ESOS Guide for UK Manufacturers Available

The Engineering Employers’ Federation (EEF) is the UK’s largest sectoral structure. Its goal is to promote the interests of manufacturing, engineering and technology-based businesses in order to enhance their competitiveness.

EEF has positioned itself in London and Brussels in order to be in a position to lobby at EU and Westminster level. Part of its role is helping its members adapt to change and capitalise on it. When it discovered that a third of UK manufacturers must comply with ESOS (and 49% had not even heard of it) EEF decided it was time to publish a handbook for its members.

According to EEF’s head of climate and environment policy Gareth Stace, For the many manufacturers that have already taken significant steps to improve energy efficiency, ESOS can be viewed as a ?stock taking exercise?, ensuring that momentum is maintained and new measures are highlighted and taken when possible?.

He goes on to add that others that have not begun the process should view it as an ‘impetus’ to go head down and find the most cost-effective ways to slash energy costs. Ecovaro adds that they would also have the opportunity to reduce carbon emissions almost as a by-product.

Firms with more than 250 employees, over 250 million revenue or both must comply with ESOS across all UK sectors. In simplest terms, they must have conducted an energy audit by 5th December 2015, and logged their energy saving plan with the Environmental Agency that is Britain?s sustainability watchdog.

The Department of Energy & Climate Change (DEEC) that oversees it believes that large UK businesses are wasting ?2.8 billion a year on electricity they do not need. Clearly it makes sense to focus on larger targets; however EcoVaro believes those halfway to the threshold should voluntarily comply if cutting their energy bills by 25% sounds appealing.

We are able to assist with interpreting their energy audits. These are often a matter of installing sub-meters at distribution points, and reading these for a few representative months to establish a trend. Meters are inexpensive compared to electricity costs, and maintenance teams can install them during maintenance shutdowns.

Ecovaro helps these firms process the data into manageable summaries using cloud-based technology. This is on a pay-when-used basis, and hence considerably cheaper than acquiring the software, or appointing a consultant.

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New Focus on Monitoring Soil

There is nothing new about monitoring soil in arid conditions. South Africa and Israel have been doing it for decades. However climate change has increased its urgency as the world comes to terms with pressure on the food chain. Denizon decided to explore trends at the macro first world level and the micro third world one.

In America, the Coordinated National Soil Moisture Network is going ahead with plans to create a database of federal and state monitoring networks and numerical modelling techniques, with an eye on soil-moisture database integration. This is a component of the National Drought Resilience Partnership that slots into Barrack Obama?s Climate Action Plan.

This far-reaching program reaches into every corner of American life to address the twin scourges of droughts and inundation, and the agency director has called it ?probably ?… one of the most innovative inter-agency tools on the planet?. The pilot project involving remote moisture sensing and satellite observation targets Oklahoma, North Texas and surrounding areas.

Africa has similar needs but lacks America?s financial muscle. Princeton University ecohydrologist Kelly Caylor is bridging the gap in Kenya and Zambia by using cell phone technology to transmit ecodata collected by low-cost ?pulsepods?.

He deploys the pods about the size of smoke alarms to measure plants and their environment.?Aspects include soil moisture to estimate how much water they are using, and sunlight to approximate the rate of photosynthesis. Each pod holds seven to eight sensors, can operate on or above the ground, and transmits the data via sms.

While the system is working well at academic level, there is more to do before the information is useful to subsistence rural farmers living from hand to mouth. The raw data stream requires interpretation and the analysis must come through trusted channels most likely to be the government and tribal chiefs. Kelly Caylor cites the example of a sick child. The temperature reading has no use until a trusted source interprets it.

He has a vision of climate-smart agriculture where tradition gives way to global warming. He involves local farmers in his research by enrolling them when he places pods, and asking them to sms weekly weather reports to him that he correlates with the sensor data. As trust builds, he hopes to help them choose more climate-friendly crops and learn how to reallocate labour as seasons change.

A Business Case for Sharing

We blogged about sharing services in a decentralised business context recently, and explained why we think why these should be IT-Based for speedy delivery. This is not to say that all shared services projects worldwide have been resounding successes. This is often down to the lack of a solid business case up front. We decided to lay out the logic behind this process.

Management Overview ? The overview includes a clear definition of why the current situation is unacceptable, the anticipated benefits of sharing, and an implementation plan were it to go ahead. The project should not proceed until the stakeholders have considered and agreed on this.

Alternatives Considered ? The next stage is to get closer to the other options in order to determine whether an alternative might perhaps be preferable. Substitutes for shared services are often doing nothing, improving the current method, and outsourcing the service to a third party.

The Bottom Line in Business ? Sharing services comes at an initial cost of infrastructure changes, and the impact on human capital (the latter deserves its own blog). The following need careful consideration from the financial angle:

Numbers to Work Through

  • Manpower to design and roll the project out in parallel with the existing organisation.
  • Capital for creating facilities at the central point including civil works, furniture and equipment and IT infrastructure.
  • The costs of travel, feeding and accommodation. These can be significant depending on the time that implementation takes.
  • The opportunity loss of diverting key staff – and the cost of temporary replacements – if appointing line staff to the project team.
  • Crystal-clear project metrics including (a) the direct, realisable savings (b) the medium and long-term effects on profit and (c) where to deploy the savings

Risk Management

Shared services projects don’t go equally smoothly, although planning should reduce the risk to manageable levels. Nonetheless it is important to imagine potential snags, decide how to mitigate them and what the cost might be.

We believe in implementing shared services on a pilot basis in the business unit that eventually provides them. We recommend building these out to other branches only when new processes are working smoothly.

Moving On From a Decision

We recommend you revisit your management overview, the logic behind it, the assumptions you made, and the costs and benefits you envisage before deciding to go ahead

The final step in proving a business case is doable should be fleshing out your roadmap into a detailed operations plan with dependencies on a spreadsheet.

Migrating from CRM to Big Data

Big data moved to centre stage from being just another fad, and is being punted as the latest cure-all for information woes. It may well be, although like all transitions there are pitfalls. Denizon decided to highlight the major ones in the hope of fostering better understanding of what is involved.

Accurate data and interpretation of it have become increasingly critical. Ideas Laboratory reports that 84% of managers regard understanding their clients and predicting market trends essential, with accelerating demand for data savvy people the inevitable result. However Inc 5000 thinks many of them may have little idea of where to start. We should apply the lessons learned from when we implemented CRM because the dynamics are similar.

Be More Results Oriented

Denizon believes the key is focusing on the results we expect from Big Data first. Only then is it appropriate to apply our minds to the technology. By working the other way round we may end up with less than optimum solutions. We should understand the differences between options before committing to a choice, because it is expensive to switch software platforms in midstream. data lakes, hadoop, nosql, and graph databases all have their places, provided the solution you buy is scalable.

Clean Up Data First

The golden rule is not to automate anything before you understand it. Know the origin of your data, and if this is not reliable clean it up before you automate it. Big Data projects fail when executives become so enthused by results that they forget to ask themselves, ?Does this make sense in terms of what I expected??

Beware First Impressions

Big Data is just that. Many bits of information aggregated into averages and summaries. It does not make recommendations. It only prompts questions and what-if?s. Overlooking the need for the analytics that must follow can have you blindly relying on algorithms while setting your business sense aside.

Hire the Best Brains

Big Data?s competitive advantage depends on what human minds make with the processed information it spits out. This means tracing and affording creative talent able to make the shift from reactive analytics to proactive interaction with the data, and the customer decisions behind it.

If this provides a d?j? vu moment then you are not alone. Every iteration of the software revolution has seen vendors selling while the fish were running, and buyers clamouring for the opportunity. Decide what you want out first, use clean data, beware first impressions and get your analytics right. Then you are on the way to migrating successfully from CRM to Big Data.

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