1.    Integration

As an existing Qlik customer, most of your organization’s resources will already be connected to it and sanitized – if you’re lucky. In other words, you can make use of an already existing catalog of resources. If you are a new Qlik customer, then once you retrieve your data for the AutoML project, it can be used by other analysts in different dashboards, automation, and ML projects in the future.

Likewise, your ML model is part of the catalog, enabling you and others to use it in different places. One dashboard might load predictions weekly, whereas another may need real-time predictions. With Qlik AutoML, one deployment object covers both cases without cluttering your assets.

2.    Discovery

As a Data Scientist, it is essential to understand the data you are modeling. Qlik’s associative engine provides a unique opportunity to explore your data. You can build (or reuse) different visualizations, filter, select multiple dimensions, and create measures (a.k.a. engineer features) on the fly – all in one place.

As a Data Analyst, you’ll eventually be building some visualizations to make sense of the predictions too, so it is potent to be able to do this to a high standard as part of your ML process. And, as Qlik is designed with self-service in mind, you will even be enabling your end-users to explore the data while you move on with the modeling stage.

3.    Speed

Machine Learning projects are naturally iterative and creative, and there are few things worse for that creative process than having time as a barrier to experimentation. The above points about integration and discovery help move the project along quickly, but just as deserving of merit is the speed of generating new models. You can add new features, remove redundant ones, and develop a new set of models to choose from in minutes.

Once you’ve settled on a model, you can tell Qlik to spend some hours optimizing the model, but that doesn’t have to bottleneck the last stage of the project, arguably the most critical part – to serve the predictions to users/systems.

4.    Versioning

Qlik AutoML makes it easy to keep track of model performance for different algorithms and sets of features. You can see the full story of how model performance is affected, which is great for experimentation. This feature also ties in directly to deployments; when you generate a new model version, you can choose to deploy it and remove the old deployment on the same screen.

5.    Real-time

I have seen few solutions that produce real-time predictions as quickly as Qlik AutoML.

Real-time predictions enable a whole new class of use cases. For example, retrospectively optimizing an industrial process to reduce waste is one thing. However, if you could do it as part of the setup, you could use machine learning to hit the ground running.

In addition, running “what-if” scenarios is a rare and engaging way for end users to work with your data. It can often be more intuitive than seeing the results of an algorithm’s predictions. Then, when you serve these predictions in a Qlik App, the user is next to visualizations that give them context and encourage them to use your BI solutions.

In closing, Qlik AutoML fits seamlessly with existing BI workflows, massively reducing the barrier to predictive or prescriptive analytics. The ease of spinning up a new predictive analytics solution allows you to deliver a high volume of projects that would otherwise take a long time to produce with other tools, so consider every office or department in your organization and how they could benefit.

We are very happy to announce that the Pomerol Writeback Extension for Qlik Sense has been accredited into the Trusted Extension Developer (TED) program. The TED accreditation insures you a stable, supported, high quality solution.

See our TED page: https://developer.qlik.com/ted/5eb177184514100012efccc9

Qlik’s TED Program includes:

Extension Accreditation: Reduces risk of code failure on upgrade, ensures security protocol and data collection compliance, and allows accreditation on additional functionality such as responsiveness and mashup readiness.

Best Practices & Enablement: Detailed requirements documentation, test script creation guidance, monthly webinars, and open feedback channels like Slack.

Testing Tools: Qlik supported automated testing tools, and test script validation with Qlik Sense via a CLI to improve submission quality.

Assured Upgrades: TED members sign up with a commitment to provide on-going updates to their accredited extensions, as well as ensuring customers can receive support during their use.

All accredited extensions are listed on Qlik Market. Nevertheless, you will obtain the subscription and the support directly from Pomerol Partners.

What is the Pomerol Writeback Extension?

Our integration for Qlik Sense allows you to edit and enter data directly into your published applications. See the effects of your changes instantly. Reduce spreadsheet risk. Avoid task switching and complex data consolidations. Help your team by giving them a tool to increase productivity, and ensure your business makes the best decisions based on the right data.

The Writeback extension helps enterprise organizations save time, and money by:

• Improving collaboration across your teams.
• Improving the speed and efficiency of your data cycle.
• Improving the quality and integrity of your data.

See Pomerol’s Writeback extension in action!

One of the first things I do at the beginning of the month is review the activity in the monitoring applications.  We use Qlik everyday to analyze data that drives strategic decision making, so it only makes sense to take that same approach to understanding how Qlik is being utilized within our environment.  In order to do this, we take a quick look at a few key elements of those monitoring tools.

For our clients that have NPrinting, we’ve developed a standard report that shows those key elements of the operations monitor and the license monitor for the past month. For those that don’t, we have created additional views in the monitoring applications to do the same.

First up is a quick glance at the engine CPU and engine RAM monitoring for the past month to be aware of any spikes to these two graphs. We can quickly investigate any significant or consistent spikes across the previous month leading us to important questions about our environment:  Do we have a cluttered reload process?  Is there a problematic app causing spikes in the CPU or RAM? Are there users causing issues with a private sheet?

The next item to consider is a review of the most active applications in the environment by tracking sessions in the last month. Usually, I split this out to look at the last 14 days, 21 days, month and 90 days. By analyzing this you can quickly spot any trends in the applications usage while also understanding which applications are getting the most attention in your environment. With this information, you find timely answers to important questions about how your applications are being utilized.

Understanding application usage can be furthered in more recent versions of Qlik Sense by analyzing sheet usage. Monitoring sheet activity can help you consolidate large apps if needed or begin an investigation into why sheets are being underutilized: Are those sheets delivering accurate results? Is more detail needed? Do users understand the visualization created?

We also want to review user session activity. By monitoring this you can quickly understand which users are utilizing their Qlik access and which are not. Understanding your most active users can identify internal champions to drive collaboration and more advanced development.  Monitoring your inactive users can raise essential questions around how to drive engagement from allocated users who have no recent or very limited activity. Are these users getting the information they need?  Is training required for the users to better understand their dashboards? Can these allocated licenses be put to better use elsewhere?

With the above considerations, we can leverage the monitoring tools to answer questions about our environment, but more importantly, we can leverage these tools to drive engagement of our users towards our most critical applications and get the most out of your Qlik environment.

Learn More about Qlik’s Monitoring Apps

Qlik’s OpenAI connector is a powerful tool that allows you to leverage the capabilities of OpenAI’s natural language models within your Qlik apps. 

What is the Qlik OpenAI Connector? 

The Qlik OpenAI Connector is a Qlik Sense extension that enables you to send prompts to OpenAI’s natural language models and receive responses in your Qlik apps. You can use the connector in various ways, such as: 

1. In chart expressions, to generate natural language insights or explanations based on your data 
2. In the data model, to enrich your data with additional information or analysis from OpenAI 
3. In application automation, to automate tasks or workflows that involve natural language processing or generation 

How does the Qlik OpenAI Connector work? 

To use the Qlik OpenAI Connector, you need to have an account at platform.openai.com and pay a fee based on the computing resources you use. However, there is a free trial available. You will find the connector under “analytics sources” when creating a new data connection. 

With the connector, you can create prompts using variables, fields, or expressions from your Qlik app. A prompt is a text input that tells OpenAI what kind of response you want. For example, you can ask OpenAI to write a summary, suggest a reply, generate a question, or complete a sentence. 

You can select different models to send your prompts depending on your use case. OpenAI offers several models with different capabilities and trade-offs, such as: 

Davinci: The most advanced and versatile model, capable of generating high-quality and diverse responses for any prompt 

Curie: A smaller and faster model than Davinci, suitable for tasks that require less creativity or complexity 

Babbage: A specialized model for numerical or tabular data analysis 

Ada: The smallest and cheapest model, suitable for simple tasks that require factual or straightforward responses 

You can also customize other parameters of your prompts, such as: 

Temperature: How creative or diverse the responses are. A higher temperature means more randomness and variety, while a lower temperature means more predictability and consistency. 

Max tokens: The maximum number of tokens (words or symbols) the model can generate as a response. A higher max tokens means a longer response, while a lower max tokens means a shorter response. 

Top P: The probability threshold for selecting the next token. The model will only consider the tokens with a cumulative probability of less than or equal to the top P value. A higher top P means more diversity and exploration, while a lower top P means more confidence. 

Frequency penalty: How much the model avoids repeating words or phrases. A higher frequency penalty means less repetition and redundancy, while a lower frequency penalty means more repetition and emphasis. 

Presence penalty: How much the model prefers using new words or phrases. A higher presence penalty means more novelty and diversity, while a lower presence penalty means more familiarity and similarity. 

What are some use cases for the Qlik OpenAI Connector? 

The Qlik OpenAI Connector opens a world of possibilities for using natural language in your Qlik apps. Here are some examples of how you can use it. 

Summarizing public sentiment toward a product or company 

You can use the connector to analyze social media posts, reviews, or feedback about a product or company and generate a summary of the main themes or sentiments. For example, you can send a prompt like “Write a summary about the reception towards ” & [Product] & ” by ” & [Company] and get a response like: 

“The reception towards Product X by Company Y is mostly positive, with customers praising its quality, design, and functionality. However, some customers have complained about its high price and poor customer service.” 

Suggesting responses to customer support tickets 

You can use the connector to help your customer support representatives handle tickets faster and more effectively. You can send a prompt like “Suggest a response for a customer support representative for the following case: ” & [ticket_text] and get a tailored response like: 

“Dear Customer, 

Thank you for contacting us. We are sorry to hear that you are having trouble logging in to your account. We apologize for any inconvenience this may have caused you. 

We have fixed the issue, and you should be able to log in now. Please try again and let us know if you still need any help. 

Thank you for choosing our service. 

Sincerely, Customer Support Team” 

You can then use application automation to send this suggestion to a Teams channel or an email address. 

Generating social media posts from job descriptions 

You can help your Marketing department by generating social media posts. You can send a prompt like: “Write a LinkedIn post for the following job description: “ & [raw_job_description] and get a response like: 

“📢 Are you a junior/mid-level BI consultant looking for an exciting opportunity? 🤔 Pomerol Partners is hiring! 🎉 We are looking for someone with a degree in Computer Science or equivalent, 2-3 years of Software Engineering experience, knowledge/experience with Microsoft Power BI, ETL processes and Data Modelling, Data Bases (Postgres, SQL Server, Oracle) and SQL. You should have functional knowledge on reporting and data analysis, an analytical mind with a problem-solving aptitude, self-motivation and commitment to deadlines, be a team player and have good verbal and written English communications skills. 🌍 You will participate in technologically attractive and international projects; receive training and certifications; enjoy hybrid work; and be integrated into a young, solid, ambitious and motivated team. 🤝 Send us your CV in English! #PomerolPartners #BIConsultant #JobOpportunity #DataAnalysis #ProblemSolving #TeamPlayer #HybridWork” 

You can do this in the data model to store responses and create a results library. 

These are just some ways you can use the Qlik OpenAI Connector to enhance your data analysis and communication. Don’t hesitate to get in touch with us if you want to learn more about this connector or see it in action. 

We hope you enjoy using the Qlik OpenAI Connector and discover new insights and opportunities with OpenAI’s natural language models. 

Our appetite for information at work is thriving and the companies that can adapt their culture and attitude to information and analytics will be able to service this demand and see a competitive advantage over their peers who are two steps behind.

In this age of information overload, we’ve become accustomed to having the answers to all our questions at our fingertips, so building a self-service business intelligence is a necessity. This tide change in our expectations is naturally flowing over into the workplace. Our appetite for information at work is thriving and continues to grow. Additionally, the companies that can adapt their culture to information will be able to service this demand. This will allow them to have a competitive advantage over their peers who are two steps behind.

Enabling your workforce with the information they need to make better business decisions is a win-win, and in today’s business environment that means building a self-service analytics platform. But what’s the secret recipe? Successful business intelligence (BI) and analytics projects have similar characteristics, here are just a few.

1. Who Builds a House Without a Plan?

Establishing a strong self-service strategy is critical for success but is often underappreciated and overlooked. More than just an ‘idea about where you’re heading and who’s going to participate’, it’s a comprehensive plan. The plan maps out the people, resources, and objectives that will drive the project forward and ensure business value is maximized.

Self-service BI projects are often chartering new territory within the organization, so it’s important to maintain an agile and flexible approach so you can deal with the curveballs along the way. However, it’s also critical to recognize that when creating something new and challenging, the norms within an organization being able to communicate a firm plan is more important than ever.

Get the right people together and ensure they have the time to dedicate to the initiative. All too often the project team is a winning mix on paper, but competing responsibilities reduces people’s effectiveness and hinders the impact of the team as a whole.

In addition to this, finding the right executive sponsor for your project is critical as they can become a magnet for resources.

2. Find an Executive Champion to Fly the Flag

Finding an executive sponsor is pivotal to creating a successful self-service business intelligence project for several reasons. Vision, experience, and sway within the organization are all important, but so is the ability for a senior leader to create drive and accountability amongst the data project team.

An executive sponsor can help guide change with their wider enterprise perspective, identifying potential bottlenecks or conflicts in advance that may not be seen by other team members. Naturally, the executive is in a better position to eliminate these roadblocks and ensure the path to success is clear.

The modern BI project will often challenge traditional IT policy and workflows. Therefore,  having an executive on board to help justify and facilitate your demands for change makes a significant difference.

3. IT and the Business Should be Buddies

Traditionally, IT was the gatekeeper to all things data. They would prepare, analyze, and then report to the business. However, this left many unanswered questions and with no ability to dig deeper and the value generated by this style of reporting was limited.

Moving forward to the first versions of self-service, companies tried to reduce the hoops to jump through by providing curated cuts of data to business functions to build their own reporting. This was a step in the right direction but brought with it other issues. Some of the issues included inconsistency in views for different business units and a lack of visibility into the logic that created those views.

Come 2022, the modern analysis environment sees IT and the business working in partnership. While still having the overarching responsibility of governance and security, IT’s role is to empower the business and achieve the strategic goals defined at the top table.

A modern approach to analytics combined with the latest tools means security and governance don’t have to be compromised in a direct trade-off to innovation and progress. Modern self-service business intelligence tools have governance baked in. In many cases the business can be self-sustainable once IT has laid the ground rules. Instead of throttling back the torrent of data, IT can set up guard rails to help navigate and direct the users to success.

From Data Capture to Analytics–The Game is Changing Fast!

The last ten years has seen our ability to capture and record information grow exponentially. On the contrast, the cost of doing so has plummeted. Companies have raced to prop up storage with traditional databases growing into the ‘data lake’. In many cases, it is an understatement as the data lake has grown into an uncharted ocean. Generating value from this ocean of data and empowering users to take advantage of is the hurdle most companies see before them.

A robust self-service platform channeling useful information to the front line will become the standard within organizations. New and innovative tools tackling data pipelines, NoSQL storage, and machine learning are also bursting onto the scene. Therefore, we need solid BI platforms with sound foundations to bolt on to. Data literacy is on the rise and the appetite is there, so time to roll up the sleeves. What more could you want than a hungry customer?

Effective data teams will be tasked with creating a program that not only measures success but also inspires employees to take ownership of the outcomes.

Introduction

Data can be immensely powerful for companies looking to use technology to improve their performance, when used correctly. Data specialists can help your organization make more informed decisions with the information you have, improving its efficiency and helping you avoid pitfalls that could prove disastrous for your business. But data science is about much more than just providing recommendations – it’s about creating a culture of data-driven decision making throughout your organization. In this article, we’ll look at how you can use data to inform decisions in your company, why having a data-driven culture is important for success, and how to build one of these cultures at your firm or organization.

What are the benefits of a data-driven culture?

A data-driven culture is a culture where data is used to make decisions, to measure success and inspire employees. While it’s easy to see the benefits of a data-driven culture from an internal perspective, let’s take a look at how it can benefit your customers too:

Using data for decision making allows you to find patterns in customer behavior that would have been hidden otherwise. You’ll be able to understand what motivates them and what doesn’t. This will help you create better products or services that match their needs and aspirations more closely.

Measuring success shows customers they are valued by you because they provide useful information about themselves when they interact with your business or organization. A good example of this is LinkedIn encouraging users who have added connections or liked content on its site over time by showing them posts from those people above others in their news feed; this means its content has been valued highly enough by other users so as not only makes itself visible but also encourages engagement amongst friends/likes/pages which keeps them coming back again and again!

This helps companies highlight key metrics around user activity which helps improve future iterations of product offerings based upon user input.

How to build a data-driven culture

As the data-driven culture is more and more important, the role of the data consultant is becoming increasingly critical. Without a strong team of data scientists to support their business, companies are leaving themselves vulnerable to mistakes that could be easily avoided with the right tools in place.

The first step in building a strong team and building an effective culture is understanding why it’s so critical:

• Data drives decision making
• Data drives business strategy
• Data drives customer experience

Creating an environment where everyone understands the value of data and believes in its ability to drive decision making

To create a culture of data science, you need to teach people why data matters. You can do this by educating them about the importance of data in decision making. Emphasize how important it is to understand the context behind decisions and not rely solely on gut feelings.

You can also allow people to see how powerful your toolset is by showing them its capabilities through demos, workshops, and other interactive sessions. If you’re working with a smaller team that might not be able to attend these events (or if they aren’t available), make sure each member has access to all the resources they need online so they are able to keep up with their colleagues who are learning on-the-go.

Deciding on metrics

Once you’ve defined your goals and objectives, it’s time to identify the metrics that will help you achieve those goals. You want to make sure the metrics you choose are quantitative and measurable. In other words, they should be focused on outcomes rather than processes—for example: “We want to increase sales” versus “We want to increase inventory turnover.” They also need to be easy for everyone in your organization (not just data scientists) to understand; otherwise, they won’t have much value as a tool for communication or accountability. Furthermore, they must be easy for anyone in the company (including executives) who needs data-driven decision making tools at their fingertips—i.e., they need not require special training or knowledge of statistics in order for someone else within an organization’s hierarchy chain of command

How to use your metrics to make decisions

You’re probably familiar with metrics. They are a set of quantifiable numbers that provide useful information about your business. Metrics can help you understand what’s working, what isn’t, and how to fix it. You should definitely start using them!

That said, not all metrics are created equal; some are better than others at providing actionable insights into your business. Let’s take a look at two types of metrics that we recommend for data-driven decision making:

• Key performance indicators (KPIs) – KPIs show progress from the past few months or quarters toward a specific goal (for example, sales). They’re easy to access and track continuously throughout the year because they’re often delivered in charts or graphs on dashboards that display historical trends over time. Additionally, they’ve been well researched by both academics and professionals alike so we know which ones work best when used together within certain contexts (for example marketing vs customer service).

• Performance indicators – Performance indicators tell us how well we’re doing in specific areas but don’t give us much context as far as whether our efforts have been successful thus far—which makes them more difficult for decision makers since no actionable insights come from them unless there’s already been enough time invested into analyzing results first! However these can be very valuable when determining whether things need tweaking later down the line since many businesses tend towards perfectionism instead of experimentation (due largely because their “experiments” aren’t always considered successful before trying something else new again). These types include financial measures like ROI which show how much money was spent versus how much money was made during a given period/time frame etcetera.”

Where data is priority number one, the most important trait is trust

In any relationship, trust is an essential ingredient. It’s how people feel that you have their best interests at heart, and it’s what keeps them coming back for more. In a data-driven culture, trust is of the utmost importance—especially when your coworkers are all working on different projects and never actually meet face-to-face.

The key to building trust in this environment is transparency: if everyone knows what everyone else is doing, they can trust that everyone is working toward the same goals. For example: if one team member finds some new insight while conducting research on their own time, they should share it with the rest of their company so everyone can use it going forward (and maybe even incorporate it into their own work). Allowing employees this freedom creates a sense of ownership within each person’s role; they feel like they’re part of something bigger than themselves because they’re contributing toward something larger than themselves—which builds camaraderie among employees as well as brand loyalty among customers or clients who value such things!

Effective data teams will be tasked with creating a program that not only measures success but also inspires employees to take ownership of the outcomes.

To ensure that your data program is successful, you must also create a culture of data-driven decision making. This means that employees should be able to use data to make decisions and not just rely on the opinions of their managers or personal experience. It’s also important for your team members to understand how their work impacts the overall goals of the company.

When it comes to measuring success, effective teams will set goals and create programs with actionable metrics in mind. For example, if you have an email marketing campaign that you want people to sign up for (which would then lead them into becoming customers), then it would be important for your team members to know what percentage of recipients actually sign up after receiving an email from you instead of just sending emails blindly hoping something good happens.

Conclusion

To effectively create a data-driven culture, you must ensure that everyone understands and believes in the value of data. Trust should be at the core of your organization – if it is not present then you cannot hope to successfully implement a data program with success. Companies need to decide how they will use their metrics in order to make decisions about future actions – but always remember that these decisions should still be based on gut instincts as well as data analysis. Just like any other tool, there are limitations to what information can tell us about our world around us so never stop thinking for yourself!

Tecumseh Poultry was founded in 1998 based on a simple goal, “to produce the best fresh chicken in the United States”, and that’s what they do! Over 20 years they have defined and then implemented methods which result in the highest quality finished products. In other words, they do things the right way. Not necessarily the easiest, fastest or most efficient way. Simply the right way.

So, what exactly does this mean? Proper treatment and care of their chickens and people, and the utmost care and discretion in how they process and package their product. Furthermore, they do all this while keeping a careful eye on the environment and on the sustainability and renewability of the resources they rely on.

Beyond being industry innovators in their day to day operations, they’re also 10 steps ahead when it comes to how they analyze their business and are visionary in how they use data to identify opportunity for improvement. In fact, Smart Chicken has more advanced predictive analytics than many investment banks we’ve worked with. Recognizing this vision early on, Pomerol was very excited to be given the opportunity to help Smart Chicken take their BI and analytics to the next level.

“Smart Chicken has more advanced predictive analytics than many investment banks”

No shortage of business challenges….

There are many challenges unique to producing a perishable fresh product. The shelf life is short, high wastage would make the business model unfeasible, and there’s no real opportunity for inventory. So, supply and demand need to be in sync, and this requires precision planning and a perfect understanding of your market. Supply is relatively fixed in the short term, but demand can fluctuate wildly depending on time of year, weather and promotional activities. Believe it or not, demand for white breast meat skyrockets for the short period of time that our new year’s resolutions are still intact, before settling back down in February. Try planning around that!

Beyond this, there are a raft of challenges unique to poultry production. Football season sees a huge uptick in wings consumption, but last time we checked, a chicken only has two wings, so there’s no ability to increase production of this single SKU. In another twist, Thanksgiving and Christmas is a time of year where we all get together and eat whole Turkey, but that sees the demand for whole chickens plummet for November and December. Planning around these erratic peaks and troughs causes plenty of pain, but together with the Smart Chicken team we’ve developed some novel ways of optimizing the planning process through a combination of live analytics in Qlik dashboards, and machine learning assisted forecasting with Data Robot.

Using Qlik to shed light on the business

The power of Qlik is leveraged for both its ETL capabilities and as a data visualization and reporting platform. Powerful associative models consolidate data from ERP, CRM, live production and planning tools giving managers an asset to empower decision making.

The customer demand forecasting process is a critical step in the chain to running a successful operation. Visualizing the forecast in Qlik, managers can quickly move from a brand level right down to individual SKUs. Different ordering patterns unique to geographies are easy to see, and the demand fluctuations driven by weather, events, promotions and holidays are all evident when trended or exposed through geographic maps.

Qlik provides a great view into the forecasting and sales data, but with such diverse ordering patterns there’s real opportunity in perfecting the forecasting process right down to the SKU and customer level. Smart Chicken and Pomerol enlisted the help of Data Robot to apply Automated Machine Learning technology to the demand forecasting process.

Taking forecasting to the next level with Data Robot and Automated Machine Learning

What really separates Smart Chicken apart from their competitors is how tight their forecasting and planning process is. Representatives in the field have their finger on the pulse by chatting to customers in supermarkets and they feed anecdotal information back into the planning team, while back at head office demand is forecast right down to the SKU and customer level, something most companies would not dare attempt.

Instead of applying a single forecasting algorithm across all customers and SKUs, Data Robot has enabled Smart Chicken to mathematically cluster and segment similar customers and products based on volume and ordering behaviors. Unique forecasting models are then auto-generated for each unique cluster.

Pulling from a library of hundreds of predictive models, Data Robot ranks its recommended models based on performance, error and speed, and then recommends a model to be put into production. Without an in-house data science team, Smart Chicken uses the power of automated machine learning and predictive analytics to further refine customer demand forecasting.

Beyond demand forecasting

While customer demand is a pivotal data point for Smart Chicken, there are other parts of the business that can also benefit from the power of Data Robot. Predicting the growth trajectory and final weight of chickens can inform the planning process of anticipated supply. In a sector where inventory is almost non-existent, having a keen eye on supply avoids surprises and lets the customer support team know exactly how much produce they have available to distribute. Having advance notice of any expected shortfalls lets customer service pro-actively manage expectations.

In another industry first, Smart Chicken have installed a full end-to-end ‘air chill’ system – the only one of its kind in the US. While the impact this has on product quality is beyond measure, it’s a tricky piece of equipment to run and presents a risk should it fail. Smart Chicken are looking to Data Robot for predictive maintenance. By analyzing the events leading up to previous parts failures, we can begin to predict when another failure is looking likely. IOT sensors constantly measure conveyor belt motor amperage, temperature and tension. The most minute change in tension may indicate a stressed or worn ball-bearing, or a slight increase in motor temperature might indicate an increased level of strain. Being able to pro-actively identify issues before they fail during production hours can have a massive impact on continuity of supply.

Looking to the future

As consumers place a higher importance on the quality and origin of their produce, the market for Smart Chicken’s products is on the rise. The game-changing innovator from small-town Nebraska is setting the bar for the larger producers. We’re excited to be on the analytics journey with them!

Visit the Smart Chicken website: https://www.smartchicken.com