Make to stock (MTS) is a traditional “build-ahead” production strategy in which manufacturing plans are based upon sales forecasts and/or historical demand. A company using this approach would estimate how many orders its products could generate, and then supply enough stock to meet those orders.
Make to order (MTO), on the other hand, is a production approach in which products are not made until a confirmed order is received. This typically allows consumers to purchase products customized to their specifications.
Building anything right now can be daunting and expensive, much less a large industrial facility. In the wake of the COVID-19 pandemic, the cost of construction materials has skyrocketed, labor is scarce and demand is surging. But that doesn’t mean the food supply chain can stop.
Food manufacturers and distributors still have customers to serve — and, for some, that still means investing in a new facility. At a time when construction costs are high, a company might make up for it in savings by reconsidering where the facility is built.
Facilities that support process operations produce some of the most expensive and complex buildings in the world. And they run the gamut: “Process operations” can range from baking desserts such as cakes to processing raw meat for grocery operations, to manufacturing parts and components for U.S. Navy submarines.
So what do facilities across such diverse markets have in common besides being founded on their process? For one, the costly and painful struggle of getting the project started. Many times, important early stages are executed out-of-order or even too late. Let’s look at four recommendations that may seem obvious, but if executed properly, will take some of the pain out of beginning your next process facility.
Today, the processing facility is a full-fledged operation supporting Sunsweet’s ongoing growth. Given its complexity and the company’s investment in cutting-edge features, the plant also serves as a “learning lab” where Sunsweet can test ideas and experiment with different processing efficiencies that will be applied to its future facilities.
When Sunsweet decided to expand their existing facility in Chile and needed design help, they turned to Stellar for a partner to help them not only design the facility but guide them through the entire process. From selecting the right site, to understanding sanitary design principles which ensure food safety, to vetting of local subcontractors, choosing the right firm to support your project is one of the most important decisions you face.
Big data comprises the large volume of data that businesses collect on a day-to-day basis. The question is: Are you taking advantage of it?
Data and analytics tools can be customized to meet your facility’s unique needs and goals — whether you simply want to gain insights to resolve certain pain points or install system-wide automation technology that takes your efficiency to the next level.
Let’s take a look at three applications for big data in a food processing facility:
Today’s big data tools and technology can create significant cost savings in modern food and beverage plants — and the return on investment (ROI) can come in the form of reducing losses or improving production.
Every company has a different outlook when it comes to capital spending, though, with some focused on short-term investing and others taking a long-term approach. Let’s look at some short-term and long-term options when it comes to big data and analytics tools.
Among all these moving parts, it can be easy for a plant owner’s original vision or goals to be lost or not fully realized. That’s why commissioning is becoming a critical part of the design-build process. A commissioning partner works with the owner throughout the design-build process to ensure their goals are achieved.
So you want to incorporate artificial intelligence and machine learning into your food processing facility — but where do you start? These tools have grown increasingly popular, and you’ve likely heard people discussing different platforms like Amazon Web Services (AWS), Google Cloud and Microsoft Azure. But how do you get access to these tools and what can they do?
Three layers of cloud computing
When it comes to introducing machine learning to your processing, think of it as a three-tiered ecosystem:
1. Service providers (AWS, Google Cloud, Microsoft Azure)
2. IoT solutions partners (system integrators, data experts, etc.)
3. End users (Facility operator, plant owner, etc.)
All data and information provided on this site is for informational purposes only and should not be construed as legal or business advice, or as providing consulting services or recommendations that you or your business should follow. The information and recommendations on this site do not apply to the needs of every reader or business, nor does the information or recommendations come with any warranties or confer any rights. Stellar is not liable for any information provided by guests, and any published information shouldn’t be construed be as an endorsement for a product or services. You should consider seeking professional advice to adequately assess your needs and to reach an effective solution. While every effort has been taken to provide the most accurate and up-to-date information and analysis on this site, the information is presented on an "AS IS" and "as available basis", is subject to change without prior notice, and is not guaranteed to be complete, correct, or up-to-date. Stellar is not liable for any losses, injuries, or damages arising from the display or use of information on this site.