By Sam Lewis
Food manufacturers are constantly seeking to improve efficiencies and reduce costs. One of the best ways to do so is to utilize automatic identification and data capture (AIDC) technologies, such as RFID tags and bar codes — or a combination of both — to collect data about food products. That data can be helpful in a number of ways, including improving how products are tracked through the supply chain, validating handling and shipping procedures, and improving production and inventory controls.
But these systems can generate a copious amount of data coming from many sources. So much data, in fact, that food manufacturers may not know what else can be done with it to further improve operations. Many companies use the data for supply chain analytics to aide operations’ leaders in making better decisions regarding enhancing the AIDC technology itself and improving labor management.
Analyzing this data provides a clear picture about the cause of supply chain inefficiency, discovers areas that could be improved, and allows for predictive analysis. Reporting tools allow food manufacturers to see:
- how users are adopting and using the AIDC tools
- the battery life and functionality of the equipment in the field
- that the right data is being collected to optimize the ROI
Supply chain analytics also can help you assign the right laborers to the right processes and tools to complete tasks most efficiently. This is done by assessing and overseeing the workforce using the technology for benchmarking standards and reporting. Then, output data, labor costs, and time spent can be evaluated. With that collected data, many workforce issues can be addressed. Does the company have too big/small a workforce? Is the company working at appropriate times? Are the right laborers — full-time, part-time, seasonal, and temporary — working the right amount of time for the amount of output being produced?
Diving even deeper into the analytics of staffing, AIDC-related data can be used to find the impact that overtime/missing time has on labor costs. It can be used to discover if your company’s pay policies are effective or to understand how the workforce can be utilized better across the entire organization. All of this knowledge gained through supply chain analytics allows food makers to adjust operating costs as necessary, increase workforce productivity, and remove inefficient processes.
In the future, food manufacturers will be able to further their decision-making processes based on supply chain analytics and gain insight into the overall performance of all manufacturing plants companywide. As more companies invest in technologies, such as the Internet of Things (IoT), more data will become available. This will allow overall equipment effectiveness (OEE) to be measured and trouble spots to be pinpointed and fixed down to the part level of a specific machine in a specific plant.
Simply deploying AIDC technologies probably isn’t enough to get all the data you could be using to bolster your business; you must actively manage and utilize that data, too. Further, use that data to better manage your workforce. Only when the right data is generated, utilized, and managed in combination with workforce management processes can food manufacturers improve the supply to operate both more efficiently and effectively.