Supply chains across many UK industries continue to be under strain. A recent survey of 1,000 British companies by fintech business lender MarketFinance showed that 79% of SMEs have seen their suppliers increase prices between May and October 2021. These issues are particularly concerning in relation to essential goods such as food and drinks.
Many of these problems are well reported, and relate to staff shortages particularly in haulage, caused by a combination of Brexit, the Covid pandemic and wider economic issues. The Road Haulage Association believes that the UK is 100,000 drivers short of the number needed to maintain functioning supply chains. The worldwide spread of coronavirus has also caused more volatility in global markets, with spikes in commodity prices and delays in shipping.
With Christmas just around the corner, leadership at the snack giant Mondelez have recently predicted that supply chain issues are likely to push prices up this winter. Current challenges show that it’s becoming increasingly important to embrace technology in the supply chain. This tech has the potential to create efficiencies, flexibility for suppliers, and better consumer targeting. Though what solutions can be adopted?
Where can software solutions be built into the supply chain for brands and logistics providers to manage supply chain issues?
There is a constant concern in the supply chain about freshness and getting products quickly to wholesalers and consumers. Much is involved in the process—careful planning, tracking purchase orders, and delivery times, for example.
There are various problems that arise in supply chain management in the F&B sector. Establishing measurements and controls is one big need that will help organizations monitor and manage inventory properly. It’s also necessary to implement systems that record these measurements in order to compare, report, and predict. Companies need access to data in order to trace products and stay compliant.
PLM technologies help with these issues by allowing producers and companies to identify the problems and adapt quickly. PLMs allow companies to connect all types of data from various sources (IOT), manage the products, and also implement traceability in order to stay compliant with local regulations. With this type of technology, organizations could use machine learning to help predict, becoming proactive versus reactive.
How does data play a key role in operations for brand and supply chain providers?
Data collection and data transparency are key to ensuring food quality and security. For example, the ability to track the ripeness of a product can help limit waste, maintain health and sanitation standards, and thus protect the customer and company margins. Or, knowing the activity in a dedicated lot or batch of products takes it one step further in full on quality control and security. IOT and data collection using location tracking data are also key to making this effective.
Supply chain management is challenging. The goals of those involved in supply chain management are to measure, control, and react. But only when proper measurements are in place in an IT system can one begin to tackle these issues. Moreover, without full visibility on the whole process, it is near impossible to increase margins. Technology can change the way those involved in the supply chain take charge and react to events and trends. Currently the innovation is limited by aging IT solutions and the future is new tech such as AI and machine learning.
What technology — including AI and machine learning — can be adopted to tackle supply chain issues?
AI and Machine Learning are new additions to the industry, and the data from these technologies can be used to start making predictions for F&B customers. There are some innovative companies out there who are starting to use machine learning, and it can help improve products in the future.
One example is the use of sensors in refrigeration that connect to voice data and to a PLM system. These sensors can alert workers when there is an abnormal change in temperature, allowing organizations to adapt before fresh products go bad. Additionally, this type of machine learning allows people to then anticipate potential issues and make predictions based on past trends. For instance, if temperature changes tend to lead to mechanical problems with refrigeration in the summer months, data from machine learning allows companies to prepare in advance.
By Christian Maurer, Industry Consultant of Food & Beverage at Centric Software