AI for Coffee Manufacturing: 3 Ways AI is Energizing The Coffee Industry

By Prateek Joshi:

From bean to barista, the global coffee industry is valued at over $100 billion. For a producer, distributor or manufacturer in this massive industry, the use of AI for manufacturing can play a vital role in optimizing critical processes. 

Specifically, in retail, agricultural and manufacturing operations, the coffee industry is discovering ways AI applications can benefit everyone from small-scale farmers to large industrial plants. Below are three examples where AI is benefiting the coffee industry.

  1. AI is helping farmers protect their crops.

The increased use of AI applications in agriculture has the potential to help farmers across the world protect their beans from disease and optimize growing conditions by monitoring factors like soil and moisture levels. AI has proven to be especially beneficial to farmers in developing nations in Latin America and Africa, where they can utilize advanced warnings about pests that threaten their crops, as well as receive data-driven insights that can help them adapt to the effects of climate change.

With the price of coffee beans at some of the lowest in a decade, AI and machine learning can provide actionable intelligence and decrease the negative impacts of the massive, yet volatile market. 

  1. AI is helping coffee at the industrial level.

The benefits of IoT-enabled AI are not restricted to the farm. Industrial operations perhaps have the most to benefit from the technology. And with some of the largest coffee companies in the world like Starbucks seriously investing in AI solutions for their industrial processes, the rest of the industry is undoubtedly paying close attention. For example, the word ‘digital’ was used over 40 times on a Starbucks investor call in Q4, 2019, which can only suggest that finding and investing in technology solutions is top-of-mind in the boardroom. 

These digital initiatives are improving industrial processes at coffee manufacturing plants in a number of ways. From decreasing equipment downtime at bottling facilities to monitoring the performance of key assets such as water, chemicals and labor at large plants.  

  1. AI is providing advanced insights into transportation and logistics.

While IoT and AI applications are still in their relatively early days of use, the technologies are quickly gaining steam and don’t appear to be a passing trend. Leading authorities have predicted that half of all manufacturing supply chains will be using some form of AI by 2021.

The use of AI by large coffee producers like Starbucks shows that AI is continuing to deliver numerous benefits to coffee growers and producers. AI is providing benefits to the supply chain such as identifying what time of the year is most advantageous to carry specific varieties in stores. 

Further up the supply chain, AI applications can predict order patterns to reduce or eliminate penalties caused by missed OTIF (On Time in Full) deliveries, providing benefits to every stakeholder in the process.

As outlined above, there is no place in the coffee industry that the use of AI wouldn’t benefit. On the production and retail side, companies in the coffee industry can automate inventory orders and predict equipment maintenance and staffing needs. For farmers, increased AI usage can lead to an improvement in water quality, increased efficiency in coffee processing, packaging, as well as provide a positive impact on the overall bottom-line. On the manufacturing side, issues like unplanned equipment downtime and asset quality can be mitigated with the right AI system. 

Curious about how to get started with AI in your company? Be on the lookout for our new report in January of 2020 that explores some of the challenges manufacturing companies face in getting their AI projects off the ground, as well as how to overcome them. 

A Day in the Life: Where an Industrial Operator’s Time Goes

Steve is a manager at an industrial beverage plant that produces bottled soft drinks. Accessing, analyzing, and sharing data about the daily performance is an integral part of his job and one that can often be tedious and time-consuming. 

Resources like energy, chemicals, and water all play a role in the quality of the end product the plant produces as well as the profit margins Steve and his team can achieve. Manual and legacy data management processes can eat up a serious portion of an operator’s day. 

The following illustrates the challenges that workers like Steve experience throughout the day in an attempt to manage and make sense of their data. 

Monday: 9 a.m.

Steve gets to his desk and opens an email from his colleague about the performance of a new piece of equipment the plant installed last week. He doesn’t quite remember what the data in the spreadsheet is measuring, but it doesn’t look good. He searches back through last week’s emails to jog his memory.

He clicks download on the Excel spreadsheet attachment in his colleague’s email, only to get a pop-up window that says he needs to update his Microsoft Office Suite in order to open the document. He asks himself, “Where is that activation code, again?”

He opens the spreadsheet and has to correct some of the data formulas that didn’t import the right way, he starts reading through the 13 tabs in the document. The numbers don’t look right for some reason. He swears it was performing perfectly when he read the initial read-outs from his technician last Friday. Steve rifles through the thick portfolio on his desk for the printout the technician gave him last week. He can’t find it. “I’ll have to give that tech a call,” he says.

Steve then gets a voicemail message saying that particular technician is out sick today. The report will have to wait. 

1 p.m.

After back-to-back meetings, Steve gets called down to the factory floor to inspect a piece of equipment that has automatically shut off due to a malfunction. Production is at a standstill as he and his team try to figure out what went wrong with the machine.

After sifting through dozens of printouts and warning screens on the equipment itself, he and the team discover the machine was overheating. Things get frantic as the plant sits idle, so Steve makes an executive decision to adjust the cooling system on the equipment to a temperature his gut tells him will work (he has over 25 years of experience, so his intuition is spot on, right?)

3:30 p.m.

Steve gets back to his desk and opens the spreadsheet from the morning. He realizes the report from the email was showing the coolant malfunction in the machine he just had to deal with on the factory floor. He has access to all this data, but it’s spread out across so many different sources that he can’t make the appropriate decisions that will lead to meaningful actions. He combs through the spreadsheet to see if the gut-based temperature adjustment he made earlier was the right one.

He’s way off…

Like thousands of other industrial operators, Steve can’t make real-time adjustments to his plant’s processes when his data is locked in legacy and manual systems. He would benefit from a centralized platform that can offer him real-time updates on his plant’s processes and assets, as well as automated recommendations and solutions on how to fix problems when they arise. 

After that nightmare of a day, Steve has to spend the next morning looking for ways to deal with equipment downtime and the issues that spreadsheets and other legacy methods have been causing him.

Increasing their industrial intelligence By installing advanced automated sensors powered by an AI system, Steve and his team can monitor critical assets and conditions around the clock in a clear and simple readout that is always up to date. And when emergencies arise, the right AI system can automatically make adjustments and recommendations before a time-wasting issue halts production.  

Does any of Steve’s day sound familiar to you or your team? Unplanned downtime can cost manufacturers an estimated $50 billion annually. It may be time to reevaluate your relationship with your data.