Demand-related risks in the supply chain: less visible, but no less dangerous

Demand-related risks in the supply chain: less visible, but no less dangerous

Discussions about risks in the supply chain usually focus on major supply disruptions such as environmental disasters and pandemics. However, there is another type of risk that can cause damage in the long run, but which receives less attention: demand-related risk. […]

Discussions about risks in the supply chain usually focus on major supply disruptions such as environmental disasters and pandemics. These dramatic events are attracting media attention because they are severely disrupting supply chains and limiting daily life– from consumer electronics shortages to vital medicines. However, there is another type of risk that can cause damage in the long run, but which receives less attention: demand-related risk. It is caused by unexpected, sudden or insidious changes in consumer behavior. Since supply chains are demand-oriented nowadays, it makes sense to look at the risks from this perspective as well.

Risk on the part of suppliers – a domino effect

A demand-related risk can be triggered by a risk on the part of the suppliers. For example, the pandemic has led to the launch of many new variants of consumer products, from designer face masks to HEPA air purifiers to organic, vegan hand sanitizers. According to various studies, the crisis also had an impact on other important areas of our lives, such as our nutritional behavior. Instead of going to the restaurant, more people are cooking at home or using delivery services at lunchtime. According to Statista, awareness of healthy, regional, seasonal and sustainable food has grown in Austria due to the corona crisis. All this has an impact on food supply chains.

Creeping developments

The link between the growing number of environmental disasters around the world and the sources of supply is clear. Pasta producers and consumers are concerned about the slump in durum wheat yields this year. What is less obvious is the fact that these disasters are causing many people to slowly opt for more sustainable products and services. Today’s consumers are asking themselves questions like: “Is there a more sustainable version of this product?”, “Do I really need this product tomorrow?”, or even “Do I need this product at all?“.

Short-lived trends

Another type of demand fluctuations occurs when global influencers such as Billie Eilish or Selena Gomez trigger fashion and cosmetic trends. The risk then is that companies will not be able to meet these short-term, strong demand peaks, and they will lose customers to competitors in the long term.

Five approaches to demand-driven risk management

According to Gartner, “the rise of digital business and the desire of brands to be closer to the customer require an agile supply chain in order to better predict short-term demand and increase the speed of response in replenishment planning.“ The key to this is the management and minimization of demand-related risks.

Fortunately, demand-dependent risk is easier to predict than supply-dependent risk, because there are so many different data sources today that help planners identify changes. There are also sophisticated systems and processes that can integrate and analyze the data.

Depending on how mature a supply chain is, changes may have to be made to the instruments, processes and employees in order to effectively manage the demand-oriented risk. The following five approaches provide a solid foundation for this process:

  • Capture new demand impulses to improve the short-term forecast – usually, forecasts are made for a period of one month to 90 days – too long a time window for planners to make improvements. By recording the short-term sales history and the associated causes of demand, companies can quickly and almost in real time gain insights into the month in order to update forecasts with a shorter time horizon. The key to improving short-term demand transparency is to increase the scope and variety of data sources collected and analyzed. The data that can be used includes social media, point-of-sales, inventory and shelf availability data.
  • Demand Modeling – a demand model helps predict future customer behavior based on past experience. The more external variables are incorporated into the model, the more accurate and predictive it becomes. These variables can include external sources such as social media feeds, competition information, weather forecasts and point-of-sales data, as well as internal data sources such as sales history and information about promotions and new product launches.
  • Probabilistic prediction – if several variables are included in the planning, a deterministic forecasting approach quickly reaches its limit. In contrast, a probabilistic (also stochastic) forecasting process takes uncertainty into account and thus helps with risk management. In probabilistic forecasting, advanced algorithms analyze several demand variables to determine the probabilities of a number of possible outcomes, one of which is the most probable. This is a much more reliable way to make predictions when demand patterns are variable, when there is only a limited order history (for example, when introducing new products), or when factors such as seasonality come into play.
  • Demand forecasting software Demand forecasting software, which uses a probabilistic approach, automatically models the bottom-up demand for individual items in detail. It analyzes order lines to model both historical demand volumes and demand frequency, thus obtaining an accurate estimate of volatility. Among other things, the software understands the difference between a bulk order of 20 units and the sale of individual units of the same product 20 times. Furthermore, the system can take into account the fluctuating “long tail” demand for slow-movers, which is difficult to predict. Market factors (trends, seasonality, causal factors and fluctuations) and organizational factors (demand-boosting actions, new products, forecast distortions and bullwhip effect) are also taken into account.
  • Centralized, integrated, cross-functional planning centralized, integrated, cross–functional planning – as soon as a solid basic forecast has been created, the employees in the company must refine it with their knowledge and experience. People play a crucial role in solving the many complex trade-offs that underlie forecasting and mitigating the risk of demand. Let’s take an example from the fashion industry: Generation Z buyers are very environmentally conscious and are keen on upcycling second-hand clothing. At the same time, however, they are also the population group that is most likely to resort to “fast fashion” in order to keep up with the changing trends. These contradictions mean that the fashion buyers who analyze the trends have to match their information with the fashion sellers who are closer to the actual sales figures. In every supply chain, the more people from finance, marketing, sales, operations and distribution partners can be involved in refining demand forecasts, the more reliable and accurate these forecasts will be over time.

As the supply chain guru Dr. Martin Christopher once said: “Today, successful companies no longer compete as independent units, but as supply chains.“ This sentence has never been as true as it is today. The companies whose supply chains are best able to detect and respond to fluctuations in demand will not only be the most resilient to risks, but will also be able to achieve the best overall results.

*Guest author Mauro Adorno is Managing Director Europe at ToolsGroup. The experienced supply chain expert helps his customers to overcome demand volatility and complexity in the supply chain and to achieve excellent service quality with reduced inventories.

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