • R in Production
  • Using R to understand COVID
  • R for Automation (automation of data pipeline, automation through package building etc)
  • R Packages developed for business
  • Shiny

We are also looking for 10-minute lightning talks on:

Data for good – the use of R in addressing, measuring or solving issues to better the world.

We asked some of 2019’s presenters what prompted their decision to speak, to share their experiences as presenters and for their advice to others who may be considering submitting an abstract for EARL 2021.

Why Present?

For Mitchell Stirling, Capacity and Modelling Manager at Heathrow Airport, the opportunity to present helped fulfil a professional ambition.  “I discussed with my line manager, slightly tongue in cheek, that it should be an ambition in 2019 when he signed off a conference attendance in Scotland the previous year. As the work I’d been doing developed in 2019 and the opportunity presented itself, I started to think “why not?”, this is interesting and if I can show it interestingly, hopefully, others would agree. I was slightly wary of the technical nature of the event, with my exposure to coding in R still better measured in minutes than hours (never mind days) but a reassurance that people would be interested in the ‘what’ and ‘why’ as well as the ‘how’, won me over”.

Dr Zhanna Mileeva, a Data Scientist at NBrown Group confirmed that making a contribution to the data science community was an important factor in her decision to submit an abstract: “After some research I found the EARL conference as a great cross-sector forum for R users to share Data Science, AI and ML engineering knowledge, discuss modern business problems and pathways to solutions. It was a fantastic opportunity to contribute to this community, learn from it and re-charge with some fresh ideas.”

In past years EARL has attracted speakers from across the globe and last year, Harold Selman, Lead Data Scientist at Ordina (NL) came from the Netherlands to speak at the conference. His motivation? “I knew the EARL conference as a visitor and had given some presentations in The Netherlands, so I decided to give it a shot. The staff of the EARL conference are very helpful and open to questions, which made being a speaker very pleasant.”

Some of our presenters have enjoyed the experience so much they have presented more than once.  Chris Billingham, Lead Data Scientist at Manchester Airport Group’s Digital Agency MAG-O, is one such speaker. “I’ve had the good fortune to present twice at EARL.  I saw it as an opportunity to challenge myself to present at the biggest R conference in the UK.”

What did you enjoy about presenting at EARL?

For many people, the idea of delivering a presentation before a large audience can be a pretty scary prospect, but our presenters all enjoyed the experience.  Chris Billington commented that “It allowed me to focus in not only doing a great piece of work in R, but also to ensure I could communicate that work (in front of a load of R experts). It was nerve wracking but a brilliant experience,” whilst Mitchell Stirling noted that  “While any public speaking should make anyone a little nervous no matter how many times they’ve stood up and done it, it was good to get past it and realise there was an appetite to hear what we had been doing at the airport “.

Zhanna Mileeva clearly suffered no nerves and “enjoyed every aspect of presenting at EARL: the atmosphere of the conference, its interactive audience with diverse background and experience, and brilliant organisation”.

Were there any specific goals you were seeking to achieve by presenting at EARL?

Presenter’s motivations for submitting an abstract vary for a conference can vary widely; some are motivated by personal development goals whilst others are keen to contribute to the community.  Zhanna’s motivations covered a range of objectives: “As a Data Scientist my goals were to learn as much as possible during three days of the conference to advance my personal and professional development and to bring the highlights and most interesting business stories back to my team at NBrown. Another aim I had in mind was to grow my network of professionals passionate about Data Science. And, of course, I wanted to take the opportunity to shout out about our company, its recent success and why we are different from other retailers.”

Harold Selman had a goal of “expanding my horizon as an international speaker” and Mitchell’s objective was “to almost prove to myself that I could do it in an environment where people didn’t know me and wouldn’t have been the type of audience that I’d have hand-picked to give a talk to on this subject”. Chris, meanwhile, had professional development in mind, “I knew that speaking at EARL was an environment conducive to trying new things out so felt comfortable continuing to push myself to be better.”

What benefits (personal or professional) were achieved by presenting at EARL?

Whether for individual gain or the greater good, there are many benefits available for those whose abstracts are accepted for the EARL agenda.  One immediate benefit is a free conference pass for the day on which the presentation is to be given, along with a free ticket for the popular Conference Evening Reception.  In Mitchell’s opinion, “the R community must talk about the work its members are doing. I was glad to play a small part in that. I hope that some people who saw my presentation would consider Heathrow as an employment choice in the future after understanding what we are looking at achieving through this type of work.”

Zhanna reported that she was “pleased with the outcomes of my participation as I achieved what I aimed for. Informal breaks allowed me to meet like-minded people and expand my professional network. And I have some new ideas of what I would like to try next in Data Science” whilst a second presenting opportunity allowed Chris  “to build on all the good things I learned the first time and really nail the delivery, whilst talking about a much more technical subject (albeit a humorous one!).”

Our final ask of the presenters was would they recommend speaking at EARL?  On this, they were unanimous:  Harold’s advice was “I would recommend you apply this year if you think you have an interesting story to tell. And don’t let keynote speakers scare you out of submitting an abstract, not all speakers can be keynote!”

Chris is already planning his third appearance at EARL, “I cannot recommend enough taking the opportunity to talk at EARL. It’s really well organised by the Mango team who ensure that everything goes to plan, the attendees are great listeners and challenge you with some of their questions but also you’ll learn so much about communication and how best to do that as a Data Scientist. I’m already thinking about what my next talk might be!”   

Zhanna was equally enthusiastic “I highly recommend presenting at EARL. It is a great opportunity for learning, networking, talking to subject matter experts and sharpening your presentation skill” and Mitchell enjoyed the opportunity to present so much that he’s agreed to deliver his talk again in March at the LondonR user group.

Hopefully, the candid comments and experiences of this small selection of 2019’s presenters will provide just the inspiration and encouragement that you need to submit an abstract for EARL 2021.  The deadline for submissions is the 31st March. EARL 2021 will be held online on Friday 10th September.

We look forward to hearing from you!

 

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There is still time left for you to submit your abstract for the Enterprise Applications of the R Language Conference.

EARL will be held online this year on Friday 10th September and we are pleased to be able to welcome talks from across the globe. The abstract deadline is the 31st of March – please use this form to submit your talk.

If you use R at work we want to hear from you! EARL is dedicated to the real-world usage of R with some of the world’s leading practitioners. If you would like some talk inspiration, take a look at some past EARL presentations on our YouTube page or scroll to the bottom of this page to see slides from EARL 2020.

If you have any questions – please get in contact with the EARL team: earl-team@mango-solutions.com

Submit your abstract before 31st March 2021.