Supply chain forecasting and planning have evolved over the years into an impressive discipline that creates efficiencies and helps companies deliver their product to the right customer at the right time at a reasonable cost.
When COVID-19 appeared on the scene, though, it shot holes in many traditional planning methods. Conventional tools built around historical demand and relatively stable sources of supply were suddenly no longer capable of making valid predictions that supply chain managers could rely on.
Suddenly, planning required a heavy dose of innovation and re-invention to account for many new factors that had never been part of the equation before. Those who had previously relied on off-the-shelf planning tools needed to build their own models from scratch. Others, having already built such models, needed to quickly re-work them with new input variables to account for dramatic shifts in supply and demand.
Over the past year, “agility” has become the watchword for businesses of all sizes. Agility requires the capacity to gather information fast, digest and analyze it, and draw actionable insights from it. In times of rapid change and disruption, that process needs to happen on a near-continuous basis. As a result, you hear a lot of talk these days about “continuous planning.”
A New Set of Decision Variables
Supply chain management can break down into a number of constituent parts: demand forecasting, inventory planning and management, sourcing, and production planning. These, in turn, interact with a number of other management disciplines throughout the organization including finance and cash flow management, human resources, and sales and marketing. In March of 2020, the pandemic impacted all of those individual disciplines in different ways.
Demand forecasting obviously drives much of the process. For most situations, past history serves as a starting point for predicting future demand. On top of that, demand models may account for the impact of marketing campaigns, seasonality, competitive market dynamics, sales trends in complementary products, and more. In a normal situation, most of those factors remain relatively stable, or at least they can be predicted with a reasonable degree of accuracy.
During periods of high demand volatility, however, those traditional models no longer work well. Who could have predicted, for example, that bicycle sales would spike in the spring and summer of 2020? With the onset of COVID-19, a renewed interest in outdoor recreation and fitness combined with the closure of gyms and a sharp drop-off in vacation travel, resulting in a spike in demand for bicycles. Manufacturers reported sales increases of 65-75 percent in the months following the initial lockdowns.
Demand models built upon past history were suddenly rendered semi-obsolete. A new set of questions began to emerge as part of the forecasting process:
- In which areas are COVID-related lockdowns most strongly impacting communities?
- To what extent are shutdowns of health and fitness facilities most likely to result in a surge in demand for alternatives such as bicycles or home fitness equipment?
- Who is buying? Is it fitness enthusiasts seeking new ways to get their workouts, or novice cyclists engaging in the activity for the first time? Or is it both? In what proportions?
- Which features and models are novice cyclists more likely to want? Will beginners prefer e-bikes over traditional models? If so, will that preference be more pronounced in areas with hilly terrain?
As events unfold, planners inevitably learn more about which variables greatly impact demand, and which may have a negligible effect. Off-the-shelf demand forecasting tools are simply not designed to answer these kinds of questions efficiently and accurately.
For those who have already built their own demand forecasting models, the post-COVID world calls for some significant changes. A key problem remains, though: Volatility not only requires changes to the input variables to those models, but also an increase in the frequency of forecasts. That, in turn, has led to a surge in “continuous planning” as a standard practice.
Managing the Workload
So far, this post has focused on demand forecasting as a primary example because it’s the foundational element that drives everything else. In reality, though, demand planning is simply one piece of the larger puzzle. The other components of supply chain management remain critically important as well, and they call for the same kind of review and update that the demand planning process requires.
After all, disruptions have come to suppliers’ processes too. Social distancing has affected production capacity and other safety measures. Workforce availability has been greatly impacted, as employees have needed to tend to family matters, including remote schooling.
Cash flow has been greatly affected as most companies are watching their accounts receivable with greater attention than usual. Even if business is booming, it can all come to a grinding halt if cash flow is not sufficient to pay suppliers.
Marketing campaigns must also be adjusted to accommodate demand. What’s the point in spending money to seek out new customers if you don’t have sufficient product to meet their needs?
The interplay of supply chain, cash, marketing, and workforce management add up to a lot of work. For many companies, the existing methods used for managing all that complexity are no longer serving them well.
In the past, it may have been the case that two or three skilled analysts would collaborate on a complex mega-model spreadsheet for supply chain planning. In a world that calls for continuous planning, though, that no longer works. Agility requires real-time access to information, with visibility and collaboration across the organization.
There are a number of enterprise-grade software tools that can provide robust planning capabilities. Unfortunately, most of those tools also require a significant upfront investment in time, money, and project management resources before the organization can achieve any value from them. They require a complete re-invention of processes, at a time when organizations can’t afford the bandwidth.
A more pragmatic approach is to improve on existing planning processes. There is a great deal of value in models that companies have built around demand forecasting, sourcing, capacity planning, and so on. The key is to industrialize those processes so that they reflect real-time information from the company’s various software systems, and to enable collaboration across the organization, such that forecasts can be shared, updated, and distributed efficiently, especially when much of the workforce is operating remotely.
To learn more, download this free guide to discover why extended planning and analysis is a necessity for every organization.
If your company is struggling to update existing planning models to meet the demand for continuous planning, insightsoftware offers purpose-built continuous planning solutions for organizations of all sizes. Our solutions are designed with the needs of frontline users (including supply chain managers, finance and accounting, and others throughout the organization) in mind. We’d love to talk with you. Contact us today to discuss your needs and request a free demo.