DEMAND PLANNING

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. Demand planning consists of processes that allow a retailer to forecast demand into the future and manage it. It has the key dimensions of product, location, time – to clearly identify the projected demand.

Once the projected demand is available, it is used to drive all types of functions, which may be expressed in different units, such as dollars or boxes or kgs, different levels such as product categories or individual SKUs, or different organizational units such as merchandising departments or regional markets.

Owing to these many uses of projected demand, the process is sometimes segregated into departmental boundaries that create their own demand projections. This practice leads to poor coordination among the organizational units, produces inconsistent results, and leads to wasted opportunities in optimizing operational costs and efficiencies.

IMPACTED PROCESSES

There are many processes that use projected demand forecasts. They span from long-range planning processes like network design that have a horizon of a few years, to execution-level processes, like replenishment, with immediate impact on operations. Using the time horizon as the basic context, these processes can be divided as detailed below.

Long range planning These processes use demand projections to plan for the supply chain flows within the network. They need aggregated demand forecasts for a longer horizon and may not have any immediate impact on the firm’s operations. Examples of such processes are supply chain network design and network capacity planning.

These processes help answer questions like: How much volume of product will be flowing through the supply chain network in the projected years? Where does this flow occur along the existing network routes? Are new routes required? How is the current network poised to handle this projected flow of product volumes? Do the warehouses have enough storage capability and operational resources to support the projected volume of flow of merchandise? Is there adequate transportation capacity available along all main network arteries?

Flow capacities that will be required to support the firm’s projected demand volumes. Any changes in the network have a long lead-time for implementation and require substantial capital investments, whether it consists of increasing the capacity by opening new facilities or through automation; or reducing the capacity by closing existing facilities or changing the facility locations for more optimal flows. The long lead-times and large capital layouts mean that these evaluations are generally done years in advance.

However, they only require aggregated projected demand in terms of number of number of cases, pallets, volume or weight of the flows expected for the projected demand. Demand projections for these processes can be directly created at these aggregated levels since they tend to be relatively more accurate over the longer horizon desired for these processes.

Mid-range planning

Medium term processes use the demand projections for revenue and budget planning purposes. Examples of such processes are merchandise financial planning and product portfolio planning. They need projected demand at a more granular level than long-range processes.

These processes generally work with projected demand for product categories in dollar value of the merchandise at monthly and sometimes weekly levels. The objective of the merchandising processes is to develop the merchandise plans and create targets for revenues and profitability, and planned budgets for promotions, clearance, marketing, and procurement of the merchandise. The portfolio planning processes generally evaluate the projected profitability using the projected demand and arrive at optimal assortments (product mix) for the projected plan horizon. Demand projections for these processes are generally created at lower levels and rolled up for use in these processes.

Short-term execution

These processes use the demand projections for supporting immediate operations of the firm so that customer orders can be fulfilled and stores are adequately stocked with the right merchandise at the right time.

There are many processes that take advantage of the short term demand projections for this purpose, such as inventory planning, purchasing, receiving, storage, store fulfi llment, and inbound & outbound shipping for the warehouse. Most of these operations need very granular demand forecasts at item and facility level, often in daily or weekly buckets.

Other processes that also benefit from this level of projected demand are price optimization, promotions, clearance, and seasonal product life-cycle events. Demand projections for these processes are created at lowest grain of product and location often in expanding time buckets along the horizon, for example, the forecasts may be produced on a daily basis for next two-three weeks, weekly basis for the next two-three months, and monthly basis thereafter. Supply lead-times generally affect the length and size of the time horizon of demand forecasts created for these processes.

Long & Medium Term Processes Driven by Demand Planning

SINGLE VERSION OF DEMAND?

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 diff erent 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 refl ects 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, and so 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, profi tability and 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 REQUIRED FORECASTS

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 logistics 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.

Short Term Processes Driven by Demand Planning

ESTABLISH 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 metadata 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 SINGLE DEMAND SOURCE

To identify a source of demand history that can be used for projecting demand for all functional areas, there are various options: actual point-of-sale (POS) data from sales at stores and individual customer order data from other channels; outbound shipments from the warehouses; or incoming goods 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.

GET A GOOD TOOL

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 software solution must have the basic functional capabilities required for demand forecasting such as the ability to consume history and 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 & 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 defi ne hierarchies for each of the main dimensions of product, location, and time. 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 for specific processneeds.

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 timeperiods 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 longrange 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 defi ned with weeks will create 52 time periods, while a funnel-shaped time horizon modeling the first three months as 16 weeks, followed by nine monthly periods and two 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.

Units & conversions

Finally, the demand planning solution must be able to model demand timeseries 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 kg) 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.

Flexible horizon modeling

TOWARDS STABILITY

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.