Why Better Demand Planning Isn't the Answer

By Trevor Miles

At the heart of the supply chain are both the prediction of future customer demand and the satisfaction of actual customer demand. Reconciling the "prediction" to the "actual" is a growing problem. Increasingly fickle customer buying behavior, rising global competition, ever-shortening product lifecycles, product proliferation, world economic events -- the factors influencing demand volatility are far too numerous to list. Let's face it, it's an unpredictable world.

An Accurate Forecast Does Not Reduce Demand Volatility

Many companies that struggle with demand volatility try to address the issue by implementing a statistical forecasting tool. Here is the problem: implementing a forecasting tool isn't going to reduce the volatility. In fact, nothing will address demand volatility other than the root causes listed above, which are often outside of the control of a manufacturer.

Demand volatility is an expression of how much the demand changes over time. Forecast accuracy is an expression of how well one can predict the actual demand (whether it happens to be volatile or not). Why is it important to make this distinction?

Consider chemical engineering, which is a mathematically rigorous field in which complex equations are used to predict the behavior of complex systems. The equations are precise and outcomes can be predicted to the umpteenth decimal point. No one questions the validity or accuracy of the equations used, though there is continued research to "improve" the equations.

The equations used in chemical engineering are sufficiently accurate to design chemical plants. When it comes to actually running a chemical plant, all sorts of control systems are placed around the equipment to ensure the plant operates in a "stable" manner. It is recognized that all sorts of factors, outside the direct control of the plant operator — outside temperature, exact chemical composition of infeed materials, etc. — require that the plant be run outside of the exact assumptions made during the design of the plant. All sorts of readings are taken from the equipment, including the outflows, and continuously fed to the control systems to ensure a desired result.

In other words, there is an implicit understanding that the plant will not operate exactly as planned because of assumptions made during the design phase that are not realized in the production phase. This is analogous to demand planning. A statistical forecast can be calculated precisely but that won't mean the result will be any more accurate. Why?

Behavior Is Anything but Predictable

In most cases, we assume systems to be highly predictable and their behavior can be described very precisely by a set of equations. When last did you hear of a lead-time or production rate described as approximate? An inherent assumption is that these are so-called deterministic systems. According to Encarta, the definition of deterministic is

de•ter•min•is•tic [ di tùrmi nístik ]

1. relating to determinism: relating to the doctrine or belief that everything, including every human act, is caused by something and that there is no real free will

2. of knowable outcome: having an outcome that can be predicted because all of its causes are either known or the same as those of a previous event

Stop Building a "Better" Plan

Managing Demand — A Capabilities Assessment


  • Without a doubt, the greatest technical challenge is getting access to the right data on which decisions can be made. This wasn't as much of an issue in the days of integrated companies with a single enterprise resource planning (ERP) system, but in today's outsourced world, getting data from many different ERP systems, including those at contract manufacturers, suppliers and even customers, is a matter of necessity.
  • True and valuable visibility is only achieved when there is fully automated integration of demand and supply data from multiple sources — including customer relationship management (CRM), ERP, demand planning tools, supply chain management (SCM) tools, spreadsheets and point-of-sale data — to provide a single integrated view of forecast, sales orders, inventory and supply.


  • A must-have capability is accurate and timely demand sensing to quickly understand demand shifts.
  • Exception-based alerts to projected misalignments in demand and supply (e.g., forecast change beyond tolerance, late demand impacting quarter-end, supply disruptions, late customer orders, etc.) can enable demand managers and order fulfillment staff to quickly realign supply and/or engage manufacturing operations as needed.


  • Resolution of an issue requires multiple people to collaborate in order to reach a consensus decision.
  • Only human judgment applied through collaboration and supported by hard facts can allow teams to evaluate several innovative ways of resolving the issues at hand and reach a viable solution that best serves the interests of the company, customers and other stakeholders in the supply chain.

Evaluation and Resolution
  • While it is important that all stakeholders can collaborate and evaluate alternative ways of resolving a supply chain issue, equally important is that they do this is in a manner that is consistent with both financial and operational corporate objectives. This requires an ability to evaluate the impact of all decisions (prior to execution) against the achievement of defined performance targets.
  • Therefore, there is a need for decision support that drives profitable responses through shaping demand and allocating finished goods supply as appropriate.

Who Does This Apply To?

Think Broader than Demand Management

The Bottom Line

About the Author Trevor Miles Kinaxis www.21stcenturysupplychain.com