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Big Data has a major impact on several functioning departments altogether. Big Data has grown out to be converted into a global phenomenon that uses thorough and logical analysis to solve real-world problems. 

Data gathering has lately become extremely advanced. With better computational strength and better creativity, we can easily drive the information revolution. According to a recent report, Big data has the potential solutions to a whole bunch of problems that we are facing today and environmental sustainability is one of them. Big data analytics for the environment simplifies preservation. 

Why worry? 

Climate change has moved to the top of the list of global risks, affecting every country and disrupting economies. While a major part of this damage is irreversible, it is still possible with the use of a wide range of technological measures to control the global increase in temperature. Big data can generate useful insights that can be as relevant to fostering environmental sustainability as they have been to other sectors such as healthcare.

There are several global risks that we face as a planet. Climate change has recently been topping in all the lists that there is. While nature has its own course of setting up and disrupting things and with itself brings a great extent of irreversible damage, Big data can still help us to generate relevant insights that will help us a great extent to take preventive measures and foster environment sustainability. The impact of data and analytics on society and the environment is huge.

Going a notch deeper to the Functioning 

Now you might be thinking about how Big Data can directly impact the environment. While Big data directly cannot stop climate change, its utilization lies in the ability to help businesses understand the statistics and see for themselves the drastic changes that had happened and act in accordance. It helps businesses act on the impact and build sustainable solutions to help limit the extent of the devastation. These efforts can be further dispersed in a very wide spectrum. Be it raw material sourcing, product disposal methods, waste removal methods and the likes of the same. Big data for sustainable development is the choice that will redefine the future.

Let us quickly dive into the top 3 ways in which Big Data will directly impact the environment. 

 

  • Residential Water Waste

Water waste has been a growing menace and problem for some time now. Too much water gets wasted through broken pipelines, sprinklers, and unattended taps. Water wastage is something our planet, on the whole, cannot afford right now. Big data helps in monitoring the water usage on a base level and makes the water management pro-environmental. Through Big data algorithm and predictive analytics, one can easily trace the water usage per house, per society and per locality. Furthermore, with the use of predictive analytics, it can also give an ideal usage ratio and volume. This can further help the municipal corporations to strictly adhere the per day volume guidelines. Predictive analytics would further answer some very important questions like How much water do you need? How much water should you use? How much water should be allocated per day? 

  • The Extinction Crisis

Extinction of endangered species is another common threat that we have been facing these days. Be it the extinction of plants or rare animals, this causes a major upheaval on the entire ecosystem. Big data helps in giving the right forecasts and analysis as per the current condition and rate at which these animals and plants are either diminishing or increasing. The right information gathered at the right time helps in creating a well chalked out plan to battle the problem and take necessary steps towards saving and preserving the species. 

  • Deforestation

Deforestation is one of the greatest concerns for the environment. Thousands of acres of woodlands are destroyed every day. It causes enormous, negative impacts on the environment. It’s an unfortunate process that removes not only life-sustaining trees but also hundreds and thousands of other plant and animal species. On the side of companies responsible for deforestation, Big Data provides alternative solutions to the immense tree-cutting done every day — lowering the carbon footprint and decreasing the negative impact on the ecosystem.

Deforestation has been a raging concern for a decade now. Thousands of acres of fertile land are being spoilt every day causing a lot of negative impact on the environment. Big data can help the companies provide analysis and offer them alternate solutions to the trees being cut.  Companies can use an enhanced predictive algorithm to have more sustainable and enhanced building solutions that are friendly to the environment as well as do not provide any hindrance to the development. 

Conclusion

Big Data is extremely pivotal in order to move ahead and provide our future generations with a sustainable future. The more expertise and intelligence we gather, the better protection we offer to our environment. This will ensure that species are being saved and any other form of potential crisis is averted to the optimum capabilities. 

Warehouse Availability and Stock Reduction

The problem

In all warehouses poor availability is a major concern, as the impact is felt at every point in the supply chain, from distribution through to the retailers. Availability issues quickly impact profitability and can cause significant issues for businesses of all sizes. It is easy to believe that a large warehouse must have great availability levels, but this is not always the case.

The client we worked with was struggling to meet availability requirements, due to the pressure of holding large levels of undue stock at the expense of capital expenditure. As an ambient food warehouse based in the UK, the challenge to balance availability with year on year stock reduction targets is not uncommon.

Our solution

The warehouse held over 2000 product lines in stock, so we understood that our solution would need to take this vast inventory into account when planning availability and stock reduction methods.

The Cloudaeon team began by analysing and consolidating multimillion row data sets to gain an insight into the warehouse product flows. This meant we could create an insight tool to highlight the causes of poor availability and undue stock holding issues.

The insight tool was used to create actionable insights which the operations team could put into action. Once the tool was incorporated into the workflows, it became clear that there were significant improvements to the line availability and stock holding positions. The insight tool also took into account the capacity of the warehouse to meet demands in the future should the business grow and expand.

The result

It was clear that our system led to significant improvements in the warehouse with a 3.7% increase in individual line availability. Our system also took into account the future demands of the warehouse, by reducing stock holding by 15% year-on-year.

Route to Market and CO2

The problem

One of the most important aspects within retail and logistical businesses is establishing an efficient route to market. The distribution chain will play a significant factor in a variety of issues from the end-to-end cost price through to the carbon emission levels.

In an ever-changing market with complex demand forecasts, there is a requirement behind the scenes for a constantly developing intelligence system. We were tasked by UK-based retailer 3PL to overhaul their transport strategy, to ensure that each aspect was working at optimum level.

The company were struggling to find the right solution which would address their ‘Route to Market’ cost challenges, within the constrained capacity of their struggling distribution centres.

Our solution

The first stage in establishing a solution for our client was to optimise the current ‘Route to Market’ strategy, which involved 8 distribution centres and over 1000 stores. The Cloudaeon team were able to build a fully explainable on demand AI model, which is designed to fit seamlessly with the whole business network.

The model is able to perform billions of permutation combinations, which result in a reduction of vehicle miles and lower carbon emissions. Before launching our complete solution, we ran a series of trials together with 3PL which were based on the output of our AI model. This included changing transport links between the different distribution centres and trial stores, with results showing a clear reduction in vehicle miles.

The result

Once the trials were complete our AI model was implemented across the business, through an on-demand user-controlled system. The system was able to address supply chain issues to establish the best possible ‘Route to Market’. By the end of the year the company was able to reduce annual miles by an impressive 7 million.

Reduce High-value Stock Loss

The problem

The number one threat which most grocery stores face today is the loss of stock through shoplifting. The high threat level on valuable items means that stores across the UK are implementing measures to make products harder to steal. Although these measures can lower the levels of theft, they can also make it difficult for customers to purchase products due to accessibility issues.

We were approached by a UK-based food grocery store who were struggling with the impact of shoplifting. The profits were significantly impacted, with both floor planners and store managers becoming frustrated in their efforts to avoid large losses.

Our solution

Our first step in assisting the company was to find a way to reduce the level of loss on high-value stock. The Cloudaeon team consolidated a variety of data sets at the most granular level possible, which enabled the team to build region specific store insights.

This insightful information was designed to highlight patterns and also the most likely root causes of stock loss. Once the insight tool was ready our team ran a trial for twelve weeks by collaborating with both floor planners and store managers.

The result

Using the actionable insights together we were able to make changes to the floor plan, which helped in reducing high-value stock loss without having a negative impact on sales. These changes led to a £700,000 year-on-year reduction in stock loss against like for like stores.

Supplier Delivery Accuracy

Problem statement

For any retailer, one of the key aspects of success is ensuring a steady store stock availability. With all retailers focusing on stock levels to drive improved sales and a better customer experience, it is essential to have an efficient and reliable system in place.

In a complex supply chain, one of the most important factors in maintaining high levels of store availability is to ensure supplier delivery accuracy. We were tasked by a UK retailer and their supplier network to develop a solution which would improve and maintain delivery accuracy. Even though the whole supply chain was working towards the same goal, they were struggling to find a solution.

Our solution

With over 150 suppliers and more than 8000 product lines we knew we would have to work very closely with the supply chain planner (SCP) and the various suppliers. To improve supplier delivery accuracy, we set up an automated consolidation of the relevant delivery data at the most granular (PO) level.

We built a transparent and easy to access system of automated insights for both the retailers and suppliers. The insights were designed to show clear patterns and to help the whole network discover delivery accuracy facts. To guarantee ease of access we designed an app which could be instantly accessed through mobile devices. This app would provide the retailer and suppliers with insights relating to deliveries whenever they might need it.

The result

Once the solution was implemented the network experienced an impressive 1.72% increase in supplier delivery accuracy. By working together using the same insights both the retailer and suppliers were able to access the same KPIs, to improve collaboration and delivery performance.

To ensure every business involved in the supply chain is able to monitor performance and establish new ways to improve, the system is designed to provide performance trend insights. This information can be used by the suppliers to find the root cause of any delivery issues, to quickly find ways to improve.

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