2. Consider new distribution channels
In some cases, the demand for a class of products has dropped so much that manufacturing operations must consider shutting or slowing operations or redirecting raw materials to new products. For example, with people honoring stay-at-home orders around the world, consumer demand for makeup has tumbled, and with bars slowly opening and sporting events and concerts cancelled, keg beer usage has decreased.
Processing plants need to consider the raw material and packaging solutions available to them and anticipate what else they can produce that consumers want right now. For example, many wineries and distilleries have produced hand sanitizer for the first time. And the raw materials and production processes that produce makeup can instead be used to make skin care lines that might be in more stable demand.
Shifting products isn’t the only option. In some cases, demand remains but has shifted to new distribution channels. For example, a large processor of cheese that served restaurants with bulk product, is transitioning to serving supermarkets. Under normal circumstances consumers might shy away from bulk cheese purchases, but nothing about shopping behavior is normal right now.
3. Diversify raw material supply chain
Restrictions on transportation of raw materials across borders to protect against the spread of COVID-19, staff shortages due to illness, and safety measures that require physical distancing have all placed pressure on the supply chain of raw materials.
Yet, access to raw materials is foundational for production agility. Equipment can run harder and longer, but if the raw materials needed aren’t available, they are stalled.
One way to combat the risk of supply chain disruptions is to diversify suppliers, increasing the chances of maintaining production agility without having to store excess inventory that under normal circumstances wouldn’t be used.
4. Leverage machine learning and data analytics
It’s tempting to focus on the immediate action in front of us – meeting the growing or shrinking demand for products. Yet we know that the decisions made today will have a long-term impact on your brand. It’s critical to make those decisions with as much insight as possible.
Predictive analytics and machine learning play a critical role in modeling the actions and consequences of actions in a way that is always valuable but are absolutely critical when circumstances are volatile. It’s important not only to understand an operation’s OEE but to be able to break it down in a granular way to take action.
We have had our Proficy CSense customers use its AI and machine learning analytics in a variety of ways. One international food manufacturer decreased customer complaints related to product weight by 33%, improved product quality, and decreased waste. Additionally, a pulp and paper manufacturer predicted Critical to Quality (CTQ) KPIs to improve productivity and eliminate wastewater regulatory issues. As a final example, a third company implemented an advanced process control solution to increase throughput by 10% using optimization technology.