A DSS approach to managing customer enquiries for SMEs at the customer enquiry stage

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Int. J. Production Economics 103 (2006) 332–346 www.elsevier.com/locate/ijpe

A DSS approach to managing customer enquiries for SMEs at the customer enquiry stage M.H. Xionga, S.B. Tora,b, Rohit Bhatnagara,c,, L.P. Khoob, S. Venkata a

Singapore-MIT Alliance (SMA), SMA-NTU Office, N2-B2C-15, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore b School of Mechanical and Production Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore c Nanyang Business School, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore Received 24 August 2003; accepted 11 August 2005 Available online 24 February 2006

Abstract A key requirement for small and medium-sized enterprises (SMEs) to remain competitive is the ability to assess incoming orders in terms of their profitability and determine the best orders that they should accept. This paper proposes a decision support system (DSS) approach that helps SMEs to make appropriate responses to customer enquiries. First, the customer enquiry and the generic processing procedure are addressed. Next, the framework of the proposed DSS approach is discussed. Four fundamental components of the framework are investigated in detail. For one specific enquiry, an available-to-promise (ATP)-based heuristic approach is developed for determining a feasible delivery date and checking its feasibility. For enquiries that are processed concurrently, an optimization model is built to evaluate them against limited ATP quantities in order to select a subset which will be fulfilled. Finally, the implementation issues for the proposed DSS are discussed. A simple application is illustrated to show how SMEs can respond effectively to customer enquiries using the proposed DSS approach. r 2006 Elsevier B.V. All rights reserved. Keywords: Customer enquiry; Dynamic BOM; Decision making; Demand; Capacity

1. Introduction and background For small and medium-sized enterprises (SMEs), the customer enquiry stage is critical because it has a significant impact on the final workload of these companies. At this stage, customer enquiries pertain to key attributes such as quantity, approximate Corresponding author. Singapore-MIT Alliance (SMA), SMA-NTU Office, N2-B2C-15, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore. Tel.: +65 67906235; fax: +65 67924217. E-mail address: [email protected] (R. Bhatnagar).

delivery time and price. The marketing department usually responds to these enquiries before the corresponding quotes are confirmed by customers and finally the enquiries are translated into customer orders. This paper focuses mainly on the framework to assess customer enquiries as promised or not, and the associated decision support system (DSS) approach which facilitates the management of enquiries at the customer enquiry stage. Pricing is not considered as a variable in this paper though it is likely to vary according to market condition and company policy.

0925-5273/$ - see front matter r 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2005.08.008

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Ideally, the marketing department’s objective is to secure as many customer orders as possible subject to certain production constraints—limited material availability and production capacity. However, in most companies, over-commitment to customer orders leads to unrealistic delivery dates (DDs). This in turn leads to delayed delivery, lost sales and extra cost. This poor performance is caused by inadequate coordination between different departments and inefficient information sharing for making decisions (Lee and Billington, 1992). It is therefore imperative to have access to real-time production information when dealing with customer enquiries at the early stage. Armed with real-time production information, the marketing department would be able to make more reliable assessment and responses to customer enquiries. At present, there is some commercially available software that is able to undertake the customer order related tasks. The Material Requirement Planning (MRP) system or Manufacturing Resource Planning (MRPII) system is able to schedule all customer orders against the scheduled receipts of materials/components and the standard manufacturing lead-time. This often leads to infeasible production plans because MRP assumes infinite supply beyond the standard lead-time and creates supply recommendations based on the order backlog (Stadtler and Kilger, 2000). Recently, some Enterprise Resource Planning (ERP) system such as SAP R/3 have sought to help manufacturing and logistics department to automate their order entry, process customer order and keep track of order status. These systems can also perform capacity planning and create a daily production schedule for manufacturing plants (Yen et al., 2002). This shows that ERP and MRP/MRPII are, to some extent, able to assist companies in dealing with enquiries at the customer enquiry stage. However, such software may be expensive and inappropriate for SMEs to use for only processing enquiries at the customer enquiry stage. Implementation of such software is complex, and requires huge initial investment and continuing maintenance expenditure (Halsall and Price, 1999). Furthermore, there are a lot of enquiries that only request a delivery time or a delivery quantity for a product without really committing them. This is especially true in today’s e-marketing environment. For example, a customer often makes a similar enquiry to several companies through the Internet simultaneously. The final selection depends primarily on the response to the

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initial enquiry. In these circumstances, a marketbased flexibility to respond to customer enquiries is very important to assist SMEs in making appropriate decisions (Chang et al., 2003). This issue has received scant attention in literature. Hendry (1992) and Hendry and Kingsman (1993) argued that once a company tenders for a job at the customer enquiry stage, it must determine the order routing and plan sufficient capacity to complete the order at the relevant workcenters. This is necessary to avoid delivering the order later than promised and/or producing the order at a high cost. The authors developed a methodology which addressed two decision levels, the customer enquiry stage and the job release stage. At the first stage, the order DD and price for a customer enquiry are determined. The second stage ensures that jobs related with quotes tendered at the customer enquiry stage are released in time to be completed by their promised DD. Using a DSS developed by the authors, the task of planning capacity and determining alternative, feasible tenders can be undertaken. As a consequence, both marketing and production objectives can be given proper consideration by choosing the most competitive option from those generated by the DSS. This research has mainly focused on the job releasing task after a customer order was quoted. However, the issue of how to tender a quote comprising reliable DD or alternative DD was not addressed. Kingsman et al. (1993, 1996) addressed the marketing and production considerations in responding to a customer order. They argued that a lack of coordination between sales and production at the customer enquiry stage often leads to unreliable delivery. Some potential approaches to integrate sales and production planning at the customer enquiry stage, as well as an input/output planning approach based on the control of a hierarchy of backlogs of work were presented in their papers. Halsall and Price (1999) presented a DSS approach for developing systems to support production planning and control for SMEs. These authors described the development of a prototype DSS in which the links between customer orders and manufacturing operations were maintained throughout the production planning process. Albers (1996) built an optimization model for jointly determining price levels as well as selling effort levels when a company sells a certain product mix through a salesforce to heterogeneous accounts. Ulusoy and Yazgac (1995) developed a multi-period, multi-product model with

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the objective of profit maximization, which addressed the cooperation between the production and the marketing department. The advertising efficiency and price of products can be determined within their developed model. Kehoe and Boughton (2001a, b) investigated the role of the Internet within the manufacturing supply chain as well as its impact on the manufacturing planning and control operation. The conclusion of their research was that the development of appropriate manufacturing information systems should work in conjunction with conventional MRPII and ERP systems. The relationship between the different manufacturing planning and control systems is depicted in Fig. 1. Every system is described in two dimensions, integration and prescription. In the integration dimension, Supply chain Resource Planning (SRP) has higher degree of integration as it incorporates more management functionalities across the whole supply chain. Compared to other planning systems, DSS is more specific but it is able to provide more decisional solutions for management. Therefore, the development of certain DSSs is critical during the production planning and control activities in companies. These DSSs help companies plan and control their production process, and provide key inputs for the decision-making process. Recently, Customer Relationship Management (CRM) has attracted the attention of both academics and practitioners. CRM focuses on managing the relationship between a company and its current and prospective customers as a key to success (Gebert et al., 2003). It provides a seamless integration between all applications and flexible deployment of solutions, merging front-office and back-office into one, and focuses on increased customer satisfaction. One of its important con-

siderations is to offer improved levels of customer service and support by means of a variety of ideas, approaches, and tools (Sheikh, 2003). In today’s highly customer-centric competitive market, improving customer service level would be crucial for firms to increase their competitiveness. It is really a challenging task but worth the effort (Xu et al., 2002). From CRM’s point of view, failing to successfully fulfill delivery promises to customers is mainly caused by information gap between marketing and production functions. Parente et al. (2002) surveyed production and sales managers and their findings indicate that the internal relationship between sales and production is important to the customer, specifically in Engineer-To-Order (ETO) production situation. Therefore, it is imperative to take into account the concerns of both production and sales personnel in managing customer enquiries at the customer enquiry stage. The customer enquiry management problem studied in this paper seeks to increase the efficiency of responding to customer enquiries and improve the feasibility of customer responses (Xiong et al., 2003b). Our findings would therefore contribute to maintaining better customer relationship. Xiong et al. (2002a, b) investigated a conceptual model for customer order fulfillment. They proposed adopting available-to-promise (ATP) as a criterion to measure the company’s capability to fulfill customer demands and developed a heuristic method to compute ATP. The DSS developed in this paper is based on this previous research. The interested reader is also referred to Pibernik (2005) for an overview of the models and algorithms used in ATP-based systems. The DSS approach may be a suitable method to deal with the complexities in processing enquiries at

Integration SRP

Notation: ERP

MRP – Material Requirement Planning MRPII – Manufacturing Resource Planning ERP – Enterprise Resource Planning SRP – Supply-chain Resource Planning DSS – Decision Support System

MRPII

Shared

Individual MRP

DSS Prescription

Procedural

Decisional

Fig. 1. Integration of several manufacturing planning and controlling systems (Source: Kehoe and Boughton, 2001b).

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MTS is to maintain an appropriate stock of finished goods. Of these four production environments, the ATO has predefined product families to produce, but the product demand is not known until customer orders are received. While receiving customer orders, an ATO company will quickly assemble a variety of finished products from components and product modules that have previously built. The MTO environment has similar characteristics except it does not have typical product families predefined. In MTO environment, some kinds of engineering design have to be done based on customer requirements (Samadhi et al., 1995). The concerns of both ATO and MTO environments will be about manufacturing or assembling capacity as well as its material supply. The complexity of the product is expressed in terms of number of subparts and its production constraints. Several studies have focused on the research area that fit in various points in the schema shown in Fig. 2. A great deal of research has been done on capacity-constrained production-inventory systems, most of which seeks to maximize profits. However, the capacity constraint in these models is simple because the production capability is not considered (Grubbstrom and Wang, 2003; Papachristos and Skouri, 2003; Hill and Dominey, 2001). This leads to the simplification that the product manufactured or assembled has fewer bill-of-material (BOM) levels and the production constraint is not complex. For a MTO or ATO company, as the product becomes complex, so does the management of capacity and materials. As a result, the customer demand fulfillment process is complex. This paper proposes an approach to help SMEs that

the customer enquiry stage as it is one of the effective methods to combine the robust rules and the fast computing capability of computers. In this paper, a DSS approach is proposed to help SMEs deal with enquiries at the customer enquiry stage. A framework is proposed and several fundamental components in this framework are investigated in detail. The rest of this paper is organized as follows. Section 2 briefly describes the research focus and assumptions of this paper. In Section 3, we discuss the details of customer enquiry and the process of handling enquiries. The general features of enquiries at the customer enquiry stage are presented. Section 4 presents the DSS framework. The four fundamental components for the proposed DSS approach are discussed in Section 5. Section 6 introduces the design and implementation issues of the prototype DSS. In Section 7, a simple application is described to illustrate how proposed DSS approach helps a firm in making an appropriate response to customer enquiries. Finally in Section 8, we present concluding comments and future research directions. 2. Research focus and assumptions

Product complexity

There are four typical types of production environments (Fig. 2), namely ETO, Make-ToOrder (MTO), Assemble-To-Order (ATO), and Make-To-Stock (MTS). In ETO strategy, the customer generally defines product specifications and then the manufacturer designs the product as per the requirements. Nothing is inventoried in the manufacturer’s system, not even the design. On the contrary, in the MTS environment, products are made and put into inventory before an order is received from a customer. A key requirement in

Research focus

Complex

Moderate

Simple Engineer-to-order (ETO)

335

Make-to-order (MTO)

Assemble-to-order (ATO)

Production environment Fig. 2. Illustration of the research focus in this paper.

Make-to-Stock (MTS)

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produce complex products and operate in the MTO or ATO environments. The assumptions for this research are











A practical methodology is needed by SMEs which cannot afford to implement commercially available systems such as an ERP system to help them manage customer enquiries. All necessary information (such as the quantity and timing of material supply and available production capacity) is provided by certain production planning and control systems already implemented in place. Product structure has been defined. Production routings for required products has been determined.

3. Customer enquiry and customer enquiry management A customer enquiry typically comprises information about requested quantity, DD and price for a product. The feasible area lies in a three dimensional space constructed with requested quantity, DD and sales price as axes, and provides a possible solution, or a guideline, for quoting orders for customers and negotiating with customers. Because the pricing issue is beyond the scope of this research, we will focus on quantity and DD as key decision variables. In all, there are four distinct kinds of customer enquiries. The DD is one of the most important considerations while responding to customer enquiries. Generally, the possibilities of a company winning an order will depend upon the value the company gives to the quoted DD. Failing to meet delivery promises will result in lost profit and depleted market share. In contrast, fulfilling the DD promises will help SMEs maintain a good image in the market for reliability. For the sake of securing an order, a SME has the choice of putting a lot of effort for increasing the material and production capacity, or making an alternative offer for the enquiry expecting further negotiation with customers. The approach to processing customer enquiries varies widely from company to company. However, there are two common cases: sequential enquiry processing and concurrent enquiry processing, as depicted in Fig. 3. In the sequential enquiry processing, the enquiries are considered and processed sequentially. It means that only one enquiry

nth enquiry

…

2nd enquiry

1st enquiry Processing Logic

(a) 1st enquiry 2nd enquiry …

Processing Logic

nth enquiry (b) Fig. 3. Two types of common enquiry processing style: (a) sequential enquiry processing; (b) concurrent enquiry processing.

is considered at a time. Generally, an enquiry requesting a large quantity or short delivery time needs to be treated in this sequential enquiry processing style. In other instances, companies process a set of enquiries jointly at periodical intervals, for example, weekly. Usually, these enquiries must be evaluated and selected against limited capability. The speed and the reliability of responding to enquiries are two major factors that determine the effectiveness of the company’s response. At the customer enquiry stage, the time spent in responding to an enquiry comprises the time between the receipt of the enquiry and the completion of the response. For some very important enquiries such as those with a large requested quantity for the product or those with very short delivery time, the response time would be longer than usual as the response should be considered and approved by key personnel, e.g. the tender approval committee, in the company. The reliability of a response is the probability of keeping the promise after responding to an enquiry. The speed of responding to enquiries and the response reliability should be controlled in managing customer enquiries. If a company is to maintain a reliable image in the market, it should make a response as fast and as reliably as possible. These two factors can strongly affect customer’s decision about selecting its supplier. There are a lot of research issues involved within the broad area of managing enquiries at the customer enquiry stage. The first important issue is the treatment of customer enquiries. The procedure responding to an enquiry is basically a multistage decision process (Kingsman et al., 1996). But

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for SMEs, a compressed decision-making structure is helpful since it can make them agile and fast respond to the changing needs of customers. In this kind of decision-making structure, the initial decision is to determine whether to accept the order based on a prescreening process. Another decision is to make a quote to customer enquiries, which basically includes the response to the request for the above-mentioned three basic enquiry elements— DD, quantity and sales price. Generally, if the customer has specified a DD, the time frame is considered as fixed. The next task is to determine whether sufficient materials and production capacity is available or can be provided to complete the new order in addition to other jobs that have already been confirmed for that time period. If the enquiry specifies a flexible DD, a feasible DD is defined and included in the response in terms of materials and production capacity.

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Receive customer enquiries

Pre-screen ♦ check product specification ♦ appraise quantity, DD and price

Phase 1

Evaluate against ♦ materials availability ♦ production capacity Phase 2 Adjust capacity if necessary ♦ over time (OT) ♦ subcontract ♦ modify production and supplyplan

Respond to enquiries ♦ feasible DD ♦ possible price ♦ delivery plan

4. The DSS framework for managing customer enquiries

Phase 3

Although customer enquiries can arise in a variety of ways, the generic workflow for processing enquiries at the customer enquiry stage is presented in Fig. 4. There are three phases in this workflow. In Phase 1, the incoming enquiries undergo prescreening where the product specification is checked and the delivery quantity, DD and product price are appraised. The purpose of this phase is to reject those enquiries that are apparently unsuitable to be produced by the company. The remaining enquiries will be evaluated against limited available capacity in Phase 2. Two types of capacities—materials availability and production capacity—that present the constraints of materials supply and production are considered in Phase 2. To meet the production needs, capacity could be adjusted by planning for over-time (OT) and/or subcontracting as well as by modifying production and materials supply plan. During Phase 3, the feasibility of a specific DD, price and delivery plan will be checked with regard to a specific customer enquiry. At the end of Phase 3, such response will be finalized and sent to key personnel in order to get appropriate permission. The proposed architecture of the DSS is shown in Fig. 5. Corresponding to the workflow illustrated in Fig. 4, several modules are built to perform different required tasks. As a whole, the proposed DSS can be envisaged as a front-end module to deal with enquiries before these are translated into customer

Approve ♦ Get appropriate permission for specific responses

Fig. 4. The workflow for processing enquiries at the customer enquiry stage.

orders and enter the production planning process. The following streams of research arise from the proposed architecture: (1) broad approaches and methodologies for managing enquiries for SMEs at the customer enquiry stage, and (2) development issues associated with the proposed DSS approach. The methodology for managing customer enquiries refers to mechanisms and supporting approaches for processing customer enquires. In the proposed system architecture shown in Fig. 5, there are several major modules described as below that sequentially perform the tasks of responding to customer enquiries. The development issues are related with the implementation of the methodology. These will be discussed in Section 6.




Check DD: The purpose of this module is to check if a requested DD is feasible based on available material and production capacity. If the

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Negotiate

J

INTERFACE


Pre-screen

Check DD

Evaluate enquiries

Determine DD

Make response

Get Approval

Enquiry DB

Customer DB

Capacity DB

Material DB

Fig. 5. General DSS architecture for managing customer enquiries for SMEs.







Value of the order in affecting future business Possibility of repeat business J Contribution to balancing the workload for work centers J Gaining entry into new market sector. Make response: This module does not attempt to determine the optimal solution. Rather, it presents the option for decision makers to assess distinct responses and make a final well-informed choice. The output of this module will be a decision regarding whether to accept an enquiry and if so the DD to quote for an enquiry with a flexible DD. Also, the decision about further negotiation with customers about the delivery quantity and DD is made if this enquiry is considered very important. Based on the information provided, the decision makers can investigate each option further, if required. For example, the detailed impact a DD would have on the workload can be examined graphically, and the alternatives of attaining the same DD by adjusting capacities could be assessed by decision makers. J

CUSTOMER Accept/ Enquiries Reject/

DD is infeasible, some capacity adjustments will have to be made and this is indicated in terms of the necessary actions such as OT usage, operator reallocation or rescheduling. The implication here is that if an enquiry is considered acceptable, its feasibility in terms of available capacities must be assured. Determine DD: This module generates a feasible DD or a set of alternative DDs. In this process, the production routing and available capacities are used to assess how the order can fit into the existing workload of the company. Hence, for an enquiry with a flexible DD, a DD or a set of feasible DDs should be determined. Evaluate enquiries: Given limited available capacity, enquiries need to be evaluated in order to select a subset which will be fulfilled. Such evaluation can be based on objectives such as reducing the inventory cost and/or increasing the potential profitability of orders. The DSS should provide several objectives to facilitate the needs of a variety of users. Hence, mathematical models may be combined with judgmental rules to improve the accuracy of the evaluation process. Examples of judgmental rules include J Profit potential of fulfilling the order J Importance of the customer

There are several other modules in this proposed DSS architecture. The purpose of prescreening is to roughly verify the enquiries and reject those that are apparently unsuitable. The objective of the response approval module is to ensure approval from appropriate decision makers for important and/or unusual enquiries. Examples of such enquiries include those requesting a very large quantity or those with a very short delivery time. These enquiries would strongly influence production planning and scheduling. Hence a high-level approval is required for finalizing before sending these to customers. The nature of the link between the proposed DSS approach and other production planning functions will depend on the other systems that a company has in place. Generally, the DSS proposed in this research would be used at the front end to provide necessary information for demand planning and generating the master production schedule. Moreover, the output of the DSS also supports the firm’s capacity planning and the material requirements planning processes. 5. Fundamental components for the proposed DSS approach There are some fundamental components that we use in the proposed DSS architecture. In this

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materials availability restricts this production process; otherwise, the production capacity is the key constraint. In this way, both materials availability and production capacity are effectively accounted for in the ATP computation process. The ATP computation result is thus more realistic and reliable. An example of ATP along time horizon is shown in Fig. 6. The ATP(t) represents the available quantity which can be used to fill customer orders at time bucket t. Once ATP(t), for t ¼ 1, 2,y,T (planning horizon), is known, the fulfillment plan for all enquiries being considered can be made in terms of the different objectives. Xiong et al. (2004) presented a model of profit maximization for evaluating customer enquiries, in which bucketized ATP is designed to be a criterion to measure the fulfillment capability of a firm. With ATP, a sequential enquiry can be evaluated separately and a set of enquiries can be concurrently evaluated in terms of certain objectives. The next section will focus on a heuristic approach to evaluate a single enquiry.

section, four such components that comprise the DSS approach are described. 5.1. ATP and extended dynamic BOM approach The traditional order fulfillment mechanism of quoting orders against finished product inventory and supply lead-time often results in order promises that are not feasible and poor on time delivery performance (Stadtler and Kilger, 2000). To overcome this shortcoming, we used a criterion ATP to measure the firm’s capability to meet customer requirements. ATP is a time bucketized quantity (typically on weekly basis) for a given type of product. It is computed based on the materials availability of all components. When computing ATP, exploding multi-level BOM is a necessary and complex issue. Xiong et al. (2003a) presented a dynamic BOM approach to tackle the complex ATP computation for products with multi-level BOMs. A dynamic BOM is essentially a two-level BOM which is generated dynamically in terms of the component material availabilities. Through an iterative process to generate dynamic BOM, the ATP can be accumulated through exploding BOM from top downwards by the associated computation approach. Typically the ATP computation complexity increases as the product BOM becomes more complex. As ATP computation takes into account the quantity and lead time of each component in a product BOM, it becomes very simple to compute ATP with an associated series of two-level dynamic BOMs. However, the above ATP computation procedure ignores production capacity constraints, which is a limitation. Tor et al. (2004) developed an Extended Dynamic BOM Approach (EDBOM) to combine the production capacity issues with dynamic BOM after the production routing is determined. When a specific dynamic BOM is created, the production capacity required for this manufacturing process is checked against the available production capacity. If the available capacity is sufficient, the Q

5.2. ATP-based approach to responding to an enquiry If D represents a required quantity at a time bucket, t+3 for instance, the determination of a feasible DD or the feasibility of a required DD necessitates that we verify whether there is enough available ATP(t), for t ¼ 1; 2; . . . ; t þ 3, to fill this required quantity D. Considering Fig. 6, a heuristic approach to check whether a DD is feasible or not can be easily developed. The pseudo-code of this heuristic approach is presented below. The assumptions for this heuristic approach are (1) Only one enquiry is considered at a time (2) The enquiry is for only one product (3) Either an enquiry can be met totally or it cannot be met as a whole and (4) ATP located after the time bucket of an enquiry cannot be used to fulfill the enquiry quantity.

ATP(t+1)

D

ATP(t)

t

ATP(t+3)

t+1

339

t+2

ATP(t+…)

t+3

Fig. 6. Illustration of ATP and a single enquiry.

…

T

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Notations: D—customer enquiry quantity for a product ATP(t)—ATP quantity at time bucket t, t 2 T (planning horizon) t_requested—time bucket (DD) for customer requested quantity D QATP—accumulated ATP quantity t—index for time bucket, t ¼ 1 indicates the starting time bucket of planning horizon t_suggested—suggested time bucket in terms of ATP(t), t 2 T. Step 1. Initialization. t ¼ 1, QATP ¼ 0. Step 2. Verification for whether the enquiry quantity D at time bucket t can be met in time bucket t. QATP ¼ QATP+ATP(t). If QATP is less than D, then go to Step 3. Otherwise, D can be met, go to Step 4. Step 3. Iteration stop condition judgment. If t is greater than T, then D cannot be met, go to Step 5. Otherwise, proceed to next time bucket (t ¼ t+1), go to Step 2. Step 4. Enquiry can be accepted. t_suggested ¼ t. If t_suggested is less than or equal to t_requested, then t_requested is feasible. Otherwise, D can only be met in the suggested time bucket—t_suggested. Go to Step 6. Step 5. Enquiry cannot be accepted. The enquiry with D and t_requested cannot be met in terms of ATP(t) for t 2 T. Step 6. Termination.

process these enquiries differ from company to company. If only limited ATP quantities are available, an evaluation is required for selecting a subset of customer enquiries so that firm objectives are met. The objectives used for evaluating enquiries depend on the company’s policy and business philosophy. However, one of the underlying principles is to identify the influence of such enquiries on company’s business. An example of an appropriate objective is to both increase revenue and minimize inventory cost. Here, we propose a model for achieving this objective. Index: i—index of customer enquiries, i 2 I I—number of enquiries t—index of time buckets, t 2 T T—length of planning horizon. Parameters: p(t)—unit sales price in time bucket t Ei(ti)—quantity required by enquiry i during time bucket ti, i 2 I and ti 2 T ti—requested time bucket for enquiry i, i 2 I ATP(t)—ATP quantity in time bucket t, t 2 T ch—unit inventory holding cost per time bucket. Decision variables: ai —binary variable stating whether accepting enquiry i bti —fraction of ATP(t) allocated to enquiry i, 8t 2 T, i 2 I. Objective: Max profit ¼ f revenue  f cost ,

t2I;t2T

The above pseudo-code describes the major procedures to determine a feasible DD (t_suggested) and check the feasibility of a requested DD (t_requested) of an enquiry. Correspondingly, the output of this evaluation process is whether the requested DD is feasible, and the suggested DD for a specific enquiry. Although such a process is straightforward, it becomes more complex to evaluate multiple enquires. This is described in the next section. 5.3. ATP-based approach to respond to several enquiries at a time Sometimes responses to customer enquiries are considered at periodic intervals. The principles to

(1)

where frevenue is revenue from accepting customer enquiries and fcost is inventory holding cost. The revenue from accepting certain customer enquiries is computed as follows. f revenue ¼

I X

½ai  E i ðti Þ  pðti Þ.

(2)

i¼1

The inventory holding cost is the cost incurred by accepting certain customer orders. As illustrated in Fig. 7, for a given enquiry quantity Ei(ti) and ATP(t), the total inventory holding cost without filling any enquiry quantity is represented by ch  ½ATPðtÞ  ðT  tÞ when keeping ATP(t) from time bucket t to the end of planning length (Tt). Assuming that enquiry i is accepted to fill,

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Quantity ATP(t) ch ∗ Ei(ti) ∗ (T-ti)

Ei(ti)

Time bucket t

…

t+1

ti

ti-t

…

T

T-ti

Fig. 7. Relationship between ATP, enquiry quantity and inventory holding cost.

an amount of inventory cost, ch  E i ðti Þ  ðT  ti Þ, must be reduced from the total inventory holding cost. Therefore, for all ATPs and enquiries, the inventory holding cost is defined by f cost ¼

T X

fch  ½ATPðtÞ  ðT  tÞg

t¼1



I X

½ch  ai  E i ðti Þ  ðT  ti Þ.

ð3Þ

i¼1

Subject to




Enquiry quantity constraint: ti X

bti ¼ ai

8i 2 I.

(4)

t¼1




ATP constraint: I X

bti p1

8t 2 T.

(5)

i¼1




Fraction of ATP allocation constraint: 0pbti p1




8i 2 I; t 2 T.

5.4. Capacity adjustment (6)

Domain constraint: ai ¼ 0 or 1

8i 2 I,

ti ; E i ðti Þ; pðtÞ; ATPðtÞX0

(7) 8i 2 I 8t 2 T.

are selected under limited ATP quantities is worked out. In the above mathematical model, constraint (4) limits the allocation fraction bti , stating that there is an equational relationship between the sum of allocation fractions from ATPs to a specific enquiry and the decision variable ai . Constraint (5) states that the allocated quantity from every ATP within each time bucket should not exceed the ATP quantity in that time bucket. Constraints (6) and (7) specify limits for decision variables. This model provides an adaptive combination of the inventory holding cost and the profit associated with the acceptance of customer enquiries. Thus, it can support management decisions in practice. The proposed model is a mixed binary linear programming model, and its global optimum can be obtained by using commercially available optimization solvers such as LINGO developed by LinDo System Inc. (2001). For this mathematical model, we also developed a heuristic approach for evaluating multiple enquiries, based on the contribution of an enquiry to profit (Xiong et al., 2004). With such an objective, all enquiries can be evaluated sequentially.

(8)

Using binary decision variable ai , for i ¼ 1, 2,y,I, the solution for which subset of customer enquiries

In some cases, because of the importance of a customer or the value of an order in affecting future business, it may be necessary to take special actions by adjusting material and production capacity to produce the order in less than the normal production time. The proposed DSS should provide such flexibility to accommodate the order. For example, this is true in cases of orders with a large requested quantity or a very short DD. As already mentioned, the capacity referred in the proposed DSS includes

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the material availability and production capacity. For adjusting material availability, the following information is used in the DSS.






Alternative materials for all materials Critical material and its related material shortage and Delivery lead time and lot size for critical materials.

In the proposed DSS, the output of adjusting material availability includes the report for alternative materials, critical paths/items, recommended purchased orders for critical materials, and the suggestions to expedite/de-expedite purchased orders. At the planning stage, the production capacity is usually planned on a weekly basis by means of the forecast values of the total workload on the shopfloor (Hendry, 1992). However, when a specific enquiry (for example an order for a large quantity) is received, the DSS should be able to adjust the production capacity to allow for the special needs for this particular order. The two most common

methods of adjusting the production capacity— assigning OT and reallocating operators between different workcenters—can be easily incorporated in the DSS. The OT is usually assigned to some bottleneck workcenters (called critical workcenters in the paper) to increase their available working time. This is necessary to expedite such orders that may be delayed if no action is taken. The method of reallocating operators between different workcenters will be appropriate if there is an imbalance of workload across the shopfloor. 6. An illustrative application of the DSS approach Although the Web-based prototype which demonstrates the applicability of the proposed DSS approach is still being developed, an example is used to illustrate how the DSS helps SMEs assess the relative value of several enquiries. First, given the materials availability and production capacity, a real-time ATP along 10 time buckets can be computed. A bar chart (shown in Fig. 8) presents ATP quantities. Using this bar chart, a decision

Fig. 8. Bar chart of an ATP computation result.

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maker can easily understand how much capability is available to fill customer demands. Furthermore, the user is able to adjust the materials availability and production capacity separately to see what happens to ATP quantities. By this ‘‘what–if’’ analysis, the decision maker is able to have more choices when responding to an enquiry. For example, if the user adjusts the capacity (available working time) of one critical workcenter, the APT can be computed by EDBOM approach and the curve of ATP vs. capacity can be shown in Fig. 9. From such analysis, managers can easily study the influence of changing capacity on ATP quantities. They can also experiment with alternative courses of action, e.g. adjusting capacity by assigning OT for critical workcenters in a specific time bucket, so that a requested quantity can be produced and customer requirements can be finally met. Suppose we evaluate eight enquiries {E1, E2,y,E8} listed in Table 1, the unit sales price is constant (10) and the unit inventory holding cost per time bucket is 1. The output interface of the system is shown in Fig. 10. Because the sum of ATP

ATP

Capacity

40 ATP & Capacity

35 30 25 20 15 10 5 0 1

2

3

4

5 6 7 time bucket

8

9

10

Fig. 9. ATP vs. capacity of a critical workcenter.

Table 1 Data of evaluated enquiries Enquiry no.

Requested quantity

Time bucket

1 2 3 4 5 6 7 8

40 60 20 50 50 20 40 50

4 6 4 2 8 6 10 10

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(157 units) is less than the sum of enquiry quantities (330 units), only a subset of these eight enquiries will be selected for fulfillment. As in Fig. 10, the proposed DSS suggests selecting a feasible subset {E2, E5, E7}. For subset {E2, E5, E7}, the profit according to Eqs. (1)–(3) is 1045. If we consider other two feasible subsets, say {E2, E5, E6} and {E1, E3, E6}, their profits are 925 and 445, respectively. Therefore, the fulfillment solution suggested by the DSS is more profitable to the company in terms of revenue and inventory holding cost. In this way, the DSS assesses feasible subsets of enquiries and provides supporting information for decision makers to make appropriate final choices. 7. Managerial implications of implementing the proposed DSS The purpose of this study was to develop a DSS framework to help SMEs make responses to customer enquiries. Although some models related to this DSS framework may be found in previous research, a systematic approach to help SMEs make responses to enquiries at the customer enquiry stage has not been established yet. From a research perspective, further work is required in this area. For example, a more efficient working environment which combines the robust rules and the fast computing capability of computers needs to be further addressed. In order to achieve better CRM, SMEs need to improve their customer satisfaction level and generate better more effective responses to customer demands. However, for achieving this aim, we argue that it may be inappropriate to use expensive software system such as ERP to deal with customer enquiry management for SMEs at the customer enquiry stage. It is thus necessary to develop a systematic but cost-effective approach for SMEs. Our study provides a framework for responding to customer enquiries and has addressed several constructs involved. The solutions provided in the study appear reliable and valid for SMEs. Additionally, industry practice and academic studies would benefit from the efforts to develop the integrated decision making environment specific to such complex decision making problems. The research model developed in this paper could be expanded to include other important decision variables. There are numerous model extensions that would merit consideration. For example,

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Fig. 10. The output of the evaluation of multiple enquiries.

extending this model to include decision variables such as suggested DD would represent a significant contribution to the model. The suggested DD for an enquiry may be different from the requested DD. By introducing the suggested DD for every enquiry, the model can provide a guideline for negotiating with customers if their requested DD cannot be promised in terms of available capacity. To make an appropriate response to customer enquiries, managers need a better understanding of the linkages between marketing and production departments, as well as the implications of marketing strategies such as new product launch and seasonal discount. The proposed DSS approach will provide marketing managers with the flexibility to make ‘‘what–if’’ analysis on production capacity when they plan to implement a specific marketing strategy. 8. Summary and conclusions Assessing enquiries effectively at the customer enquiry stage to attract an optimal load of profitable work in a highly competitive market is a key imperative for many SMEs. Failing to successfully fulfill delivery promises would not only result in lost profit and lost market share, but also negatively affect future customer orders. However, this is a complex task for SMEs at present. The key causes

of this include the information gap between marketing and production functions as well as lack of funds for implementing expensive software such as ERP system. The objective of this paper is to suitably address these problems. A DSS approach is presented for managing enquiries for SMEs at the customer enquiry stage. The DSS seeks to assist the marketing and sales personnel in SMEs to make good and well-informed decisions. By providing the flexibilities to assess different responses and experiment with alternative courses of action, the speed and efficiency for making an informed response to customer enquiry can be significantly improved. Intended to work in conjunction with other production planning and control systems such as conventional MRPII and ERP, this DSS can be envisaged as a front-end module to deal with customer enquiries before these are translated to real customer orders and enter the production planning process. Starting with a generic workflow modeled for streamlining the enquiry treatment, a framework of the proposed DSS approach is presented. Some fundamental approaches supporting this DSS framework are developed. In order to determine a feasible DD or check the feasibility of an enquiry, an ATP-based heuristic approach is discussed. Another ATP-based optimal model is built for evaluating enquiries under limited ATP quantities.

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For these two approaches, the issue of computing ATP on the real-time basis is very important. We present a method for ATP computation by means of the EDBOM. Using EDBOM, both constraints of the material availability and the production capacity can be easily taken into consideration. The ATP computation result is thus more realistic and reliable. The capacity adjustment approach in the DSS is very important for preparing a negotiation with customers and suppliers. It will provide alternatives for securing a specific order. Since the prototype of proposed DSS approach is currently under development, a simple application was used to illustrate its ability to help SMEs process customer enquiries. For the implementation, it is intended that future research will incorporate the DSS integration of other production planning and control systems, followed by a field assessment in one or two representative SMEs. Acknowledgements This research was supported by research grants from Singapore-MIT Alliance (SMA) and Nanyang Technological University. The authors would like to thank the reviewers whose comments helped us to enhance the presentation of this paper. References Albers, S., 1996. CAPPLAN: A decision-support system for planning the pricing and sales effort policy of a salesforce. European Journal of Marketing 30 (7), 68–82. Chang, S., Yang, C., Cheng, H., Sheu, C., 2003. Manufacturing flexibility and business strategy: An empirical study of small and medium sized firms. International Journal of Production Economics 83 (1), 13–26. Gebert, H., Geib, M., Kolbe, L., Brenner, W., 2003. Knowledgeenabled customer relationship management: Integrating customer relationship management and knowledge management concepts. Journal of Knowledge Management 7 (5), 107–123. Grubbstrom, R.W., Wang, Z., 2003. A stochastic model of multilevel/multi-stage capacity-constrained production-inventory systems. International Journal of Production Economics 81–82, 483–494. Halsall, D.N., Price, D.H.R., 1999. A DSS approach to developing systems to support production planning control in smaller companies. International Journal of Production Research 37 (7), 1645–1660. Hendry, L.C., 1992. COPP: A decision support system for managing customer enquiries. International Journal of Operations and Production Management 12 (11), 53–64. Hendry, L.C., Kingsman, B.G., 1993. Customer enquiry management: Part of a hierarchical system to control lead times in

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