Design of a Marketing Decision Support Method for Price Quotation In a multi-stage Single-product Kanban-Based Manufacturing Environment

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2 XII ADM International Conference - Grand otel - imini Italy - Sept. 5 th -7 th, 00 Design of a Marketing Decision Support Method for Price Quotation In a multi-stage Single-product anban-based Manufacturing Environment M. Al-Tahat*, A. Liverani*,. Persiani* *DIEM - University of Bologna, V. le isorgimento, Bologna 4036, Italy. Tel: +39 (05) , ax: +39 (05) Abstract anban is a philosophy aimed at minimizing total costs through the pursuit of continuous improvement, the application of specific manufacturing and commercial techniques, a commitment to total quality and the unlocking of the full potential of an organization's human resources. In more recent times there has been an increase in research into the effect on and importance of the application of specific purchasing and marketing techniques to the success of anban. This paper focus on practice in the areas of empowering and improving the sales efforts in a competitive environment market, it describes a method that implements a formal methodology to assess the price quotation of products that have been manufactured in anbanbased production environment. A multi-stage single-product anban based production line with variable demand is considered. The problem is to study and analyze anban-based Manufacturing Environment in order to design and develop a anban-based Marketing Tool (MT) for the purpose of determining the price to be quoted, the delivery time and the technical specifications to a single order item in a make to order manufacturing setting. A newly computer program is presented, it is developed for Microsoft Access database and Visual Basic for Application (VBA). The system consists of three main phases: ) production line database (nowledge epresentation Phase); ) mathematical modeling of the production line; 3) computer program development (Inference Engine). umerical simulation examples are carried out which allow studying the feasibility of applying developed system and its influence in the continuous improvement of sales. The research indicates that the ability to tap into and maximize the sales potential of the organization address: mohammad.al-tahat@mail.ing.unibo.it G-60

3 XII ADM International Conference - Grand otel - imini Italy - Sept. 5 th -7 th, 00 will be a maor determinant of the success, or otherwise, of continuous improvement processes, and therefore the anban methodology. ey words: anban; Design of Manufacturing Systems; Systems Design; Operations Planning and Control; Methods Engineering; Continuous Improvements.. ITODUCTIO In the early950 s Mr. Taiichi Ohno, the founder of the Just-In-Time JIT production system began to study the efficiency of the American supermarket. e was trying to determine how the structure of the supermarket could be applied to a manufacturing environment. In 953, Taiichi Ohno applied the principles of ust-in-time and anban signals to Toyota s machining factory. These principles were so successful that they were applied to other factories and the Toyota anban system evolved. The anban concept has attracted wide interest during the last decades, and a large amount of research papers on anban-based production systems has appeared in the literature, through these, literature have been found to consider the different characteristics of the anban systems. Some of these papers considering the general philosophy and review of anban [, ], others deal with the modeling of such systems, as will as the development of mathematical and/or simulation models. Evaluating, improving and optimizing anban systems performance [3-6], as will as minimizing production cost, have been addressed in several other research papers. anban stands for kan- card, ban- signal. A anban is a signal card containing all the information required to be done on a product at each stage along its path to completion and which parts are needed at subsequent processes. These cards are used to control Work-In-Progress WIP, production and inventory flow. A anban system allows a company to use JIT production and ordering systems, which allow them to minimize their inventories while still satisfying customer demands. A anban system consists of a set of these cards, with one being allocated for each part being manufactured, that travel between preceding and subsequent processes. Mr. Taiichi Ohno, a vice president of Toyota, to adapt to changes in demand, and therefore production more quickly, developed the anban system.. TE POBLEM AD OBJECTIEVES In today s global manufacturing characterized by quick technology advances and availability of capital, firms are increasingly dependent on fast response times as an important source of sustainable competitive advantage, in response to that many industrial companies are interested in implementing JIT marketing relationships concepts that lead to quick response to customers inquiries which considered as an important strategy for a firm to survive in this timebased competition market place [7]. One type of relationship marketing is anban exchange, which involves close collaboration between buyer and seller on product specifications and production information flows [8]. ully implemented anban exchange involves many activities G-6

4 XII ADM International Conference - Grand otel - imini Italy - Sept. 5 th -7 th, 00 that should be carried out by Product Marketing Engineer who is responsible for various tactical and some strategic marketing aspects which aid in the growth of the product line, such as generating price quotations, resolving delivery/factory issues, working with production engineering to understand test/quality aspects of products, responding quickly to customer surveys/questionnaires, tracking design and working with customers to aid in the design-in of products and to define future product requirements, generating presentation materials, and visiting customer locations. The problem considered in this paper is given by studying and analyzing anban-based Manufacturing Environment, designing and developing a anban marketing relationship system with the special obective to meet the requirements of a multi-stages single-item anban-based manufacturing lines. urthermore, in this paper, a simple way to get the price to be quoted, the delivery time and the technical specifications to a single order items in a make to order manufacturing setting is shown... Production line formulation We consider a multi-stages single product manufacturing system that procures raw materials, processes to convert them into finished products, and finally delivers the completed products to the customers. The production line is composed of multi workstations, and a anban system operates between two consecutive workstations. The anban are used primarily as mechanisms to feed back information from the succeeding to the preceding workstation, and to transport materials to the succeeding workstation. The raw materials are stored near the demand points on the production line such that they are available immediately whenever needed. The raw materials are replenished at the first workstation of the production line instantaneously from a supplier in batches at different intervals of time. The demand of finished goods at the last workstation changing with time. The production rate at any stage is assumed to be at least equal to the demand rate of the WIP at that workstation so as to ensure no shortage of WIP or the final product. Since the demand of the final product is changing over time, the similar pattern of demand of the WIP is reflected at the intermediate stages as well. Because of the changing in demand over time, the number of anban at each WIP areas plays a role in the transporting the WIP from previous station to the succeeding station. The quantity of WIP transported per shipment is less with the higher number of anban incurring lower inventory cost of WIP to the next station and more cost to the anban system due to higher number of anban. On the other hand, the quantity of WIP transported per shipment is higher with the fewer number of anban incurring higher inventory cost of WIP to the next station and lower cost to the anban system due to fewer number of anban employed between the stations. Based on the above-considered manufacturing line we will achieve the research obectives by configuring a anban Marketing Tool (MT) to communicate with the customers easily and quickly. G-6

5 XII ADM International Conference - Grand otel - imini Italy - Sept. 5 th -7 th, otation To model the interactions of the different production activities the following notation have been used: Q : Quantity of raw material ordered (Units/Cycle), : Unit holding cost of WIP at the th WIP area ($/Units/Unit Time), C : Average unit holding cost of aw materials ($/Units/Unit Time), : Average unit holding cost of finished products ($/Units/Unit Time), : umber of anban at the th anban stage (WIP area), : anban container size (Units), : Manufacturing shipment cost ($/Batch), : Average ordering cost of raw material ($/Order), W : Cost of withdrawing anban from stage to stage ($/anban), : umber of WIP areas in the production line n : umbers of orders of raw materials, m : umber of full shipments of finished goods from the last station, L : Lead time (Time Units), T : Cycle time (Time Units), T : Production time or update time (Time Units), D 0 : Demand at time t = 0 (Units) D ( t ) : Demand rate of finished goods at time t. D i : Demand at the end of period i (Units) D : inished products demand at the end of the production cycle (Units/Cycle) ω : ate of change (increase) of demand of finished goods (Units/Unit Time) P 0 : Production rate at time t = 0 (Units) p : ate of change (increase) of Production per unit time (Units/Unit Time) OEE : Overall equipment effectiveness (%) TC : Total manufacturing cost for producing the product by multi stages ($).... Modeling assumptions The model introduced here has been designed to simulate the introduction of single product onto a multi workstations production line according to the following assumptions; () There will be no shortages allowed at any stage in the system ( p ω ) ; () The WIP areas (anban stages) are assumed to be large enough to accommodate the WIP inventory; (3) There is an instantaneous replenishment of raw materials at the first workstation for each kind of products; (4) The final demand of the finished products is assumed to increase linearly with time. The G-63

6 XII ADM International Conference - Grand otel - imini Italy - Sept. 5 th -7 th, 00 advantage of modeling with linear demand is that it can analyze system with increasing, constant or decreasing demand of products at the time of its market introduction, persistent demand and losing the market in competition, respectively. Another advantage is that it can roughly capture the life cycle pattern of demand of a product; (5) The quantity of materials consumed during the production period T is equal to the amount of raw materials supplied during the time period T for one-to-one conversion ratio (i.e., ƒ= D / Q = ); (6) Each workstation is considered as an equivalent single processing server, which can processes only one container at a time one type of products; (7) A generic i th stage, consists of :(a) A workstation with one processing center;(b) Production anban post collecting anbans waiting for full containers from the preceding stage;(c) A downstream buffer, where full containers with withdrawal anban at output queue a wait for requesting anban from the next stage;(d) An upstream buffer, which collect full container with production anban waiting to be processed at the i th workstation; (8) Materials from each workstation move to only one adoining workstation and visit processing stages only once and they never come again to that stage. 3. TE DEVELOPED ABA-BASED MAETIG TOOL (MT) In a real-life Multi stages single products manufacturing environment, the anban system is subected to various types of uncertainties such as stochastic variable demand, inequality and multiplications of ordered products, production rates, Setup and changeover time etc. In this section, a newly developed anban system is discussed which is used to determining the price to be quoted, the delivery time and the technical specifications to a single order items in a make to order manufacturing setting taken into consideration the blocking and starvation caused by the said uncertainties during a production cycle. The new system is termed a anban-based Marketing Tool MT. The steps of the work are detailed and an analysis of MT is demonstrated using an examples. 3.. MT system architecture As mentioned previously, the newly developed MT is consist mainly of the following three parts components, these are; mathematical modeling of products pricing system; production line database; the codes which is control the events and the procedures of the production system and the user interface. These components are linked together to form the over all structure of the MT system in the way shown in ig.. Visual basic VB codes and Structured Query Languages SQL codes have been used to develop the interface between functional parts of the system. This arrangement helps in developing each functional part separately as we will ustify in the following. G-64

7 XII ADM International Conference - Grand otel - imini Italy - Sept. 5 th -7 th, 00 Pricing Model Input Inquiry Computation and Data Integration Output Quotatio n Production Database ig.. Overall structure of MT Pricing model The main goal of the needed model is to calculate the expected product cost, in order to prepare a quotation to be sending to the customer upon his request. The quotation should include clearly the product price, specifications, and the delivery time. Many formulas have been found in the literature like [7,9,0] as a basis for cost calculations. One of the best foundation for anban system seeks single-product multi stages lines is given by Sarker and Bhaba [9], Sarker has derived the following production model which seeks single product multi-stages lines in terms of cost functiontc equation () for n orders of raw materials, anban in the stage th of production ( =,..., ), and m shipments (lots) of finished products. As a bases to his model he assumed that the components of the total cost are the cost of finished goods, inventory Cost of work-in-process inventory and Cost of raw material inventory therefore he developed and solved the following model for single product production lines. TC n T P0T = n T 3nT pt 3 + β P0 T + α avg + avg = pt + = β α n T α β + m Q pt 3 + α pt 3 6 n T + ( T T ) avg np = α α β avg ( n ) + = pt α β 3 + () Where the production time T, and the production cycle time T, could be calculated by equation () and equation (3) respectively. G-65

8 XII ADM International Conference - Grand otel - imini Italy - Sept. 5 th -7 th, 00 α avg D T = P0 ± T = = = α = 0 ± D0 + ω 0 p * ω * Q P + * p * D and W = β avg =, W β = () (3). (4) The number of setups, m and the number of kanbans at stage, can be calculated using the relationships between m, n and in equation (6) and equation (7). W J = n (6) n P0 + ( n ) pt m = D0 + ω T (7) T = m L (8) d ( TC ) * When the value of dn n set to zero, and the equation solve for, the optimal number of kanbans at stage * m, and the optimal number of shipments for the same product * * can be calculated using the relationships between m, n and as demonstrated in equation (9), (0) and equation () respectively. (5) m * W = n * n = * D C = 0 D 0 * P + ( n J ( u ) ) pt + ω T (9) (0) () Solving the above model needs the determination of all types of independent input variables, which available in the production database, these variables are classified into; products level variables and production line level variables. Product level variables (Processing details) are related to the product itself, the product has it is own independent variables that should be determined and fed to the developed system as an input variables, these are, CO D,ω, 0 D, P p, 0,, Q, CT W,,, and, =,.... Production line level variables (General data) are independent input variables related to the manufacturing line all products have G-66

9 XII ADM International Conference - Grand otel - imini Italy - Sept. 5 th -7 th, 00 the same values of these variables, these variables such as,,. On the other hand, T T the output parameters of the model,,, m, =,... C, =,... and TC could be calculated by the inference engine of the developed MT system according to equations (), (3), (), (9), (0), and () respectively. inally products unit price will be equal to the production cost plus the profit margin (PM ) divided by the total quantity, as shown in equation () below. ( + PM)*( TC) Unit Pr ice = D () 3... Inference engine and erythematic manipulation The most powerful tool of MT for development and generation result of our proposed model through performing sophisticated target driven data manipulations, is the inference engine, which allows rapid constructions of sensitive end-user interfaces. This part of the MT has been designed from the start to run in a fast way according to the daily variations in customer s inquiries, production resources and limitations. It integrates the database with the pricing model, computes and manages deferent parameters values in their corresponding queries. These queries have been design using SQL format and classified into two types; auxiliary queries like avg sum, afa, Xaxa, etc; and main query like Stages Details, minimum cost, Quotation, etc. Simply, this phase retrieves input data from the database, performs the needed calculations and holds their values, and finally generates an output result. The input variable data are the different independent variables of the MT which are stored in the database elements these are classified into; line level variables or general variables; and Product level variables or details variables. General independent variables are founded in the costing elements table, they are fixed for all types of production these variables are,,, and PM while the details variables p p, Q,,, W ω,, D, n,pp, B u, CO, CT, u,, D 0 o u, are. The details variables are founded in the demand data table, production data table, process plans table and change over data table. In addition to that there is an input fixed data in the general level which includes; product ID; product name; customer ID; product description; unit weight; product dimensions; and production standard, that represent all the needed information a bout any product. The related tables for these data are, drawing table which includes information about drawings, customers table that include data about customers such as; customer ID; company name; contact name and complete addresses, employees table, product mix table and plans assignments table. The output of the MT has been designed to hold the values of the anban system parameters (the dependent parameters) then to translate these values to the desired MT output in terms of unit price, delivery time and production specifications Production database An access database is one of the most useful environments in which to store and retrieve data and an ideal tool to manage and manipulate that data. The benefits of a well-structured G-67

10 XII ADM International Conference - Grand otel - imini Italy - Sept. 5 th -7 th, 00 database are infinite, with increased efficiency and timesaving being more immediately obvious benefits. In-depth investigation of what the anban-based production line is, how it works, and what manual systems it has in place, will its employees need access to the database from out in the field? These and a crowd of other questions have been asked during the initial investigation. The answers enabled us to design, create, integrate, and develop the MT database, which has been constructed from forms, queries, reports, and menus in an efficient and exclusive way with the minimum data duplication and the easier representation of the user's perspective. The MT database describes the details of the production in the two levels, products level and line level (actory level). This manufacturing knowledge include the production capabilities with its corresponding costs rates, Manufacturing process plans and they re details, information a bout demand and demand rate, and many other general information. All these information is stored in Microsoft access database in a table format to be retrieved later by the system to generate queries that are necessary when applying the proposed approach to generate competitive customers quotations. 3.. Expressive simulation examples To demonstrate more how the MT work for multi-stage single product anban system. We assumed a received inquiry for product 440 which is produced by the second process plan PP 440 = Second, the line parameters assumed to be as =$/unit/day, =$0.5/unit/day, =$50/order, OEE =00%. Technical data from the production know how related shows that this item will be produced with a 3-stage process plan called second (anban stages 440 =3), for this plan p P 440=$35/batch 440 =0 units/day, o 440 =00 units/day, 440 =[0.,0.,0.] W $/unit/day, and 440 =[50,60,95] $/anban (withdrawal cost). rom the received orders of ω production it found that the demand change rate 440 =0units/day, and the required quantity for this item in this inquiry 440 D D =000 units with an initial quantity of 0440 =80units/day. PODUCT O Product ame Unit Price ($) Delivery Within (Days) 440 Adaptor $ Product Specifications According to DI.07 Product Description Used for Excavator om 966 ig.. MT output result. As shown in the previous figure, based on 0% profit margin the delivery time will be within 6 days from receiving a confirm production order with a unit price of $6.7 for a quantity of 000 piece. The number of shipments that should be delivered to the customer, the anban numbers and the anban sizes are m 440 C =, 440 =[5,,3], 440 =[400,8,54] respectively. The maximum number of anban is circulated in the third anban stage, which is indicated by G-68

11 XII ADM International Conference - Grand otel - imini Italy - Sept. 5 th -7 th, Thus, the optimal number of anban in the third anban stage is 3440 =3.the above result explanation are shown in ig. and in ig. 3 as an out put result for the MT. PODUCT O Process Plan Stage () o of ABA () Size of ABA (C = Unit) 440 Second Second Second ig. 3. MT output result for anban numbers and sizes. 4. ESULTS AD COCLUSIOS anban technique has been used to achieve the goal of JIT philosophy in a production environment to maximize the profit and to improve the effectiveness of production line. The research issue considered here is one of the main problems faced anban-based production lines that operate in an industry which are very susceptible to market competition. This research has considered the issues of empowering and improving the marketing efforts thought the generations of a quick and a feasible response to the customer s inquiries in a multi-stage single product anban-based production line. or that we have suggested an approach for adusting the different quotations variables. Introducing such a method brings the difficulty of sharing manufacturing resources between different part types in addition to the decisions of whether or not to produce that were considered, the presented method indicates that the issues of raw material orders, WIP inventory and finished goods shipments have to be considered together rather than separately in order to maximize the profit of production. The research has proved that these issues are dependent on one another. The cost parameters of raw materials, changeover between products, WIP and finished goods dictates the number and the sizes of anban needed at production stage and batch sizes for shipments. A logical and constructive link between production line database and the suggested pricing model has been done, this integration results in a satisfactory output of the newly developed MT that shows the important benefits of such integration. The developed MT has been designed with the goal of being used in the multi-stage single-items anban based production lines, The numerical simulation examples show that the concept is realizable and able to create successfully a sound results for any given customer inquiry with a satisfactory quality of generated solution. The MT s database can hold large number of records, contacts and notes, this comprehensive will structured history allows the system to capture production data, automatically and generate quotations easily. With MT, we can instantly enhance the effectiveness of the company's sales and communications activities. EEECES []. John Bicheno. anban: The Old and The ew. Institute of Operations Management, Volume 5 o. 07, September 999, Page:. []. Mark Bower. anban In Batch Production. Institute of Operations Management, Volume 0, o. 03 (April/May 994). G-69

12 XII ADM International Conference - Grand otel - imini Italy - Sept. 5 th -7 th, 00 [3]. Vito Albino, Michele Dassisti, and Geoffrey O.Okogbaa. Approximation approach for the performance analysis of production lines under a anban discipline. International Journal of Production Economics, volume 40, 995, pages [4]. T.. Wonga, C.. Cheunga and. Laub. Decision support system for a ewellery manufacturer. International Journal of Production Economics, Volumes 60-6, 0 April 999, Pages -9. [5]. A.Gunasekaran and P.Cecille. Implementation of productivity improvement strategies in a small company. Technovation, Volume 8, Issue (5), 998, pages [6]. Moeeni,.; Sanchez, S.M.; Vakharia, A.J. obust design methodology for anban system design. International Journal of Production esearch, Volume 35, Issue 0, October 997, Pages [7]. Mohsen Elafsi. An operational decision model for lead-time and price quotation in congested manufacturing systems. European Journal of Operational esearch, Volume 6, Issue, 6 October 000, Pages [8]. ichard Germain and Cornelia Dröge Effect of Just-in-Time Purchasing elationships on Organizational Design, Purchasing Department Configuration, and irm Performance. Industrial Marketing Management, Volume 6, Issue, March 997, Pages 5-5. [9]. Bhaba. Sarker, and Chidambaram V. Balan. Operations planning for a multi-stage anban system. European Journal of Operational esearch, Volume, Issue, 999, Pages [0]. endrik Bessembinder. The degree of price resolution and equity trading costs. Journal of inancial Economics, Volume 45, Issue, July 997, Pages G-70