Monday, August 10, 2009

Demand Planning: Essential to Your Success (Part 2)

If you had to pick a single process that has the largest impact on the company's plans and operations, what would it be? Better pick demand planning since it is the starting point for a lot of processes that collectively make retailers hum.image_thumb12

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In part 1 of this series, we presented various business processes that can benefit from a single source of projected demand. These processes included planning and execution functions in supply chain and merchandising functions spread over a long time horizon. The exhibit below provides a quick summary of the processes covered in the first part of this series.


In this part, we present how retailers can proceed to create a single source of forecasted demand to drive their processes and align their functional plans with their operations.

Creating a Single Version of Demand

So far, we have seen that projected demand drives a number of diverse processes in a company. Given this versatility of the demand planning process and potential use of demand forecasts driving many other processes, one would think that companies will have a single forecast to ensure alignment among all the downstream processes. This however, remains a myth. In reality, companies routinely use many different demand forecasts to drive their processes for planning and execution. It is not uncommon to have different historical data as well as techniques used to generate forecasts that drive different processes. For example, the long term demand projections generated for merchandise planning are routinely an affair of a budgetary projection that reflects more of the firm’s financial growth targets rather than any statistically indicated growth trends. Same is true for aggregate demand projections used for network planning.

This disconnect is only partly due to the lack of awareness. The other major reason is lack of proper tools for demand planning. Most corporations just do not have the right tools to maintain a single source of demand forecasts to address all the above processes. It is common to have a statistical demand forecasting application used for the execution processes, but then have a simpler, often subjective tool for addressing the needs of the mid and long-range planning processes that also need forecasted demand. This leads to inconsistent demand projections being used for different processes leading to plans that are misaligned with the operations. Lack of alignment between plans and operations causes misguided capital investments, infeasible plans, and unmet targets for revenue, profitability & budgets. They also lead to under or over capacity in the network, inventories, and the resources.

To avoid this misalignment, firms must get out of the siloed mentality and ensure that functional plans that share critical inputs like projected demand are based on using a single source of truth for such data. This is not very hard to achieve if corporations are aware of the different functional requirements and implement a single solution for creating demand forecasts driving all their planning and operational needs. This can be achieved by establishing clear process goals and having a tool capable of manipulating the demand forecasts in many different ways to address the unique but closely related requirements of these separate business functions.

Establish Series of Forecasts Required

Establish what types of demand forecasts are required. Not all retailers need to create plans covering all the business processes, nor do they need to create them with the same objectives. For example, if your logistic operations are largely 3PL based, the changes in the network flow capacities can be accommodated with relatively short lead-times and a large capital outlay planning may not be required. In another example, if your distribution is largely based on cross-docking operations, the future plans should largely plan for increased number of shipping and receiving operations to accommodate growing demand rather than conventional warehouse storage. Therefore, the first step towards creating and using a single demand projection is to establish what processes are critical to a company’s continued operations and depend on forecasted demand.

This means that well-defined requirements exist establishing the frequency of the forecast, level at which it will be generated, units for the forecast data, horizon definition, length of history to be consumed, data cleansing & enhancement pre-processes, and the consuming process for the forecast. This will help in evaluating the right solution and validating the feasibility of producing such forecasts from the single source of demand data.

Establish Functional Meta-data Standards

Next, understand how a single source of demand data will be modeled to cater to differing needs of individual functions. An important aspect for creating functional plans using the same demand data requires well thought out meta-data and master data models. Do the different business units and regions use common master data? Do they organize data using identical hierarchies and meta-data definitions? Make sure that all the functions use common definitions and understanding of the following meta-data structures and these structures fully address their needs.

· Item master data and attributes

· Item groups and hierarchies for aggregating and dis-aggregating demand data

· Hierarchies for locations and organizational units

· Time & horizon definitions

· Retail and cost for items, discounting structures, and consistent definition for units of measure like cases, boxes, and pallets

Identify a Single Source of Demand

Identify a source of demand history that can be used for projecting demand for all functional areas. There are various options to think of: firms can use the actual point-of-sale (POS) data from the sales at stores or individual customer order data from other channels, outbound shipments from the warehouses, or receipts at the store. While the latter two may provide easier to implement processes to obtain demand history, the former usually is the best source of collecting demand data. Select a source based on the granularity of demand required and establish technology solutions to support the data being generated. If POS data is selected, remember that it needs to be collected from physically distributed stores across time-zones in relatively near-time fashion to support good-quality demand management processes.

Finally, Get a Tool that Works

Finally, get a tool that provides the flexibility to use a single source of demand history and create many different forecast series as required by the consuming processes. Of course, the solution must have the basic functional capabilities required for demand forecasting such as the ability to consume history & create forecasts: such capabilities are not part of this discussion. Check for the following capabilities to address the requirements of various processes that need demand forecasting data. What makes these solutions versatile is their ability to manipulate data, slice and dice it, and roll it up and down to create different views of the same data. Specifically, the following features create such capabilities that enable or prevent the solution from catering to all the processes mentioned above.

Dimensions and Attributes:

A good demand planning solution must allow modeling and working with the basic dimensions of demand. This also provides a flexible framework to manipulate data along these dimensions & create views that are most relevant for the process under consideration. Ask if your solution can model the three basic dimensions of product, location, and time in order to qualify the demand data and model attributes for the members of these dimensions that can then be used for quick analysis of demand by slicing and dicing the data. For example, product attributes like their sales velocity, style, targeted customer segment, or season provide good criterion for grouping and reviewing the demand at aggregated levels relevant to different processes. Having the ability to model such attributes and manipulate data using these attributes is an integral part of the solutions that would cater to different functional processes.

Hierarchies & Roll-ups:

A good solution must be able to define hierarchies for each of the main dimensions of product, location, and time mentioned above. For example, the hierarchy along the product dimension allows the users to create product categories and to aggregate demand along the levels of this hierarchy to look at demand by category, product group, department, and so on. The solution must also allow multiple hierarchical representation of the same underlying entity: this means that products can have a merchandising hierarchy that groups them together for use in merchandising processes, but they can also have an inventory group hierarchy to quickly manage the inventory levels. These groups are generally created by using the attributes and their values for the entity. For example, the inventory groups may be created by using an attribute that models the sales velocity of the products, while the merchandising categories may be a result of attributes like style, season, and the target customer segment. Having multiple hierarchies allows the users to aggregate demand data along different paths and analyze it for specific process needs.

Horizon Modeling:

The solution must allow flexible horizon modeling. This helps the users to construct a funnel-shaped horizon with finely defined time buckets for early time-periods and coarse time-buckets for future time periods. This makes the solution more responsive, faster to run, and allows for modeling longer time horizons as would be typically required for long-range planning processes. It also reduces forecasting errors by aggregating demand for the farthest time periods. For example, the immediate time periods can be defined as days, followed by weeks, followed by months and quarters. A planning horizon of a year defined with weeks will create 52 time periods, while a funnel shaped time horizon modeling first three months as 16 weeks, followed by 9 monthly periods and 2 quarters would actually allow a meaningful forecasting horizon extending up to 18 months into the future, yet having only 27 time periods for the forecast. The latter approach to modeling will be much more efficient for computing while both models will provide equal functional utility as long as supply lead-times for the products of the firm are less than 16 weeks. The shape of the funnel depends on the lead-time characteristics of the firm's products and their procurement practices, but as long as the solution provides a flexible way of modeling, it can be implemented usefully.


UOM & Conversions:

Finally, the demand planning solution must be able to model demand time-series in any relevant unit of measure. As mentioned above, some processes like replenishment need the projected demand data in individual product units, while others like merchandise planning needs the same data in dollars. The solution must allow for modeling multiple units of measure and provide the ability to convert from one unit to another. It should also be able to represent multiple time-series for historical and projected data since not all units of measure can be converted from one to the other. For example, when products with different physical units (one measured in meters, other in lbs.) are grouped together under a common merchandising group, they must be converted to a common unit such as dollars to make any sense: this can be achieved either by having multiple time series in dollars & other measures or by modeling retail price per unit and converting the sales in units to sales in dollars. In another example, if products are rolled up using their handling characteristics (conveyable and non-conveyable), their demand may be presented in cartons & pallets, or by weight or volume. If the solution allows for such modeling flexibility and easy conversion from one unit to another, it allows itself to be leveraged in different functional contexts required by different processes.


Demand forecasting caters to many organizational processes that are spread across the time horizon and functional boundaries. To ensure that long-term organizational plans are aligned with the short-term operational objectives and the processes across functional boundaries support each other, it is imperative that companies implement demand planning solutions that will allow them to create a single demand forecast to drive these processes. Such a forecast must use a single source of historical demand and forecasting techniques that use similar assumptions. This cross-functional alignment in plans and operations will establish process synergy, reduce plan conflicts and volatility, and create operational stability that otherwise remains elusive.

In part 1 of this 2-part series, we presented the business processes that require forecasted demand to create plans that support everything from supply chain network capacity planning to every-day replenishment operations. These processes span across time and functional boundaries. We also presented how their requirements for projected demand differ by horizon, data granularity, and units.

In this part, we conclude this series by presenting how companies can break the functional silos to create a single source of demand forecasts to support their plans for different processes and ensure functional alignment as well as operational stability as a result. This requires careful planning and the right tools as discussed in this concluding part 2 of our series.

© 2009 Vivek Sehgal, All Rights Reserved

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