Demand Chain Optimization Whitepaper Pitfalls

Creating an efficient demand chain requires a combination of art and skill. Managing the demand chain—from manufacturers through wholesalers, distributors and retailers and onto consumers—is a daunting task, according to Atlanta-based supply chain based services provider Manhattan Associates. The numbers of SKUs, outlets, supply sources, seasons, and product characteristics are huge and because of the degree of complexity involved can be riddled with pitfalls. In its whitepaper, “Demand Chain Optimization – Pitfalls & Key Principles,” Manhattan Associates identifies several potential problems.

Here’s a look at three major pitfalls.

Demand forecasting relies on one approach.
This is very risky, because demand patterns vary significantly based on both the type of SKU and where the SKU is in its life cycle. Consider a product with a short life cycle, such as a computer video game or a DVD. The life cycle for this SKU typically lasts two to six weeks. The forecasting technique used for a regular-turn product in the middle of its life cycle is problematic for a short life cycle SKU. By the time a standard forecasting algorithm can catch up with the demand pattern of a short life cycle SKU, the SKU’s life may be over—and sales opportunities have evaporated. Fashion apparel itemscan be notoriously slow sellers; therefore, they require alternative forecasting techniques. For new prod¬ucts with no historical demand data, you cannot rely on the same forecasting algorithm you use for regular-turn SKUs. Different approaches are needed when forecasting demand for different product types, such as “functional” and “innovative.” New products fall into the innovative group and require very responsive forecasting and replenishment approaches.

The replenishment methodology is simplistic or is the same for all SKUs.
For replenishment of regular-turn SKUs, the traditional order-point, order-up-to-level approach has wide application. The order point is the inventory position that triggers a replenishment order.

The order-up-to level is the inventory position objective of your next order, if you do order. Often, these set points are determined subjectively. For example, it is not uncommon for a retailer to have infrequently updated values that depend primarily on merchandising factors such as presentation or model stock; factors like demand, lead time variability, and desired customer service are not part of the equation. The logic for determining when and how much to order should also depend on SKU characteristics such as demand velocity (slow vs. regular movers); stage in life cycle; and temporary impacts on demand, such as promotional events. These characteristics influence the amount of safety stock, the frequency of inventory status reviews, the forecast update cycle, and the order postponement decision. In fact, many companies use the so-called ABC method to manage inventory replenishment. They classify items into “A,” “B” and “C” classes and then apply one standard methodology for each class. For example: All “A” items get two weeks of safety stock, all “B” items get three weeks and all “C” items get four weeks.

This approach is flawed for several reasons. Specifically, the classification schemes typically are based on volumes, item values, suppliers, and other characteristics that may not have any direct link to safety stock needs. The schemes ignore true inventory drivers, such as lead times, demand uncertainties and supplier delivery reliabilities. Furthermore, it is not clear that three classes can capture the diversity and differences of the large number of SKUs many retailers and distributors carry.

Additional factors that replenishment strategies tend to ignore are the criticality of a product to the business and the availability of a recovery mechanism in emergency shortage situations. As an example, a product may be costly to stock but relatively inexpensive to ship from a different location within the demand network. To maintain high service levels for this item, the customer demand locations could keep lower inventory levels, while a central location keeps a pooled safety stock for emergency shipments. Another case where centralized inventory pooling is warranted is for products with low service criticality.

Time-phased forecasts for demands and lead times are inadequately calculated and utilized.
Extended demand forecasts are critical for supporting long-range production and sales planning efforts. They also are essential inputs for determining optimal investment buys. When supplier lead times are very long, accurate extended forecasts are required. A common approach is to provide such forecasts in aggregate form by using a simple rule based on the current period’s demand forecast. For example, a six-month forecast is obtained by taking the current month’s forecast and multiplying it by six, or the current week’s forecast and multiplying it by 26.

A more useful forecast would be time-phased—spread out by day or week or month.

Such a forecast could better support manufacturing and ware¬house capacity planning, work force assignments, sales forecast¬ing and budgeting. To obtain an accurate time-phased forecast requires careful modeling of day-of-week, trend and seasonal effects, and consideration of supplier constraints within the demand chain. The corresponding time-phased projections of inventory (which can be useful in their own right for cash flow analyses), vendor lead times and order receipts also are required. From a shorter time-frame perspective, accurate demand forecasts and expected receipts by day are key drivers to managing SKUs with short life cycles or short shelf lives. For these types of items, accurate day-of-week patterns must be modeled and used. One other area where time-phased forecasts are crucial is in managing special events that impact the demand chain. Examples would be promotions, special purchases, new item introductions and new customer loads. Forecasts for these events must incorporate external causal factors as well as historical performance data.

For more information about Manhattan Associates, visit

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