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Best Practices

Supply Sensing™ – A vital practice for agility in CPG supply chains

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Published: Reading time: 5 min
Zarrin Lilani Supply Chain Expert
Zarrin LilaniSupply Chain Expert
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Supply Sensing™

The need to deeply understand consumers has been the driving force behind recent digital transformation projects at Consumer Packaged Goods (CPG) companies. The CPG companies that set up demand sensing capabilities to understand critical drivers of demand including what external factors impacted consumer demand, were the companies that launched the right SKUs in the right markets and maintained the highest fill rates. Given the proliferation of external factors that affect the supply capabilities of CPG companies, if this increased understanding of how external factors will affect demand worked so well, can the same approach be used to predict impacts on supply? The answer is yes. This new approach to sensing external factors or events that could affect supply is known as “Supply Sensing.”

In the wake of an unprecedented number of disruptions, supply chain leaders have been asking themselves, “how can I better predict disruptions before they occur?” Some innovative companies have been adopting a novel approach called “Supply Sensing” to predict, assess and mitigate disruption.

Supply Sensing takes tried and true methods used to understand consumer or customer demand and applies this methodology to predicting supply disruptions. By understanding the factors that lead to supply volatility or disruptions, companies can better predict when disruptions will occur and act sooner to mitigate this risk. These can be game-changing for ensuring availability and avoiding sudden cost shocks.

Supply Sensing as a practice consists of 5 steps:

  • 1.

    Identifying the relevant events that could affect supply: Understand what key factors trigger supply reliability, not only for Tier 1 suppliers but for Tier 2 and 3 suppliers (and even further upstream as necessary).
  • 2.

    Monitoring drivers impacting events: Connect the correct data with reliable sources of insights to your planning system and create methodologies to process qualitative and/or unstructured insights, such as press articles.
  • 3.

    Estimating the evolving probabilities of specific events occurring: Develop the right algorithms that translate the relevant data points into a time-based projection of supply risks. 
  • 4.

    Predicting the potential outcomes of these events: Leverage the capabilities of your planning system to quantify the impact of predicted supply disruptions on your cost, service, and quality. 
  • 5.

    Leveraging scenario planning to respond to these events: Finally, translate these supply-sensing insights into actions to mitigate supply risks and cost shocks.

To further contextualize, an event could be a specific weather event affecting a crop that is a crucial input into a CPG’s raw materials, or a factory call-off due to Covid-19 for a particular supplier or set of suppliers in a particular region. There are three critical use cases where Supply Sensing capabilities can be effectively applied:

  • 1.

        Predicting the impact of key weather events on key commodities

CPG companies are amongst the world’s largest purchasers of key commodities such as corn, wheat, chemicals, coffee, and milk. Imagine if a large coffee manufacturer could look at a heat map of predicted agricultural yields based on weather patterns, and they could see an increased probability of reduced yield in a specific coffee-producing region. The manufacturer could act weeks (or even months) earlier than previously possible and set up agreements to buy more coffee from other regions. Additionally, the coffee manufacturer could feed this data into their purchasing models, assuring they buy coffee at the best possible price. This could be a game-changer for maintaining profits in the current inflationary environment.

  • 1.

        Utilizing leading indicators to predict transportation availability

Over the last few years, the leading indicators of transportation disruptions at a port or on-road transportation in a specific region have become more evident. Just as organizations switched to using leading indicators of demand to predict swings in demand, it’s now possible to use leading indicators to model transportation capacity and availability in the network. ML models can be built utilizing leading indicators to identify when a transportation disruption is about to occur in a specific region. Once set up, alerts can be sent to the relevant team members indicating the area, the probability of a transportation disruption, and a recommended action to take. Transportation teams can then move ahead with securing contracts ahead of disruption to keep service levels high.

  • 1.

        Calculating the probabilistic disruption of a specific supplier or facility

Many organizations have invested in Digital Twins that map supply chains from customers to Tier 1, 2, or even 3 suppliers. Predictive Machine Learning models can then be overlaid onto these Digital Twins so users can be alerted to the probabilistic disruption of a specific supplier. This changes the mindset from a one-time supplier health check when suppliers are onboarded to consistent health checks of the supplier network. This paradigm shift is key to ensuring that businesses lead by looking into the future instead of looking back at the past. Similar logic can predict the potential for call-off at production facilities. CPGs that honed this prediction capability and strong scenario planning capabilities to understand alternative sites that could be activated were amongst those with the highest on-shelf availability rates during the pandemic.

Moving out of firefighting mode…finally

The impact of climate change and the current political instability on the products we eat or use daily is already starting to set in. The July 2022 heatwave in Europe will undoubtedly affect crop yields across many sectors. It hit during a crucial pollination window for maize crops and caused overheating in livestock, which means that experts are forecasting a decline in milk production. Just as CPG companies moved to predict, shape, and influence demand instead of reacting to it, they need to do the same for supply. Only when organizations embrace this Supply Sensing mindset will they be able to move out of the reactionary firefighting mode that has characterized so much of the past two years. 

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About the author

Zarrin Lilani Supply Chain Expert

Zarrin Lilani

Supply Chain Expert

Zarrin is a Supply Chain Leader and technology strategist who has built out Supply Chains and their supporting tech systems at organizations both large and small. Zarrin is passionate about building resilient and carbon-neutral Supply chains with a particular focus on sustainable food systems.

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