Our partner Celestica recently published the following article, ‘What If You Could Take The Guesswork Out Of Forecast Planning?’. The author, Osgood Vogler, Director, Analytics, Celestica Supply Chain Managed Services, describes an insight-based demand management process:
So, how do you take the guesswork out of forecast planning? Let’s find out.
Demand planning has a big impact on business performance. Planning error can put revenue at risk by driving component shortages. Persistent planning biases can tie up cash by driving excess inventory. Furthermore, the act of planning and dealing with planning error is time consuming and drives costly overhead. In fact, it is common for supply chain management executives to cite “planning errors” as the greatest obstacle they face to achieving their goals and objectives.
The factors which impact demand management and forecasting are nearly endless. Uncertainty in end markets, shifts in the competitive landscape, constant time-to-market pressure, economic volatility, geopolitical and environmental issues all play a role in component shortages, excess stock and lost revenue. Given this volatility, it is not surprising that organizations are struggling to make effective demand predictions.
To avoid the financial risks associated with planning errors, supply chain leaders and manufacturers should consider building an “insight-based” demand planning process, which brings together analytical tools and data with key human inputs across various functions. This “next generation” demand management approach will allow supply chain operations to evolve and scale with the ever growing volatility and uncertainty of today’s markets.
The insight-based demand management process contains several key principles.
One size does not fit all
One solution is never going to address every challenge an SCM executive will face, so it is important to determine the best approach for your supply chain through segmentation.
One planning approach may work well for one group of parts but not for another. Segmenting parts in a supply chain is incredibly useful to help guide the development of a cohesive demand management strategy. There are three questions that are central to the demand segmentation.
• Why is planning necessary?
• How important is the part to your business?
• How predictable is the demand?
Several considerations will likely go into answering each of these questions.
For example, to answer the first question about whether planning is necessary, SCM executives need to determine if supply is constrained and how quickly customers expect their order to be fulfilled.
If planning is absolutely necessary because supply of a particular part is constrained, an organization needs to determine how critical that part is to the supply chain, what profit margin is realized from the sale of the part and whether the demand is predictable across related parts and products.
This exercise is important because it will help supply chain leaders understand exactly where planning is necessary and how to drive exceptional performance in their supply chain operations.
Measure where it matters
Defining what actually needs to be predicted to effectively manage a supply chain is a requirement for accurate and efficient demand planning.
While prediction accuracy is often measured at the lowest level of granularity, such as by item, customer or region, these factors may not actually matter as much as prediction accuracy at a higher level. For example, the overall demand accuracy by part type at a regional distribution center may be more important to supply chain performance than item-customer-region level accuracy. To accurately judge one approach versus another, the primary metric for evaluation purposes may need to be established at a different level.
For example, if a planning process needs to determine “how many widgets do we need?,” the answer might be “we know we need 1,000 pieces.” However, if the demand planning process needs to determine “how many widgets of each color do we need?,” the answer might be “we are not really sure, say 600 black and 600 blue.” In this scenario, a forecast bias was created and it led to an order of 200 additional widgets.
To eliminate these low-confidence guesses and move toward a more informed demand forecasting process, the inputs used to generate a plan should be carefully selected.
Some common examples of guesswork in the demand forecasting process can include systems forcing planners to input forecasts at granularity that is lower than what can reasonably be estimated and sales teams tasked with translating customer intelligence directly into a demand plan.
Guesswork should never be hard-wired into the demand management process. The best results are most often achieved through human knowledge of the market and customers behavior coupled with analytics such as data on observed patterns, market trends and dynamics.
Find the right blend
Effective demand management requires a blend of two perspectives. The first is the customer’s perspective “looking outside in” at an organization’s products and the second is the supply chain’s perspective “looking inside out” to the supply base.
Understanding how the customer’s needs, wants and behaviors translate into demand is just as important as understanding what is known and/or knowable at different points in the marketing, sales and supply chain cycles.
Human wisdom combined with analytical insights need to be operationalized and integrated into a cohesive process. For example, what your sales and marketing teams really know about customers in end markets at various points during the sales cycles needs to be captured and leveraged effectively.
Furthermore, shared parts and bill of materials (BOM) commonality may present opportunities to generate more accurate and meaningful aggregate forecasts for the supply base. For instance, if two parts have BOMs that are 80 percent in common, it may be more effective to forecast the common parts separately from the unique parts.
Always keep a running score
Of course, implementing an insight-based demand management process structured around an understanding of key insights from human wisdom and analytical data is not a “set it and forget it” decision. Segmentation questions and criteria evolve with the business. Modeling and collaboration are ongoing activities. What is now a “guess” may become a “known” and what is currently “known” may become “unknown”. Scoreboards keep us honest and drive constant evolution. Insight-based demand management never stops.