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Identifying supply risks to successfully manage supply variability

Post By : Karvi Rana
Post Date : October 30, 2020
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Reading Time: 3 minutes

The most common way to predict the future is to use previous patterns that underline demand planning processes and activities. However, with the outbreak of the Coronavirus pandemic, it was clear as a day that demand planners must understand variability. One needs to be proficient in identifying factors that impact supply and then manage them to mitigate risk.

While risks and variability in the supply chain are viewed as a forecast problem, it is necessary to understand that the forecast isn’t the only part of the supply chain that features variability, and demand assumptions aren’t the only assumptions driving our supply chains and S&OP.

Importance of understanding Supply Chain Variables

Assumptions exist outside demand. They impact our ability to serve our customers and need to be identified and managed, just as we do with demand assumptions. Attribute data (data relating to those things that cause variability in supply) related to an item’s characteristics, distribution network, manufacturing process, purchasing terms, and even general planning settings- all contain variables that impact the supply chain.

The pandemic has given everyone an opportunity to introduce previously unrecognised uncertainty into supply process.

Typically, the unrecognised factors aren’t considered to impact supply. Often these attributes are poorly managed and invalidated unless persistent and serious issues make them impossible to ignore. When we input this data into the master data of planning systems, we have an opportunity to introduce previously unrecognised uncertainty into the supply process. When we do this, we get visibility into supply constraints and are in a position to manage them. However, a failure to understand these supply assumptions results in either a growth of excess and obsolete inventory or in a shortage of goods inventory.

This data (or attributes) should be regularly reviewed for accuracy, tracked for performance, and actively managed and maintained. For example, we must understand the impact on supply performance when lead times, run rates or process times change, or minimum order quantities and lot sizes are greater than our total annual forecast, or when any other supply constraint appears. Failure to understand these supply assumptions results in either a growth of excess and obsolete inventory or in a shortage of good inventory creating customer dissatisfaction and service failures.

Variability in Demand is Variability In Supply

When things don’t work out as planned from a supply chain perspective, we often assume it’s due to a random, non-repeating event that could not be prevented when in fact there were indicators in the data that could have alerted us to it ahead of time. Key attributes impacting our supply chains should be identified to reduce unplanned variability and poor performance. They should be measured for accuracy and adherence to bring about best supply results. Unfortunately, when this is not done it shows up as variability in demand! 

We know there are many circumstances that can change the results of attribute values both internally (labour availability, repairs, inaccurate inventory) and externally (weather, transportation limits, vendor capacity). Measuring supply performance and communicating this performance and its drivers back to demand can help demand planners better manage forecast variability. Loading supply settings without validating if they are correct restricts the value demand planners add and that of the demand plans they generate.

Managing Supply Assumptions for better Scenario Planning

Properly managed supply assumptions create better scenario planning. Scenario planning is a useful part of the S&OP process that aims to maximize margins and profitability. Many of us already create scenarios based upon various demand expectations, but we should also create scenarios based upon potential supply changes. Understanding how adding an extra shift impacts inventory is just as important in reaching our goals as understanding what happens if customer X sells 40% more than planned.

Taking the time to understand the assumptions outside of demand allows us to create scenarios and understand potential financial risk to the business. Understanding variability in supply and how we are performing against expectations will certainly improve the quality of the scenarios we run in S&OP.

To conclude

It is necessary for demand planners to teach Supply Planners how to recognise and manage assumptions. Also, both Demand Planners and Supply Planners should be held responsible for understanding and managing variability.

While the metrics may be different between demand and supply, they have the power to work together to reduce it.

One must study and understand the why there are differences between planned and actual results. One needs to accept that we might see changes in the future that don’t adhere to standards. And, the year 2020 has very well brought in the changes that are miles apart from the usual standards. Thus, this makes for the year to start redefining, reviewing and measuring variability and incorporating them into your process as core assumptions.

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