Before drawing conclusions about the resilience of manufacturing supply chains, we must first establish how they will bounce back from over a year of intense pressure and change. As countries navigate through various stages of the pandemic, and prepare for an unclear future, using the lessons learned from the past year will be crucial for the health of the economy in the long term.
The first challenge is uncertainty. There are many variables to consider. These include each country’s ability to contain the spread of COVID-19, the speed of vaccinations, and the ongoing concerns this situation generates. There are entirely new data points and assumptions for understanding when and to what extent consumer demand will rebound.
Moreover, last year, the IMF predicted that by the end of 2021, the pandemic will represent a net loss of $9 trillion to the global economy. Governments, businesses and consumers will absorb some of these losses. Inventories have become obsolete and measures to protect against the further spread will continue to weigh on costs, productivity and capacity. The supply chain will have to restart efficiently in order to avoid any further loss.
Using forward-looking demand scenarios to manage uncertainty
Planning processes can help meet these challenges. The best approach for organizations to manage uncertainty is to forecast multiple versions of the future. A single forecast will generate a single plan that may have little chance of success. Instead, multiple forecast demand scenarios offer a much better chance of modeling a version that is closer to reality.
How does this help? The process of building multiple visions generates consensus around the assumptions made and the expected results. This ensures the plan is understood by all functional teams and therefore has a greater chance of being executed successfully by operational teams. In addition, scenarios can sway between caution and optimism. Understanding the financial impact and likelihood of various scenarios will help focus supply chain planning to manage the trade-offs between risk and opportunity.
Demand scenarios are a key element of an agile and collaborative integrated planning cycle. In this process, distinct yet interdependent functional teams come together to align their perspectives and ability to execute an agreed plan. Multiple iterations can take place in a single cycle as different perspectives are recognized and alternative goals prioritized. The level of uncertainty and the pace of change during this crisis is likely to require a more sustained pace in the planning cycle.
Exploit technology and data to model the future
Once baseline demand scenarios are defined, you can begin to quantify expected demand. The limitation with past sales forecasts is their inability to spot trends and adapt to them. The pandemic is changing what consumers buy, how they buy and how much they buy. For instance, will consumers want to buy new clothes when lock downs are a thing of the past? Or will they avoid trying on clothes that have been tried by other customers? These demand levers are not available in a company’s internal sales history data. They are, however, accessible from external data sources.
Social media monitoring and search engine optimization (SEO) tools, for instance, provide massive samples of real-time data to identify trends. Artificial intelligence (AI) algorithms transform unstructured data, like pictures or comments, into information about consumers' state of mind and propensity to buy. Social media analysis also allows you to segment data attributes into specific segments of your customer data and sales history. The trend becomes a new “input” that forecasting algorithms can interpret along with sales history to generate forecasts in the new context. Retailers can then start making statistically significant forecasts for a variety of demand scenarios.
Next, you have to model demand scenarios according to the constraints of your supply network, as heightened by the pandemic. Delivery times are a critical component in determining reorder points and maintaining customer satisfaction. They are also based on averages from the past. As demand shifts from traditional sales channels to e-commerce, demand forecasting will help model at the level of granularity where the channel mix takes effect.
Collaborate to make the right decisions
Distribution centers use separate packaging flows for picking up packages for online orders and for pallets to restock stores. These are based on different infrastructures with different levels of productivity. Many retailers struggle to keep pace with skyrocketing e-commerce sales. Without visibility into the constraints that affect these flows, distribution centers may appear to be equipped to fully meet demand even though packaging management will prevent them from doing so. The result: a considerable contrast between what is planned and what is executed.
Collaboration is essential for operational teams to have visibility of expected changes in the product/channel mix to confirm delivery. Integrated planning in times of crisis ultimately looks like a command center where coordination between the company's internal departments and its external partners is essential to steer the decision-making process.
Expand from forecasting to achieving goals
Scenario planning isn't only about demand. On the contrary, it can also take into account a company’s sales objectives. This is especially relevant as you integrate your financial vision into the planning process.
Demand scenarios provide an overview of the balance necessary between opportunities and risks, while constraint scenarios do the same between service and cost. Ultimately, scenario planning moves organizations from a model that is only about forecasting and budgeting to one that is focused on achieving goals. In an agile supply chain planning process, modeling the impact of decisions on the bottom line will allow your organization to achieve goals instead of constantly trying to adapt to erroneous sales forecasts and budgets.
Explore new possibilities to create more value
With efficient processes in place, possibilities to create new revenue streams and provide added value can be explored. For instance, integrating data and automating processes, using digital twins to provide real-time stress modeling at granular levels, and leveraging big data and AI algorithms to integrate external data sources that impact your business.
In addition, control towers can facilitate the exchange of information in real time between partners. This helps improve the accuracy and speed of the scenario building process, allowing more time to be spent creating them and understanding the tradeoffs to be made.
In the long term, these simulations will provide a more holistic understanding of the supply chain and provide the ability to cope with constant changes in the business environment.
The ability to leverage the true potential of data offers immense possibilities for manufacturers to make insight-led decisions and quickly adapt to evolving business and market demands. Contact me to learn how we can help you seize the opportunities of data to drive the future of manufacturing.