Demand Chain Optimization: Pitfalls and Key Principles Demand

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  • Evant White Paper Series

    Demand ChainOptimizat ion:Pit fa l l s and KeyPrinciples

    Demand ChainOptimizat ion:Pit fa l l s and KeyPrinciples

    By Calvin B. Lee, Ph.D.Vice President and Chief Scientist, Evant Inc.

  • 2003 EVANT All rights reserved. / 1 /

    E v a n t W h i t e P a p e r S e r i e s

    Demand Chain Optimization: Pitfalls and Key Principles

    By Calvin B. Lee, Ph.D.

    Vice President and Chief Scientist, Evant Inc.

    Foreword

    Managing the demand chain, from manufacturers through wholesalers, distributors and

    retailers, and onto consumers, is a daunting task. The numbers of Stock-Keeping Units,

    outlets, supply sources, seasons and product characteristics are huge, and the degree of

    complexity in the supply chain is challenging. Creating efficiency in the demand chain

    requires a combination of art and science.

    We need sound, scientifically based replenishment methodologies, which incorporate

    statistical and operations research techniques, to analyze the richness of contemporary

    databases and to deduce patterns, trends, variabilities and the dynamics of customer

    demands. Such scientific techniques enable us to balance the various costsinventory,

    transportation, handling, warehousing and other direct and indirect laborwhile simul-

    taneously providing optimal services for customers. This balancing act requires timely

    and accurate data coupled with the appropriate analytical techniques. At the same

    time, we must consider the delicate industrial practices associated with specific cul-

    tures and organizational behaviors, the constraints created by environmental factors,

    the impact of external factors and the influence of specific individuals in different

    industry sectors.

    This is precisely what Dr. Calvin Lee offers in the following white paper. His company,

    Evant Inc., has proven its ability to provide hyper-efficient replenishment services for

  • / 2 /

    D e m a n d C h a i n O p t i m i z a t i o n : P i t f a l l s a n d K e y P r i n c i p l e s

    2003 EVANT All rights reserved.

    an extensive customer base. Dr. Lee has had the experience of combining his and his

    teams rigorous scientific training and research, as well as their deep knowledge and

    experience of industrial practices, to advance innovative solutions for demand chain

    management.

    In this white paper, you will learn, based on Dr. Lees experience, how to avoid the

    common pitfalls and how to reap the benefits of demand chain management. The princi-

    ples described will profoundly impact your demand chain. It is gratifying to see that

    companies that have mastered demand chain excellence, avoiding the pitfalls and

    implementing the principles described in this paper, are widening their margin of market

    leadership and shareholder returns vis--vis their competitors. The principles in this

    paper form the foundation on which they have built their success.

    Hau Lee

    Director, Stanford Global Supply Chain Management Forum

    Professor, Graduate School of Business, Stanford University

  • / 3 /

    E v a n t W h i t e P a p e r S e r i e s

    2003 EVANT All rights reserved.

    Overview

    Recent estimates from the U.S. Commerce Department indicate that, in the United

    States, $1.1 trillion in inventory supports $3.2 trillion in annual retail sales. This

    inventory is spread out across the value chain, with $400 billion at retail locations,

    $290 billion at wholesalers or distributors and $450 billion with manufacturers. With

    this large stockpile of inventory, stock-outs at the retail level should be very lowone

    would think. But that is not the case. Studies have shown that 8.2% of shoppers, on

    average, will fail to find their product in stock. These stock-out events represent 6.5%

    of all retail sales. Even after recouping some of the loss with sales of alternative

    product, retailers will suffer net lost sales of 3.1%. This takes an enormous toll on

    retail margins, not to mention customer goodwill.

    So whats the problem? The unsatisfactory service is not for a dearth of inventory. The

    problem lies in lacking the right product, at the right place, at the right time to service

    customers. Before exploring remedies to the problem, let me first paint the picture of

    the demand chain.

    A demand chain is a network of trading partners that extends from manufacturers to

    end consumers. The partners exchange information, and finished goods flow through

    the networks physical infrastructure. The physical facilities include manufacturers

    warehouses, wholesalers distribution centers, retail chains warehouses and retail

    outlets. A demand chain can include multiple business enterprises. As product flows

    through the network, the partners incur costsbut they also enjoy revenue, as product

    changes ownership between business enterprises.

    The objective in Demand Chain Optimization (DCO) is to increase enterprise value for

    any part or all of the demand chain. We say part, because one can only optimize

    those components in the demand chain for which control can be exercised. For example,

    consider the demand chain that extends from manufacturer to wholesaler to retail

    chain warehouse to retail store. Its possible to optimize the demand chain for each

    trading partners portion of it, but total optimization requires a common objective

    that may or may not exist.

  • / 4 /

    D e m a n d C h a i n O p t i m i z a t i o n : P i t f a l l s a n d K e y P r i n c i p l e s

    2003 EVANT All rights reserved.

    DCO can impact enterprise value in many ways. It can produce:

    Higher customer service levels, which lead to greater revenue and net income.

    Higher inventory turnover, which frees up working capital.

    Higher worker productivity, which lowers operating expenses.

    Higher capacity utilization, which increases the return on assets.

    Lower logistics costs, which decreases operating expenses.

    Lower costs of goods sold, which increases net income.

    Each one of these will increase an enterprises return on assets. That, in turns, leads

    to increased return on equity and shareholder value.

    The effects of DCO are broad, influencing the overall financial health of the enterprise;

    however, the business decisions that drive DCO are ultimately made at the Stock-

    Keeping Unit (SKU) level. A SKU is a specific product at a specific location. SKU

    management requires many decisions, such as: When should you replenish the SKU?

    What quantity should you order? What customer service objective is appropriate for

    this SKU? Who do you order from? Could you better utilize the inventory for this SKU

    at another location? Should you even stock this SKU? What will happen to demand if

    you change the SKUs price? And there are many more questions.

    Finding the right answers is not trivial. This complexity is due to the many sources of

    uncertainty and the large number of decision alternatives. These many alternatives

    partly stem from the variety of approaches that you may want to consider. SKU demands

    are highly stochastic, and may vary because of seasonal, day-of-week and other

    special-event effects. Product availability and supplier lead times may also play a role.

    Making these decisions, and the many others required for effective DCO, requires an

    understanding of the key principles of DCO and knowledge of the pitfalls that trip up an

    enterprise as it strives to optimize its demand chain.

    Pitfalls of DCO

    DCO is rife with pitfalls. Many may seem obvious, yet they are typically the reason

    demand chain improvement efforts fail. Here are some common pitfalls:

  • / 5 /

    E v a n t W h i t e P a p e r S e r i e s

    2003 EVANT All rights reserved.

    1. 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 SKU with a short life cycle, such as a computer video game or

    a DVD. The life cycle for this kind of SKU typically lasts two to six weeks. The figure

    below shows examples of these types of SKUs, along with a typical fashion apparel

    SKU. 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 SKUs

    life may be overand sales opportunities have evaporated.

    Fashion apparel SKUs can be notoriously slow sellers; therefore, they require alterna-

    tive forecasting techniques. For new products with no historical demand data, you

    cannot rely on the same forecasting algorithm you use for regular-turn SKUs. Marshall

    (1997) distinguishes between functional and innovative products and points out

    the need to use different approaches when forecasting demand for each type. New

    products fall into the innovative group and require very responsive forecasting and

    replenishment approaches.

    Even in a business with some of the most endurable products, SKU types are highly

    diverse. The toy industry provides an example. Standard techniques will suffice for

    steady sellers like traditional board games, but as Johnson (2001) points out, new,

    fad-sensitive, short life cycle toys call for new forecasting approaches.

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