Summary: Demand planning is getting more and more important in enterprises’ horizons, especially in times of increasing market demand volatility. As its variables grow complex, legacy system rollouts have taken place to deal with this development. Yet, they are unable to tame the rising tide of data processing and planning capability requirements. So what is the reason for this setback? Let this article help you find out the demand planning systems.
Growing data, growing complexity, but not growing efficiency.
Accurate demand planning indubitably forms the resilient supply chain that can shore up against today’s rapidly changing market needs. To improve demand forecasting, numerous market players have implemented first-generation Demand Planning systems from SAP, Oracle, JDA, etc. However, the problems that they looked to eradicate: the low forecasting accuracy rate and the interdepartmental chasm between sales, finance, and supply chain operations, persist.
So, now emerges a problematic question for companies’ executives: Why did legacy demand planning fail to deliver results?
The failure to formulate an appropriate adoption strategy.
Although demand planning systems certainly improved forecasting capabilities tremendously. Yet, people fail to capitalize on the benefits they offer due to multiple difficulties in updating and revising forecasts.
In fact, in most companies, even in global conglomerates, the majority of demand planning processes are still done in Excel spreadsheets, using laborious processes.
The legacy demand planning systems have gradually backpedaled to becoming record sheets. Their only use is to jot down the final forecasts that came up elsewhere as an input of waterfall planning processes. To name a few, there are supply chain planning, Material requirements planning (MRP), and Executive Sales and operations planning (S&OP).
Demand planning and S&OP systems have a reputation for being cumbersome, inflexible, and difficult to use. End-users remain unknowledgeable about changing the forecasting and planning models in the underlying system and integrate these changes into their workspaces. When it comes to model changes, they have no choice but to rely on IT vendors or consulting partners to change a model. As a result, they invariably move back to spreadsheets, which they can control and understand to maintain the workflow. This, unfortunately, relegated the system to the least common denominator use case—the answer box for the final forecast.
Misalignment in sales adoption of Demand Planning systems.
Most demand planning solutions employ weekly or monthly forecast entries as the sales organization’s primary use case. These forecasts, however, are a significant time commitment that often ensues further time-consuming management scrutinies. From the sales perspective, this only hinders the salesperson’s primary purpose: to sell their products.
Even if enhanced supply availability helps boost the sales performance, the salespeople tend to view this as a Supply Chain problem, not theirs. If the DP system doesn’t help them add more value to the sales pipeline, Sales are highly unlikely to adopt it, regardless of the tangible benefits in improving supply availability. And the result is the rift breaks open in the Demand Planning process.
Management challenge: why is sales adoption the key to successful demand planning?
In response to the sales department’s inhospitable stance towards the demand planning process, several manufacturing organizations have shifted the responsibility to generate the demand forecasts to the supply chain departments.
But this has inadvertently raised another challenge: low accountability in the sales department to the demand forecast. Demand planners are hitting blockages when collecting the essential inputs on sales activity (pipeline, pricing, promotion) for better predictions.
However, it is the sales department that sits on the front line, and therefore needs to adopt the systems and the ownership of forecasts. Sales sense risks and opportunities emerging in the marketplace earlier than the rest of the organization. Consequently, it is critical for an agile supply chain and S&OP process to get these inputs to respond rapidly.
For example, suppose an account manager for a large retail account finds out that a retailer plans to reduce shelf support and store coverage for their brand. The reason is that a competitor is coming out with a superior product with better pricing. In that case, we can call this a “Risk event” that needs to be shared with all employees across the organization departments:
- Product R&D: They need to know what the competitor’s product is.
- Product Marketing: Know about the competitor’s pricing strategies.
- Sales Management: Know if they can create incentive programs to counter the risk and if this is likely to repeat in other accounts.
- Supply chain department: Know how this affects demand forecasts
Unfortunately, all relevant information trickles very slowly and sequentially across the organization. And we are talking about if there is any flow at all. The info usually reposes in the salesperson’s head, or an email, or as an update in a CRM system. What is alarming is that they are only brought to the weekly sales meeting table with sales management executives. Planners may or may not update sales forecasts in accordance with this “Risk event.” Even if they are, the context of the update is not shared across the organization.
When supply chain coordinators see the change in forecast without being aware of the “Risk event” that backs this change, they often second guess and alter that already-updated forecast update. This further digs into the interdepartmental misalignment within the organization, resulting in unnecessary double-works and even conflicts.
The future now lies in the hand of next-gen demand planning systems.
To truly drive sales adoption and sales accountability to the demand plan, the solution needs to help salespeople achieve their primary goal—Selling.
Legacy systems have failed to prove their worth. However, this is not putting an end to the hope for a better, comprehensive demand planning system that can solve all these problems, besides the pristine issue of forecasting accuracy.
And this is what next-gen demand planning software can bring to the table: a solution to all obstinate problems that legacy systems could not address during their tenures.
What differentiates next-gen demand planning systems from their predecessor is not only the ability to improve the accuracy of forecasts, using the newest development in AI (artificial intelligence) and ML (machine learning). Furthermore, their new systemic structure allows a more collaborative planning horizon for multiple departments, why staying simple for adoption. By now, the obstacles that are blockading the much-desired interdepartmental collaboration are nowhere to be found in the domain of negative impacts.