How to lower uncertainty in the supply chain

Supply chain is exposed to regulations and changes in rules, politics, weather, terrain, and infrastructures — such as roads and highways and to the risks, these represent that might cause delays or interruptions.

However, from a realistic point of view, when our goal is to offer clients a better and faster response, the risk is not the only factor to consider when deciding during uncertain times. Focusing on the characteristics of systems, such as the efficiency of processes, the capacity to respond, vulnerability, and resilience, has become a trend to help make better predictions. Considering these issues in our decision-making process helps us mitigate risk.

Risk joins Change

Besides the risks already mentioned, we must also account for changes in technology, in the way we conduct business and in society at large, such as how consumers demand more, a greater assortment of products, and longer and more complex supply chains, issues that have increased uncertainty in the markets.

To face these changes, companies have been forced to change too so they can deal with variations in supply and avoid interruptions in the supply chains.

How to lower uncertainty

In terms of supply chains, uncertainty means changes in fulfillment and profitability caused by unpredictable events and how difficult it is to decide when there is a lack of unambiguous in the supply chain, meaning we have no way of knowing the status and impact of the actions we may take.

There are also tools and data analysis–available for companies–that can help lower the internal uncertainty.

Inventory

We can use inventories as a cushion to deal with unforeseen events and abrupt changes to the offer and demand. To achieve a good optimization model, we must keep asking ourselves what the reason for the inventory to be out of sync with the supply chain is, dropping below or going above the optimum levels. By discovering the reasons, we also uncover the uncertainty our optimization model must deal with. The next step would be to identify the data that backs our model up. We may find this information in our ERP or MES systems; late deliveries and quality issues are good examples of this.

Maintenance

Companies should know Murphy’s Law because “Everything that may go wrong will go wrong at the worst possible time” is true. Companies must have preventive maintenance programs capable of foreseeing and preventing breakdowns.

This helps automate processes, alert operators about unexpected and notable events, and gather data to monitor and measure the performance of machines. If we want to go further, by surveying the sensors’ data in relation to the historical breakdown information we have, we can reveal more complex patterns; therefore, we may detect issues earlier by reacting to the readouts of several sensors instead of reacting to the readout of a single sensor.

Quality

It is essential that we find the fundamental reasons for low-quality manufacturing. Depending on data and the particular production line, this may require statistical analysis, predictive analysis, and even artificial intelligence. Otherwise, we may install additional sensors in the production line to collect the missing data.

To sum up, companies must use all the external data to better assess external uncertainty and analyze their internal data to lower the costs associated with internal uncertainty, making the best use of, and combining, the analytic competencies with the knowledge of the manufacturing process itself. The better we understand the present, the better we may foresee the future.