Using history to predict the future is the most basic assumption that underlines demand planning processes and activities. This assumption presupposes that there is inherent demand variability in sales and, by analyzing sales data, the variability can be identified and managed – that is a given. But it’s not only demand planners who must understand variability; Supply Planners also need to be proficient in identifying factors that impact supply and then manage them to mitigate risk.
Not All Risk Comes From Demand
Supply planners must look internally and understand that not all risks come from demand. Risks and variability in the supply chain are often viewed as a forecast problem but 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. 2020 has been a year that has really highlighted the need to manage assumptions across the entire supply chain and not just for demand.
Understanding Supply Chain Variables
Assumptions exist outside of 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.
We have an opportunity to introduce previously unrecognized uncertainty into the supply process.
Typically, these factors aren’t considered to impact supply. Often these attributes are poorly managed and unvalidated 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 unrecognized uncertainty into the supply process. When we do this, we get visibility into supply constraints and are in a position to manage them.
Failure to understand these supply assumptions results in either a growth of excess and obsolete inventory or in a shortage of good 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 Often In Fact 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 (labor 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.
Scenario Planning Requires Properly Managed Supply Assumptions
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.
I believe demand planners need to teach supply planners how to recognize and manage assumptions.
Bottom Line: Supply Planners Can Learn Much From Demand Planning
I believe demand planners need to teach Supply Planners how to recognize and manage assumptions. Not only that, like Demand Planners, Supply Planners should also be held responsible for understanding and managing variability.
Metrics may be different between demand and supply, but they have the power to work together to reduce it. Supply planners must understand why there are differences between planned and actual results, find the root causes of the differences and understand when we might see changes in the future that don’t adhere to standards. When we fail to manage supply variability, we introduce additional variability not just into the supply plans but also into the demand plan, resulting in inventory imbalances and poor service levels.
If you haven’t defined which supply attributes are key in your supply chain, 2020 is the year to take a leaf out of demand planning’s book and start defining, reviewing, and measuring them and incorporating them into your process as core assumptions.