Information Sharing Within Supply Chain Business Essay


In order to showcase more holistically how demand smoothing/management is discussed by many authors, key elements of demand management and related concepts from several selected journals, books and from thesis have been summarized below. It shows how different logisticians, economists and theorists have defined major theoretical concepts relating to the research topic of the project.

3.1 Supply Chain Management

The term Supply Chain Management (SCM) was initially coined in the early 1980s by consultants (Oliver andWebber, 1992). There is nevertheless no consensus as to the exact meaning of SCM, as there are many different definitions of supply chain and supply chain management. For the purpose of this study, the definition of Mentzer et al. (2001) has been chosen, who define SCM as "the systemic, strategic coordination of the traditional business functions and the tactics across these business functions within a particular company and across businesses within the supply chain, for the purposes of improving the long-term performance of the individual companies and the supply chain as a whole". Discussions on defining SCM are for instance provided by Croom et al. (2000); Cooper et al. (1997) and Gibson et al. (2005).

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Outsourcing and advances in information technology have been among the reasons for the emergence and development of SCM (Kokand Graves, 2003). The focus on core competencies and the outsourcing of non-core activities (Prahalad and Hamel, 1990) has led to reduced vertical integration, meaning that more companies are involved in producing and delivering a product. Advances in information technology (e.g., Enterprise Resource Planning (ERP) systems, Advanced Planning Systems (APS), etc.) and the internet are also important as necessary data can be better provided and the exchange of information between companies is facilitated (Kok and Graves, 2003).

The bullwhip effect is the main motivation for collaborating within a supply chain. Many of the collaboration practices in SCM have been developed to reduce the bull-whip effect. Popular examples are Vendor-managed inventory (VMI) (Disney and Towill,2003a; Waller et al., 1999; Aviv and Federgruen, 1998) or Collaborative Planning, Forecastingand Replenishment (CPFR) (Chopra and Meindl, 2007; Holmstrom et al., 2002;VICS, 2004). Most of these concepts heavily rely on improved exchange (flow) of information,compared to the traditional buyer-supplier interface, where only orders are sentby the buyer and the supplier "simply" has to deliver without any further information.

Types of supply chain models implemented within an organization depend on the nature of the company. There are four types of supply chain models and they are,

Integrated Make to Stock Model - This model focuses on tracking customer demand in real time, so that the production process can restock the finished goods inventory efficiently. This integration is often achieved through use of an information system that is fully integrated within an organization

Build to Order Model - In this model a company begins assembly of the customer's orders almost immediately upon receipt of the order. This requires careful management of the component (input) inventories and delivery of needed supplies along the supply chain. This type of supply chain model supports the concept of mass customization.

Continuous Replenishment Model - This model tries to constantly replenish the inventory by working closely with suppliers and/or intermediaries. Very tight integration is needed between the order fulfillment process and the production process. This model is most applicable to environments with stable demand patterns.

Channel Assembly Model - This model is a slight modification of the build to order supply chain model. In this model, the parts of the products are gathered and assembled as the product moves through the distribution channel. This is accomplished through strategic alliances with third party logistics (3PL) firms.

The primary objective of supply chain management is to fulfill customer demands through the most efficient use of resources of an organization. In theory, a supply chain is looking to match demand with supply and do so with the minimal inventory. Various phases of optimizing the supply chain include liaising with suppliers to eliminate bottlenecks; implementing JIT (Just In Time) techniques to optimize manufacturing flow, procuring strategically to strike a balance between lowest material cost and transportation, retaining the right mix and location of factories and warehouses to serve customer markets, and using location/allocation, vehicle routing analysis, dynamic programming and traditional logistics optimization to maximize the efficiency of the distribution side.

3.2 Information Sharing within Supply Chain

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Usually, SCM deals with three types of flows, moving upstream and downstream the supply chain: products, funds and information (Chopra and Meindl, 2007). For many concepts and best practices in SCM, the flow (exchange) of information is crucial (e.g., for VMI). In a very general statement Simchi-Levi et al. (2008) notes that abundant information reduces variability, facilitates forecasting, enables coordination, enables retailers to react and adapt to supply problems more rapidly, and enables lead time reduction. As information plays such an important role within supply chain management, information flows and the concept of information sharing are investigated in plenty of studies. Useful frameworks to organize the knowledge of that manner are provided by Huang et al. (2003) and Sahin and Robinson (2002).

For instance, companies at a stage far downstream of the supply chain have better understanding of end consumer demand and should share this knowledge (forecasts, POS-data) with upstream partners. On the other hand, upstream partners can inform downstream partners about order status, capacity utilization, production schedules or inventory levels. The downstream partner can use this information to quote better due dates and to better organize inventory replenishment. In addition to the more common vertical information sharing (upstream and downstream), horizontal information sharing is also possible between companies at the same stage in the supply chain.

3.3 Supply Chain Uncertainty

First, uncertainty must be clarified. One possibility of defining uncertainty comes from contingency theory, an important stream of organization theory, where uncertainty plays a crucial role. According to Downey and Slocum (1975), uncertainty is "a state that exists when an individual defines himself as engaging in directed behavior based upon less than complete knowledge . . ." In their paper, they further underline the psychological dimension of uncertainty, as they investigate differences (variance) in perceived uncertainty. A recent discussion of different definitions of uncertainty is presented by Yang et al. (2004b).

Davis (1993) distinguishes between three main sources of uncertainties within supplychains: suppliers, manufacturing and customers. Geary et al. (2002) added control systemas the fourth main source of uncertainty, which transforms customer demand into productionplans and supplier orders. A more sophisticated view on sources of uncertainty isprovided by van der Vorst and Beulens (2002), differentiating the following three maintypes of uncertainty:

Inherent characteristics that cause more or less predictable fluctuations (whichhave stochastic occurrence patterns). Uncertainty may take the form of high variabilityin demand, process or supply, which in turn creates problems in planning,scheduling and control that jeopardize delivery performance (Fisher et al., 1997).For instance, food supply chains are especially vulnerable to this type of uncertainty,because of the specific product and process characteristics, such as perish-abilityof end products, variable harvest and production yields and the huge impactof weather conditions on consumer demand.

Characteristic features of the chain that result in potential disturbances of systemperformance (non-optimality):

Chain configuration (e.g., inflexible capacities);

Chain control structure (e.g., wrong decision rules applied);

Chain information system (e.g., information delays); and/or

Chain organization and governance structure (e.g., misjudgment by a decision maker).

Exogenous phenomena that disturb the system, such as changes in markets, products, technology, competitors and governmental regulations.

3.4 Demand Variability

Demand variability is probably the most important and most obvious source of variability.Customer order behavior is mostly very uncertain, difficult to predict and of course muchharder to influence than production variability. Nevertheless, there are strategies and possibilitiesfor addressing the different types of demand variability. The most famous modelwith respect to "tailoring" a supply chain according to demand characteristics was providedby Fisher (1997). He suggested two main types of supply chains (efficient and responsive), mainly based on the differentiation between functional andinnovative products, which show different demand characteristics.

Compared with innovative products, functional products usually have a more stable,more predictable demand, longer product life cycles, lower product variety, lower contributionmargins, less stock-outs, low stock-out costs, higher volume per SKU (stock keepingunit), no markdowns (discount at the end of the selling season) and low obsolescence.In contrast, innovative products usually have unstable, difficult to predict demand, shortproduct life cycles, greater product variety, higher contribution margins, more stock-outs,higher stock-out costs, lower volume per SKU, more markdowns and higher obsolescence(Fisher, 1997; Lee, 2002).

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This differentiation gives a good overview of aspects concerning demand. In the following these aspects are discussed with respect to the suggested framework of this study, classifying demand variability due to randomness as well as variability due to management decisions.

3.4.1 Demand variability, due to randomness

Within classical B2B relationships the manufacturer gets orders from his direct customer and delivers the products, without any further information shared. Consequently, the manufacturer has to be prepared to fulfill uncertain customer demand. In such a situation planning activities are based on forecasting. This is true for Make to Stock (MTS) manufacturers as well as for Make to Order (MTO) manufacturers. The difference between these two types is just the buffer, used to hedge against demand uncertainty, as the former has mainly to determine the dimension of safety inventory of finished goods and the latter mainly of safety capacity (machinery, staff). Clearly, both have to hold raw material inventory (RMI).

Therefore, it is essential for any manufacturer to use forecasting methods which predict future demand as well as possible. Unless there is no further information or cooperation possible, forecasting tries to understand past demand by identifying the influencing factors and quantifying their impact. If past demand could have been explained by a set of influencing variables, the probability is high, that future demand can be predicted with reference to these influencing factors.

Forecast methods can be classified in (1) qualitative forecasting and (2) quantitative forecasting. Approaches belonging to the former group are used if historical data is not available (e.g., new products) and attempt to predict future demand by using the knowledge of experts. Examples for this group are the Delphi method (opinion consensus of a group of experts, see Dalkey and Helmer (1963)) or judgmental forecasting (Gaur et al., 2007).

In a B2B environment, more frequently dealing with functional goods, mathematical(quantitative) models are used, as historical data is available. There are two groups ofmodels: (1) causal models (regression) and (2) time series models. Causal models try to predict demand for a particular product as a function of other parameters (e.g., average temperature, growth of GDP). Time series models try to predict future demand as a function of past demand (past values of the same parameter). In practice time series forecasting is probably the most relevant.

According to Silver et al. (1998) any time series is composed of five components: (1) level, (2) trend, (3) seasonal variation, (4) cyclical movements and (5) irregular random fluctuations. These components can be used to describe a demand time series. Level captures the amount of demand. If there is no other pattern than the level, demand is constant over time. Trend denotes the growth or decline of a series over time. Seasonal variation refers to a periodic variation of a fairly constant shape (e.g., increase of demand for mineral water during summer). The period explaining this repetitive behavior can be, for instance, a year, a month, or a week. Cyclical variations capture increases or decreases due to business cycles. Business cycles usually last several years, whereby the relevance for short and medium term operations and production planning is limited. Irregular fluctuations represent the remaining fluctuation after identifying the effects of the other four components.

The orders received by the manufacturer are usually the result of a replenishment policy. Replenishment policies give answers to the following questions (Silver et al., 1998):

How often should the inventory status be determined?

When should a replenishment order be placed?

How large should the replenishment order be?

The first question is addressed by differentiating between continuous and periodic review systems. Continuous review means that the inventory level is always known, which requiresmonitoring and immediate updating of the inventory level after every inflow or outflow. Periodic review means that the inventory level is only determined at fixed time intervals. Questions two and three are resolved by the replenishment policy (inventory control policy).

The R, Q-policy means that after a fixed period of time (R) a fixed quantity (Q) is ordered.Using the R, S-policy, also called order-up-to-policy or base stock policy, after the fixed review period (R) the inventory level is determined and then enough is ordered toraise the inventory level up to the level S, called order-up-to-level. Using the s,Q-policy a fixed quantity (Q) is order when the continuously monitored inventory level drops to the reorder level (s), also called the reorder point. The s,S-policy means that the inventorylevel is continuously monitored. As soon as the inventory level drops to or below thereorder point (s) enough is ordered to raise inventory to the order-up-to-level (S).

Assuming that the customer uses one of these policies to replenish his inventory, the manufacturer faces orders according to this policy. Further, it is important to know whether there is only one customer ordering a particular product or multiple customers. Generally, in a B2B environment it should easier to get at least some additional demand information compared to retailers, serving the mass of end consumers.

3.4.2 Demand variability, due to management decisions

A very important source of variability is the product portfolio. Clearly, more the products and more product varieties, variability will be high. If product variety is an important success factor in a particular industry, companies have to provide products in greater variety to earn money. In such a case, it is, of course, more important to design products and processes according to these customer expectations (product variety is order winner or at least order qualifier) than to minimize variability. Therefore, offering greater product variety can be regarded as "good" variability (Hopp and Spearman, 2007).

A famous example from the early days of customization is the automotive industry. At the beginning of the 20th century the pioneer of the assembly line, Henry Ford, offered cars with any desired color as long as it was black. This offer was an extreme reduction of variability by restricting product variety, making his manufacturing process very efficient and thereby the cars affordable to the mass. However, in the 1930s and 1940s General Motors took over much of the market share of Ford by offering greater product variety (e.g., more colors than just black). By introducing higher variability GM could not produce as efficiently as Ford, but could increase its revenues to such an extent that the additional costs were offset (Hopp and Spearman, 2007).

Therefore, it is important to know that product variety is an important cause of variability, but a possible reduction has to be in line the firm's business and operations strategy. If product variety is crucial for the company's success, it should not be reduced just to reduce variability, as the main purpose of a company is to earn money and not to reduce variability.

Finally, pricing has to be mentioned as a main source of variability. If the manufacturer uses discounts, which do not correspond to end consumer demand, then the demand variability will get induced. Generally, the more volatile prices create the worse scenario of the demand variability.

3.5 Bullwhip effect

The Bullwhip Effect refers to the phenomenon that demand variability increases upstream in the supply chain, and is one of the main drivers for the development of supply chain management (see Section 2.2). The main reasons for the bullwhip effect are (Lee et al., 1997b,a):

Demand signal processing - Even small changes in the demands of the direct customer (compared to the forecast) are interpreted as increasing (decreasing) future demand, leading to immediate adjustments of order quantities with the supplier. Possible counter measures are: Sharing point-of-sale Data (POS), single control of replenishment, lead time reduction, collaborative forecasting.

Order batching - Order batching means that customer demand is not passed on to the the supplier immediately but is consolidated by using replenishment policies. One important driver for using larger order batches is the fixed order costs. Clearly, the larger the order quantity the less frequent a company has to order and the lower the overall order costs. Possible counter measures: Electronic Data Interchange (EDI), consolidation by 3rd party logistics, regular delivery appointment.

Price variations - Changing prices within a supply chain increases the bullwhip effect, as additional demand fluctuations are induced, which are not related to end consumer demand. Possible counter measures: Every-day-low-price (EDLP) strategy, long-term contracts.

The rationing game - When a manufacturer has limited capacity and starts rationing production output among customers, the customers start to order more, because they observe or know that they can only get a particular percentage of the ordered quantity. Possible counter measures: allocation according to past sales, shared capacity and supply information, and limited flexibility of order quantities over time.

Generally, the bullwhip effect is an important aspect of demand variability within a supply chain. Concerning the general supply chain model of this study, the demand variability the manufacturer faces is to some extent due to the bullwhip effect. From the general definition it follows that the far more upstream the manufacturer in the supply chain the higher demand variability. Moreover, reducing the bullwhip effect means reducing variability and therefore comprises an important set of actions within the targets of this study.

3.6 Demand Management

Peter F. Drucker's view on predicting the future: "To try to make the future happen is risky; but it is a rational activity. And it is less risky than coasting along on the comfortable assumption that nothing is going to change."

A first basic concept to grasp is that demand management embodies much more than just developing a forecast of demand. According to Philip Kotler, a respected thought leader on marketing management, demand management involves influencing the level, timing, and composition of demand. Kotler makes two key points about demand management. First, demand management is the responsibility of the marketing organization (he considers the selling function parts of the marketing function). Second, the demand forecast is the result of planned marketing efforts. Planned marketing efforts should not just stimulate demand but should influence demand so that a company's objectivesare achieved.

Oliver Wight International has introduced a broad view model of demand management and it consists of the following elements;

Planning demand, this involves more than just forecasting

Communicating demand, which includes communicating the demand plan to the supply and finance organizations and, increasingly, to supply chain partners

Influencing demand, which includes marketing and selling tactics, product positioning, pricing, promotions, and other marketing and sales efforts

Managing and prioritizing demand, which includes managing customer orders to match available supply

In economics, demand management is the art or science of controlling economic demand to avoid a recession. In natural resources management and environmental policy more generally, it refers to policies to control consumer demand for environmentally sensitive or harmful goods such as water and energy. In the environmental context demand management is increasingly taken seriously to reduce the economy's throughput of scarce resources for which market pricing does not reflect true costs. Examples include metering of municipal water and carbon taxes on gasoline.

In economics the term is also used to refer to management of the distribution of, and access to goods and services on the basis of needs. An example is social security and welfare services. Rather than increasing budgets for these things, governments may develop policies that allocate existing resources according to a hierarchy of needs.The underlying idea is for the government to use tools like interest rates, taxation, and public expenditure to change key economic decisions like consumption, investment, the balance of trade, and public sector borrowing resulting in an 'evening out' of the business cycle.

Within manufacturing organizations, the Demand Management term is used to describe the activities of demand forecasting, planning, and order fulfillment.

John Maynard Keynes comprehensively challenged the Classical economic theory. He argued that a slump was not a long-run phenomenon that we should all get depressed about and leave the markets to sort out. A slump was simply a short-run problem stemming from a lack of demand. If the private sector was not prepared to spend to boost demand, the government should instead. It could do this by running a budget deficit. When times were good again and the private sector was spending again, the government could trim its spending and pay off the debts they accumulated in the slump. The idea, according to Keynes, should be to balance your budget in the medium term, but not in the short run.

So his theory was that the government should actively intervene in the economy to manage the level of demand. These policies are often known as demand management policies, as the idea of them is to manage the level of aggregate demand. Keynes demand management policies can be also defined as counter-cyclical demand management policies. They are termed this because the government should be doing the exact opposite to the trade cycle. When economic activity is depressed the government should spend more, and when the economy is booming the government should spend less.

If aggregate demand is low then the government should pursue reflationary policies such as cutting taxes or boosting government spending to push aggregate demand higher and boost employment and output. However, if aggregate demand is too high and causing demand-pull inflation then the government should pursue deflationary policies. These may include increasing taxes or cutting government spending to reduce demand.