Supply chain management related issues

Since the introduction of the term “supply chain management” (SCM) in 1982, it has received a lot of interests both in the literature and practice. According to Christopher (1998), a supply chain (SC) can be defined as a network of organizations that are involved, through upstream and downstream linkages in the different processes and activities that produce value in the form of products and services in the hand of the ultimate consumers. Therefore, supply chain management is the task of integrating materials, information and financial flows in order to fulfill customer demands with the aim of improving competitiveness of the SC as a whole (Stadtler, 2005). Furthermore, supply chain management can be viewed as logistics outside the firm to include customer and suppliers (Lamber and Cooper, 2000). The planning tasks can be classified to different levels depending on the time horizon, namely strategic, tactical and operational. The operational planning includes vehicle routing, scheduling, etc;

the tactical planning involves the procurement, inventory and transportation system, etc;

and the strategic planning includes the determinations of plant sites, the number of plants, etc. Specifically, the main determinations of strategic level include (1) the

number, location, capacity of manufacturing plants; (2) the active suppliers; (3) the amount of raw materials and products to produce and ship among suppliers, plants and customers, etc (Vidal and Goetschalckx, 1997). In practice, the short-term operational flexibility is directly related to the strategic decisions.

There exist different areas of literature associated with supply chain management, such as strategic management, logistics, relationships/partnerships, etc. Among those studies, a lot of studies have concerned about demand amplified, i.e. bullwhip effect in a supply chain, which can be described as the variability of orders increases from down stream to upstream (Simchi-Levi et al., 2000). This phenomenon makes supply chain planning difficult. Different ordering policies were considered and discussed to reduce the order variance. Geary et al. (2006) identified major causes of bullwhip and provided several principles to reduce bullwhip effect. A great deal of literature has investigated the impacts of different ordering policies on the order variability and the most mentioned policies are (s, S) ordering policy (e.g. Kelle and Milne, 1999). In addition to quantify bullwhip effect, some studies proposed different strategies to reduce bullwhip effect, such as accurate demand forecasts (e.g. Metters, 1997) and application of information flows technique (e.g. Cachon and Fisher, 2000). Bourland et al. (1996) exploited timely demand information (TDI) to reduce inventories. The results show that inventory-related benefits are sensitive to demand variability, the service level provided by suppliers, and the degree to which the order and production cycles are out of phase. Berman and Kim (2001) considered a problem of dynamic replenishment of parts in the supply chain consisting of single class of customers, company, and supplier.

To sum up, the above literature can be classified as production-inventory models that involve uncertainties in the environment. The most attentions focused on the probabilistic modeling of the customer demand side. The timeframe of the literature

can be classified as operational planning in the supply chain management.

In another line of research, the economic order quantity (EOQ) or lot sizes decisions have been discussed extensively in the inventory control literature (e.g. Silver and Peterson, 1985; Wagner, 1980). Schniderjans and Cao (2000) and Fazel et al.

(1998) presented an analytical model to evaluate and compare the total purchasing and inventory cost associated with just in time (JIT) and EOQ. The proposed model expanded the classical EOQ to include a quantity discount scheme. Other studies relaxed the assumption from deterministic demand to investigate the impacts of stochastic demand on the optimal ordering policy (e.g. Haneveld and Teunter, 1998).

Other studies assumed a stochastic supply and discussed the optimal ordering policy with EOQ (e.g. Hariga and Haouari, 1999; Wang and Gerchak, 1996). Other studies have applied fuzzy theory to investigate the optimal ordering policy, such as Vujošević et al. (1996) assumed both inventory cost and ordering cost as two fuzzy variable and Roy and Maiti (1997) constructed a fuzzy EOQ model with time-dependent unit cost under limited storage capacity. In sum, past EOQ models are mainly based on the time frame, with the active suppliers being known and given.

There are still some studies aiming at reviewing literature about supply chain management. Croom et al. (2000) considered the bodies of literature associated with supply chain management and discussed the different perspectives adopted in different studies. Beamon (1998) provided a focused review of literature in multi-stage supply chain modeling. According to Beamon (1998), the four categories of modeling approach are (1) deterministic analytical models, in which the variables are known and specified, (2) stochastic analytical models, where at least one of the variables is unknown, and is assumed to follow a particular probability distribution, (3) economic models, and (4) simulation models.

The demand forecast methods and information flows technique have been demonstrated important factors influencing inventory cost and customer service performance in past literature. Most of the studies aimed at short-term demand uncertainty and the influencing on company’s operational efficiency. However, the extent to which the economic fluctuation influences the market demand of high-tech product is quite apparent. And the duration of different economic fluctuations during the planning year is quite different, which may further influences service quality and cost economies. Though some conventional statistical forecasting models can be applied to generate future customer demand, the fluctuations surrounding customer demand may affect accuracy of forecasted results.

In addition to apply accurate forecast methods, some studies aimed at investigate the relationship between supply side uncertainty and ordering policy. Whybark and William (1976) presented a framework for characterizing and studying the uncertainty which affect inventory level investment and service level performance in a material requirements planning (MRP) system. This study also showed how safety stock or safety lead time can be used for buffering a part against uncertainty. The results showed that under conditions of uncertainty in timing, safety lead time is the preferred technique, while safety stock is preferred under conditions of quantity uncertainty.

Petrovic et al. (1998) represented supply chain fuzzy models and a corresponding simulator, developed to assist in decision making on operational supply chain control parameters in an uncertain environment. The objective in this study is to determine the stock levels and order quantities for each inventory in a supply chain during a finite time horizon to obtain an acceptable delivery performance at a reasonable total cost for the whole supply chain. Güllü et al. (1999) analyzed a periodic review, single-item inventory model under supply uncertainty. In the study, the uncertainty in supply was

modeled using a three-point probability mass function and the supply is either completely available, partially available, or the supply is unavailable. Machine breakdowns, shortages in the capacity of the supplier, strikes, etc., are possible causes of uncertainty in supply. An algorithm was also given in computing the optimal inventory levels over the planning horizon.

Zimmer (2002) considered a supply chain, consisting of one producer and one supplier in a just in time environment, where the supply of component is uncertain due to uncertain availability of the capacity of the supplier. This study also proposed a coordination mechanism that allows the system to perform just as well as a centralized one. Moinzadeh (2002) considered a supply chain model consisting of a single product, one supplier and multiple retailers and studied the benefits of information-sharing in a supply chain characterized as the availability of online information of retailers’ inventory positions to the supplier. Maia and Qassim (1999) presented an analytical solution for an optimization model that determines whenever it is preferable to incur inventory cost or opportunity costs. The results showed that products should be stocked only if opportunity costs are higher than inventory costs.

In another line of research, operational issues in the supply chain for high-tech manufacturing industries have been intensively discussed. Chen et al. (2005) considered production scheduling planning and developed a capacity planning system, which considered the capacity and capability of equipments for multiple semiconductor manufacturing FABs. In their study, “Capacity” refers to the upper threshold on the load on an operating unit and “Capability” refers to a specific processing capability of a machine, respectively. Due to there are often capacity shortages, Mallik and Harker (2004) proposed a game theoretic model to deal with the conflicts in forecasting and capacity allocation to product lines between product and manufacturing managers in a

semiconductor manufacturing firm. The result shows that truthful reports of demand and capacities by the managers can be induced through a bonus. Since the capacity expansion is costly and time consuming, it is important to incorporate the economies of scale and demand fluctuation into the production-distribution planning phase.

Numerous studies have addressed supplier selection issues in supply chain management. Some of them investigated the important factors for selecting suppliers by collecting data and by conducting hypothesis (e.g. Verma and Pullman, 1998;

Jahnukaiene and Lahti, 1999). The important criteria include price, quality and delivery reliability, etc. Other studies focused on the quantification-factors and discussed the supplier selection problem as a cost-minimization formulation problem.

Supplier selections also influence the design problem structure with additional factors such as geographical location of the suppliers and the manufacturing plants. There are few studies that consider the effects of spatial distance between suppliers and manufacturing plants in the optimal supplier selection process. Table 2.1 summarizes main issues and results in the existing literature on supply chain management.

Summary:

There are extensive studies addressing the impacts of uncertainty factors on company’s cost and customer service level performance with respect to demand and supply uncertainty. The literature also proposed various strategies to reduce the impacts of uncertainty under different scenarios. These strategies include accurate forecast methods, usage of information exchange system, etc. Since the demand uncertainty has been proven the source of downgrading the performance of a supply chain, forecast methods are emphasized in some studies. Most of these studies focused on the short-term operational issues and constructed analytical models in terms of operation research. However, the performance resulting from demand fluctuation was

seldom evaluated based on an economic theory. In addition, past EOQ models are mainly based on the time frame, with the active suppliers being known and given.

Few studies have considered the impact on the optimal shipping frequency and size and resulting costs because of geographical combinations of suppliers and manufacturing plants and the total flows between them. This study formulates a series model to investigate the above issues. This study aims at investigating strategic issues of supply chain management. This study also incorporates fluctuations in customer demand into designing a supply chain network and proposes different fine-tune strategies in response to short-term fluctuations.

Table 2.1 Main issues, features and results in literature on supply chain management related issues

Authors Main issues and features Important results Christopher (1998),

Stadtler (2005), Lamber and Cooper (2000)

Discuss the issues and modeling approaches in supply chain management

Define supply chain, supply chain management, etc

Croom et al. (2000), Beamon (1998)

Literature review Four categories of modeling approaches in supply chain management: (1) deterministic, (2) stochastic, (3) economic, and (4) simulation models

Metters (1997), Cachon and Fisher (2000)

Quality bullwhip effect and propose strategies to reduce bullwhip effect

Accurate demand forecasts and apply information technique can effectively reduce the impacts of bullwhip effects

Fazel et al. (1998), Schniederjans and Cao (2002)

The impacts of JIT purchasing and EOQ with a price discount on the total inventory cost

The higher the value of the item, the carrying cost, or the ordering cost associated with the EOQ model, and the smaller the quantity discount rate, the wider will be the range of demand for which JIT remains preferable Güllü et al. (1999) Periodic review, single-item

inventory model under supply uncertainty

Focus on developing an efficient algorithm in solving the optimal inventory levels

Maia and Qassim

(1999) Develop an analytical solution for determining whenever it is preferable incurring inventory cost or opportunity costs

Products should be stock only if opportunity costs are higher than inventory cost

Mallik and Harker (2004)

Deal with conflicts in forecasting and capacity allocation to product lines between managers from the operational level

Truthful reports of demand and capacities can be induced through a bonus

Verma and Pullman (1998), Jahnukaiene and Lahti (1999)

Investigate important factors for selecting suppliers by collecting data and conducting hypothesis

The important criteria are price, quality and delivery reliability, etc

Source: this study