The need for food processing services is increasing as the world gets more fast-paced. Nowadays, when everyone on the earth, regardless of gender, is career-oriented and wants to establish a name for themselves in their various professions, they don’t have enough time to cook their food and are compelled to eat previously processed dishes that require a few minutes to get prepared.
Food processing businesses have proven to be a lifeline and a magic wand for working women. Many of our households rely on processed foods to run their kitchens, so we can say the processed food industry is an emerging industry that has not only helped working women save time and prestige, but has also contributed a significant amount of money to the economies of the countries, and it appears that it will continue to grow and contribute a significant amount to the GDP of many developed and developing countries.
As a result, the primary focus of any federal government should be to take steps to accelerate the expansion of food processing enterprises while also bringing quality standardization through the use of technology and high-tech machinery.
As the growth of food processing companies is also linked to the growth of all linked industries with it such as agriculture. To increase productivity, manufacturers must automate their processes.
Many manufacturers still collect data using time-consuming and labor-intensive manual techniques. According to a survey, 75% of manufacturers still use manual methods, 53% use spreadsheets to collect data, and 47% still use pencil and paper.
Many Ways Manual Data Collection Harms Productivity
Data gathering has been slower to catch up to other production techniques in terms of technological advancement. Insufficient data collection continues to plague the business, according to our study and on-the-ground experience.
It is one of the most common roadblocks to increased productivity and improved OEE, and it is primarily due to manual data collection methods. These factors influence not only data accuracy but also management decisions and the efficient distribution of jobs among manufacturing workers.
The difference between manually using computer tools and appropriately automating data collecting is obvious to hands-on operators and management.
There are four common issues with persisting to collect data manually:
Good Manual Metrics Become Bad Batched Metrics
You must observe personnel collecting data over time to fully comprehend the issues with manual data gathering. If data gathering is done manually, people, in my experience, stop writing down the outcomes after each event and instead start writing them down in batches.
This is a steady procedure that begins every other time, then every fourth time, and ends shortly before lunch and departure.
This can lead to the recording once a day or even once a week so real-time information is completely absent, as is a speedy response to unpredictable halts.
Because of the delay between when an event occurs and when it is assessed, it is currently impossible to discover and rectify these problems in most factories. The data becomes less and less dependable as the recording is done in larger and longer batches.
Manual Collection Slows Productivity
When someone has to write something down, their productivity suffers. Manually recording a task may only take 15 seconds, but if they do it every minute, they are wasting 25% of their time.
This could cost you 1.5 hours of productivity per day. Manual data-collecting makes it difficult for employees to concentrate and enter into a “productivity zone,” as it is known. Manual data collection can throw off the rhythm as it takes time, and workers must commit a significant portion of their shift to this inefficient task.
Staff Will Hate the Process
All Staff are initially interested in manual data gathering since it helps them better understand their process and may even lead to improvements to improve their performance.
Staff, on the other hand, may develop to dislike data collecting over time, leading to doubts about the effectiveness of these measures. In rare circumstances, dissatisfaction can lead to sabotage reports. Some people will alter the numbers and report inaccurate data in the hopes of halting data collecting.
Hence there are chances that data which you deem highly important for your firm and highly valuable for future decision-making processes may not be original which can further hinder the productivity of the business.
Tough to “Slice & Dice” (Analyze Parts of) The Data
Manual data collecting is also more difficult to comprehend because the information is not collected. It’s difficult to pinpoint trends or core reasons. Some issues, for example, are time-related.
They might only happen in the morning or on specific days. Mondays cause more jams in various digital presses and inserting equipment than other days. Without the data, you wouldn’t be able to observe the problem and determine the fundamental cause, which is usually temperature and humidity.
The point is that data should be obtained, compiled, and then sliced and diced for analysis in order to make it valuable for interpretation.
Manual Data Collection Methods Aren’t the Future
Many of the manufacturers are utilizing an out-of-date, difficult-to-use indigenous system. It’s simple and adaptable. Using a pencil and paper to record data does not require education.
This is common knowledge. But that doesn’t make it the best option. The majority of these existing systems or papers are either too expensive or too difficult to use to extract data from machines.
Temperature, humidity, vibration, and other points of process data that are crucial to quality, traceability, or the broader manufacturing processes are difficult to acquire.
This is no longer the case. Everything can be done automatically. Value-added operations may run uninterrupted with automatic data collecting and reporting, and people working to improve can become a reality.
Get rid of the old whiteboards and reporting system. Implement a real-time reporting system to show you the problems that develop as they happen, so you can practice learning manufacturing and one-piece flow.
Save time and effort for your operators, supervisors, and reporting employees. Clipboards and spreadsheets were once sufficient, but that is no longer the case, and manufacturers must adapt.
Using Data Intelligence to Reduce Unit Costs for Food Manufacturing Companies
Small and midsize processors have seen the benefits of automation firsthand, but they are missing out on one critical side benefit: data intelligence.
Data abounds in the modern food manufacturing industry: Production information, Data about utility usage, Data from the SCADA, the BMS, the PLCs, the sensors, and the equipment at each step of the supply chain and production process.
There’s data everywhere. This information is useful since it assists manufacturers in making better judgments. However, there is a significant difference between collecting data and being able to use it.
You’ll be overwhelmed if you don’t have a structure in place to grasp what the data is showing you. Continuous labor shortages and the never-ending demand for food and beverage items intensify these problems.
Large firms have the resources and expertise to gather, manage, and act on the information provided by data, while smaller and mid-size businesses sometimes struggle to do so due to cost or labor constraints.
That’s a problem because data isn’t simply useful for optimizing the manufacturing process. It assists manufacturers with anything from scheduling maintenance to dealing with workforce shortages.
Understanding the skills required to handle and efficiently use data opens up a world of possibilities for food and beverage companies.
Here are some areas where employment of data intelligence can help to minimize costs and maximize profit in the business:
Every manufacturer has a maintenance strategy, although it isn’t always optimized. If you’re working with equipment that can’t indicate its condition, you can end up in the dreaded “wait till something breaks, then fix it” position.
Because you can’t predict when a part will fail, if a machine breaks down in the middle of a production run, you’re looking at unplanned downtime, which may be costly. It also makes it difficult to ensure that you have adequate maintenance coverage.
If your maintenance department is short-staffed and a machine fails, your repair personnel may be dealing with another issue, slowing reaction time and extending downtime.
However, if your equipment can report its condition and send out warnings when something goes wrong, it may be detected early and dealt with as part of a preventative maintenance program. This not only allows for fixes outside of production runs but also helps with manpower shortages.
That isn’t to suggest it will completely solve them; rather, it will allow you to schedule maintenance to address issues when they are still minor and reasonably simple to resolve, rather than having to scurry to fix a machine that breaks down in the middle of a production run. This also makes it possible to perform proper maintenance.
Effective infrastructure is required to use data successfully, regardless of the application. Of course, this necessitates investment. That cost might be frightening for small and mid-sized businesses.
For a long time, automation systems were closed loops, which meant that when you bought an automation system, the equipment that would function with it was limited.
Yet, modern automation systems are interoperable, which provides more options; however, having equipment that can function with many control systems increases complexity and makes customization more difficult.
Equipment with a flexible technological stack built-in, which allows it to interface with multiple automation systems and be modified to the individual demands of a factory, is the key to combating this.
Rather than fitting a square peg into a round hole, the equipment is designed to collect and manage data throughout the manufacturing process.
The Use of Artificial Intelligence in Food Processing Activities
From robotics to machine learning, AI in the food industry encompasses a variety of technologies. Here are five ways artificial intelligence is improving the food and beverage industry:
Developing New Recipes Guided By Consumer Trends
To stay relevant and tap into new revenue streams, all food manufacturers recognize the importance of always looking for innovative methods to renew their product ranges. Traditionally, this has taken the form of surveys and responses to new trends, but AI now allows businesses to predict their customers’ desires.
Manufacturers may now forecast future trends and design new products to capitalize on them faster by evaluating massive volumes of data on sales patterns and flavor preferences for each demographic group. AI is also being utilized to provide customers with more personalization options when purchasing things.
This technology not only identifies the most popular taste combinations but also speeds up and lowers the cost of product development, allowing companies to bring new items to market faster and with less trial and error.
Better Supply Chain Management
One of the key priorities for food makers is the ability to properly manage supply networks. Companies on the cutting edge are now using artificial neural network-based algorithms to track shipments at every level of the supply chain, enhancing food safety standards and enabling complete transparency.
In the food industry, AI can also generate accurate projections for inventory and pricing management. Predictive analysis like this keeps food firms one step ahead of the competition, allowing them to prevent waste and wasteful costs. Modern food supply chains are more complicated and fragmented than ever before, yet AI allows businesses to obtain a better understanding of their operations, allowing them to generate income.
A More Efficient Cleaning Process
All food-processing equipment and machinery must be cleaned to the highest possible standards. This is not only to avoid pathogen contamination of food but also to avoid allergen cross-contamination.
Regrettably, this comes at a price—both in terms of time and money. This is starting to change thanks to cutting-edge AI technologies.
A Self-Optimizing-Clean-In-Place (SOCIP) system developed by the University of Nottingham employs optical fluorescence imaging and ultrasonic sensing to scan the food residue left in machinery after an operation.
Because the equipment does not need to be disassembled, the cleaning process may be streamlined, resulting in a 20-40% reduction in water usage and a 50 percent reduction in cleaning time.
More Hygienic Production Lines
Food safety violations can be extremely costly for food producers. Fines (in the worst-case scenarios, penalties can be in the millions of dollars) and reputational harm are both factors to consider.
In food processing, artificial intelligence is minimizing the danger of security breaches in a variety of ways. The more aspects of the food manufacturing process that can be handled by robots, the lower the risk of pathogen contamination. Robots can not only be faster and more efficient than humans but they can also be sterilized.
However, artificial intelligence is necessary for increasingly sophisticated jobs for these robots to physically carry out their duties and adapt to changes in the same way that a human would.
Artificial intelligence can also be utilized to improve the cleanliness of a company’s human staff. Facial and object recognition technology are being utilized to monitor compliance with cleanliness measures.
These AI-enabled devices will identify situations where proper production processes are being neglected or PPE is not being worn, helping organizations to maintain tighter control over on-site hygiene.
Food sorting is a time-consuming and labor-intensive procedure that slows the manufacturing line and requires a large number of employees.
This is especially true when it comes to sorting fresh produce products, with human sorters in charge of removing any units that do not meet the required standards for sale.
With the help of AI, the amount of time and people necessary to execute this critical task can be drastically decreased. Every object is assessed using cameras and lasers to determine its shape, color, and structural integrity, automatically flagging those that must be filtered out.
Furthermore, when machine learning technology is used, such systems will increase their accuracy over time, reducing the number of acceptable products wasted.
How Folio3 Can help Processing Plants in Their Digital Transformation?
Folio3 provides you a perfect as well as customized software solution based on the needs of the food manufacturing firm to digitize and automate their crucial data and hence make it traceable for their analysis on regular basis.
Not only Folio3 provides a digital solution for a food manufacturing firm but it also trains the staff about its usage and also help you modify it later in future based on the growing demands of the business so we strongly recommend the firms to consult Folio3 to get their digital software solutions.
How is AI used in food production?
Artificial Intelligence (AI) aids organizations in reducing time to market and dealing with uncertainty. Automated sorting will significantly cut labor expenses, speed up the process, and improve yield quality. The food sector will eventually improve in terms of safety standards as a result of AI.
What is manual data collection?
Manually obtained data is data that is gathered by hand, usually with a pen and paper. When collecting a novel measure, manual data collection is typically an acceptable standard operating procedure.
What are the challenges in manual data entry and processing?
Challenges of Manual Data Entry
- Error rates are high.
- Time to Turn Around
- Formatting and ambiguous fields:
- Checking for Quality
- Data overload
- Focus Deficit
With changing food trends, there are more ready-to-eat foods, beverages, processed frozen fruit and vegetable goods, marine and animal products, and so on – many of these things necessitate specific storage settings. The unavailability of these types of facilities is a significant barrier for the food processing industry.