Virtual Production Control System

Abstract—This work develops a novel virtual production control system (VPCS), which is allocated on a manufacturer side to adjust production pace of a supplier using a short-term demand plan for reducing inventory level. In addition to remotely monitoring production progress of a supplier, the proposed VPCS allows manufacturers to pre-schedule the latest outputs using an integer programming model, to simulate the short-term plan by a stochastic colored timed Petri Nets, and to adjust the production pace of the supplier via the plan. An industrial case study shows that the supplier inventory was reduced by 13.8% over that of the real inventory level with an order-fill rate exceeding 99.6% using the proposed VPCS.

I. INTRODUCTION

NDER intense global market competition, supply chain management is proposed to link the upstream and downstream flows of products, services, and information across businesses to integrate supply and demand [1]. However, supply chains are typically subject to a bullwhip effect, which is caused by cascaded safety stocks for demand forecast. Notably, this effect spreads to all chain manufacturers when forecasts are uncertain and time-dependent. To reduce the bullwhip effect on inventory, a management model of vendor managed inventory (VMI) is presented [2], in which a manufacturer shares its material forecast with its supplier and authorizes the supplier to manage manufacturer-side inventory. By adopting the VMI model, forecast and authorization capabilities enable a supplier to produce and deliver material on time as well as maintain an agreed inventory of materials on the manufacturer side.

The material shortage on the manufacturer side is reduced by using the VMI model; however, the inventory problem is deferred to a supplier when the supplier and manufacturer relationship is unequal. Since a manufacturer typically assumes its suppliers have unlimited capacities to respond to arbitrary material demand changes, which can be caused by such factors as market variation, forecast error, and production variation. To adapt to and meet demand changes, suppliers are suffering from large material inventory for

Manuscript received January 14, 2010; revised June 5, 2010. This work was supported in part by the National Science Council of the Republic of China under Contract No: NSC-95-2221-E-327-042-MY3 , and by the Ministry of Economic Affairs of the Republic of China under Contract No: 98-EC-17-A-02-S1-130.

1_{H. C. Yang is with the Institute of System Information and Control,}

National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan. (e-mail: [email protected]).

2_{Y. L. Chen and F. T. Cheng are with the Institute of Manufacturing}

Engineering, National Cheng Kung University, Tainan, Taiwan. (e-mail: [email protected]).

3_{M.-H. Hung is with the Department of Computer Science, Chinese}

Culture University, Taipei, Taiwan, R.O.C. (Email: [email protected]).

buffering demand changes using the VMI model. However, the bullwhip effect still exists on the supplier side.

The supplier-side bullwhip effect is mainly caused by manufacturer-side forecasting errors and production variations, both of which can be decreased by sharing information of supplier-side, i.e. work in process (WIP) information, is completeness and precision, where completeness refers to complete sharing of production progress information with manufacturer, while precision represents shared information with sufficient time resolution for estimating material production progress [3][4]. For instance, RosettaNet is a popular standard in the semiconductor industry for exchanging WIP information. Additionally, a virtual factory was proposed by a supplier to serve a customer with the corresponding WIP information by demand to monitor a real factory [5][6]. By using a virtual factory, a customer can evaluate its material demand by checking production progress on the supplier side. However, as the virtual factory is located on the supplier side, the supplier provides the WIP information which could be modified by the supplier for business concerns. Obviously, incomplete and inaccurate WIP information eventually causes planning losses and increases the incidence of production breaks on the customer (manufacturer) side.

In addition to acquiring WIP information, a supplier also applies WIP information to predict production progress and locate production problems, in which production parameters from WIP information are identified for simulating or programming according to a production schedule [7-10]. When production parameters are identified using incomplete WIP information, the parameters could fail to support the supplier in estimating and evaluating its production, not to mention used by the manufacturer.

Therefore, our concept is if a manufacturer can receive complete and precise WIP information of supplier to identify production characteristics, i.e. capacity and throughput, and takes these characteristics into subsequent demand changes, then using updated supplies the manufacturer can reduce forecast errors and production variation and can freeze demands in short-term. With frozen short-term demands, a supplier can effectively cut material inventory which was reserved to prevent demand changes. Based on the concept, in contrast with conventional WIP applications, this work presents a novel active WIP application, the virtual production control system (VPCS), which is allocated on manufacturer-side to virtually evaluate and control supplier-side production utilizing WIP information. When a customer’s order change (Fig. 1), the VPCS allows a

Virtual Production Control System

Haw-Ching Yang1*_{, Member, IEEE, Ying-Liang Chen}2_{, Min-Hsiung Hung}3_{, Senior Member, IEEE}

and Fan-Tien Cheng2_{, Fellow, IEEE}

manufacturer to collect WIP information from supplier-side for evaluating demand changes such production risks as production risk and capacity bottleneck. If a demand change is issued, the VPCS supports both sides to negotiate this change before it is being released.

The remainder of this paper is organized as follows. Section II describes the design of the proposed VPCS architecture, including data preprocessing, WIP programming, and production planning modules. A simple case is presented to explain the step-based results obtained by the proposed VPCS. Section III then summaries an industrial case study result. Conclusions are finally drawn contributions of this work described in the last section, along with recommendations for future research.

II.PROPOSED SYSTEM

Fig. 2 shows the VPCS control structure. According to a customer orders and forecasts, a manufacturer releases a long-term material demand forecast to a supplier by the T0

period. Within the T1 period, the manufacturer will receive a

shipping plan from the supplier. In the same time, the proposed system also allows the manufacturer to collect transaction-based or snapshot-based WIP information from the supplier by the T3 period, and to develop a short-term

demand plan before releasing it to the supplier. Since the

proposed system assists a manufacturer in addressing production risks, i.e. delivery risk and production bottlenecks, negotiating with a supplier for finding the risks’ solutions in time, and allowing the manufacturer to decrease demand change probability demand based on the supplier capacity. Therefore, if a supplier would follow the short-term demand plan to control production paces, its objective of high inventory turn-over rate (TOR) under a high order-fill rate (OFR) can be achieved, effectively.

The proposed VPCS has three principal modules: the data preprocessing, the latest WIP programming, and the feasible production planning modules. In the VPCS, the data preprocessing module processes Stage WIP-In and Stage WIP-Out information by importing a shipping plan and WIP information from a supplier and exporting a verified shipping plan and stage WIP information. The latest WIP programming module locates the WIP-Out boundary with the latest production outputs according to the shipping plan. Furthermore, the feasible production planning module simulates Stage WIP-Out from distribution parameters, which are estimated, forecasted, and synthesized into a Petri Nets model for predicting production risks, including late delivery, material gaps, and bottlenecks. The details of the modules are described as follows:

z Inputs of the proposed VPCS

A. Shipping plan: when a material demand forecast is received from a manufacturer, a supplier should reply with a shipping plan including shipping quantities and dates by products to the manufacturer within time-constraint T1, e.g., 24 hr.

B. WIP data: after replying to a shipping plan, a supplier provides transaction-based WIP data to a manufacturer, where the transactions are triggered and recorded according to the transaction events by lot, e.g. move-in, move-out, and hold, which are defined in the supplier WIP glossary.

, 1 ˆ p k θ + ,1.. p k θ , 1, , 1, , 1 p k p k p k S + X + Y + ˆ ˆ ˆ_{, ,} X Y V , 1 p k S′ ₊ , 1 p k S +

Fig. 2. VPCS Control Structure Fig. 1. Production Control Flow with the VPCS

Since WIP data in the VPCS is utilized to analyze correctly production behaviors and locate production risks, the glossary is defined in the linking of WIP in production stages. Hence, the WIP data also contains production details such as material number, order number, process stage, product group, transaction event, transaction time, quantity, and status. z Data Preprocessing

By retrieving, transferring, and storing raw data, the data preprocessing module has two functions Stage (the green blocks in Fig 2) Preprocess WIP-In Data and Process Stage WIP-Out Data function to qualify WIP data for the following modules. The Preprocess Stage WIP-In Data function links the corresponding WIP dataWIP{X,Y}with the shipping plan

, 1

p k

S + , whereXand Y represents inputs and outputs by stages, respectively. Here both of the functions process WIP{X,Y}

which represents the corresponding input and output times

and quantities of a WIP, e.g. a lot. Additionally, the Preprocess Stage WIP-Out Data function analyses acquired WIP quantities, production losses, and output times. When abnormal or unreasonable data is embedded in WIP data, such as a missed WIP, earlier move-out time than move-in time, or extreme quantity differences between I/O and stages, this module could identify the erroneous data for monitoring and fixing.

z Latest Stage WIP Programming

The Program Latest Stage WIP function (the blue block in Fig 2) finds the stage WIP boundaries by maximizing the OFR to schedule the latest inputs and the latest outputs of a shipping plan, where the inputs are dominated by an inventory turnover policy and the outputs are limited by historical capabilities. This function adapts an integer programming method to find (1) the OFRVp k, +1, where p is

product ID, k is the current period, and k+1 is the next period to be estimated. In this module, decision variables are the estimated number of input Xp k, +1and the estimated number of

outputYp k, +1. The following three constraints are given: eq. (2)

shows that the output is less than the sum of the initial WIP

0

p

W and the inputXp k, +1; eq. (3) denotes that the output , 1

p k

Y + is bounded by the product capabilityHg k, +1, where g is

the product group; and, eq. (4) shows that the estimated TOR should exceed the target TOR 0

1 k

R+ , where the TOR

represents the sum of Yp k, +1 and an initial finished good 0

p

F over the sum of Xp k, +1and Wp0, as shown in the following

equations. Objective: 0 , 1 , 1 , 1 ( ) max . p k ( p k p ) p k Y F V S + + + + = ₍₁₎ Subject to: 0 , 1 , , 1 (Xp k+ +Wp)×ep k ≥Yp k+

,

∀

,

∀

After solving the integer programming model, estimated outputYp k, +1, i.e. the latest output, is taken as the simulation

output in the subsequent module. z Feasible Production Planning

From programming results, an estimated input Xp k, +1

represents an estimated latest input time, which does not imply that cycle time differences exist for various products. It means that since a production needs times, a feasible input must be earlier than the corresponding Xp k, +1 even with

sufficient parts for production.

To identify feasible production inputs and outputs plans (the brown blocks including Find Production Parameters, Forecast Production Parameters, Synthesize Production Model, and Plan Feasible Stage Production functions in Fig 2), the production planning module first integrates inputs and the corresponding outputs from periods 1 to k to find the process distributed parameters θp,1..k , which includes

batching, inter-arrival time, and cycle time.

However, for available constrained resources collected data represent more than one activity; thus, a frequency histogram formed from historical data is difficult to fit in an identical distribution. This finding implies that different activities contribute various distributed families into a frequency histogram. We need a multimodal distributed identification method to recognize different activities. For instance, although collected transaction-based WIP information denoted as move-in and move-out time can be used to determine a cycle time, which is embedded with three production associated activities message transmission delay, material waiting time, and production processing time. These three activities contribute to the corresponding probabilities in a cycle-time frequency histogram; i.e., transmission delay represents inter- or intra- message communication time delays for production release and execution, waiting time is time spent waiting for a material or resource, and processing time is actual production time. For any production lot, cycle time of each lot is cascaded with the three activities, indicating that cycle time equals to sum of transmission delay, waiting time, and processing time.

To identify distributed parameters of cascaded activities, this work adopted the following two statistics to evaluate fit results: the Kullback-Leibler Distance (KLD), which is used to determine the distance between two probabilistic models; and the Kolmogorov–Smirnov value,D=max F xf( )−F xe( ),

where F x_e( )is the expected c.d.f., F_f( )x _{is the fitted CDF,}

and D represents the maximum error between the fitted and the expected CDF.

Since different statistics represent different fit measurements, this work adopted the two statistics to fit cascaded activities and proposed a niche genetic programming (NGP) method to obtain a Pareto front set, which presents the optimal distributed parameters chromosomes, to form the optimal fit set. In each genetic generation, more than 30 niches provide feasible chromosomes, which are filtered to satisfy significance level α in the D statistic and sorted by a KLD-based fitness function, to mutate for generating next generation chromosomes. When acceptable conditions are reached, the reserved chromosomes in each niche eventually form a Pareto front set. According to the law of large numbers, the derived Pareto set mean is normal or uniform distributed; such that, the set mean and variance can be used to represent actual processing time.

After applying the identified process parameterθp,1..k to a

regression method, the next period process parameterθˆp k, +1

can be derived and used in the next period process simulation. In simulation, the VPCS uses the stochastic colored timed Petri Nets to synthesize a plant modelPNp k, +1, which is used

to simulate stochastic and time behaviors of identified lots in processes. For instance, in Fig. 3, a circle represents a place denoting a state of a token, i.e. a lot, a rectangle represents a transition denoting a state change condition, and an arc stands for a condition from a state to a transition or from a transition to a state. Additionally, θˆp k, +1 with the corresponding

distributions are allocated at the arcs and the specific activities are defined in the actions of transitions to realize functions, e.g. lot splitting, merging and processing. Based on a given plant model, the shipping plan Sp k, +1 is then used to

simulate for estimating feasible inputs, outputs, and OFR. z VPCS configuration

To apply the VPCS to various processes and different suppliers, the VPCS configurations including master data and production types are presented as follows.

A. Master Data: These data include bills of material, process structures, and data mapping tables, where bills of material define product ID and quantity relationships from the material phase to the finish phase. Additionally, process structures define material routings in processes, while data mapping tables cover product groups, process naming maps, and stage maps.

B. Production Type Configuration: The VPCS supports two production types, i.e. assembly and process, which are configured by specific processes definitions and workflow configurations of a supplier. For instance, in the TFT-LCD industry, a backlight production belongs to an assembly type, while a driver IC production is categorized as a process type. z Outputs of the proposed VPCS

A. Inventory profile: This inventory profile assists a manufacturer to explore a supplier’s inventory for monitoring and controlling inventory level. This profile, which includes updated production states and estimated TOR of material inventory, provides a manufacturer with obsolete material forecasts. Based on forecasts, a manufacturer can review and even modify demands before releasing to a supplier for reducing obsolete inventory.

B. Delivery profile: This delivery profile shows OFRs which are estimated according to a shipping plan confirmed by a supplier, that presents potential production problems, such as production bottleneck and starvation for negotiation with supplier to find problem solutions. To explore potential problems, this profile provides estimated input Xˆp k, +1 ,

estimated outputYˆp k, +1, estimated OFRVˆp k, +1, estimated tardy

quantity, reason for tardiness, and capacity bottleneck. The following illustrative example demonstrates the effectiveness of the proposed VPCS. We assume that a manufacturer sends a material demand Dp k, +1to a supplier on

11/30, where p represents product ID and k represents time period. For this demand (Table 1), a supplier is requested to deliver materials on 12/8 and 12/10. The supplier then replied the shipping plan Sp k, +1( )i to the manufacturer, where i is the

ith day at period k+1. Meanwhile, the supplier also transfers the initial WIP 0

p

W on 11/30 to the manufacturer. We assume

0

1 20

W = andW20 =0. By using the VPCS, the manufacturer

loads the initial WIP and the shipping plan and finds that the supplier may fail to fill the order on 12/8. According to the delivery profile, this failure can be attributed to a delayed input and insufficient capacity. The detailed procedures in using the VPCS are described as follows.

According to historical inputs and outputs for products 1 and 2, the next period of the capacity H and the production loss e by product areH1,k+1=90,H2,k+1 =100,

e

and

e

Table 1. The Shipping Plan

Let next the period of IOR Rk0+1be 65%. By using the

integer programming model in the WIP programming module, the latest input Xp k, +1( )i and the latest output Yp k, +1( )i in

period k+1 are found (Table 2). Based on the historical input

,1..

p k

X and the historical outputYp,1..k, the optimal fitting of the historical distributed parameter θp,1..k was found by

minimizing statistical errors between fit and actual models using the niche genetic programming method. The next period was determined using the neural regression network to estimate the distributed parameter θˆp k, +1in the next period.

Based on the estimated parameter θˆp k,+1, a Petri Nets

based process modelPNp k, +1was constructed and provided

with the estimated outputsYˆp k, +1and the shipping planSp k,+1to

simulate the process for deriving the feasible input scheduleXp k, +1( )i , the feasible output scheduleYp k,+1( )i , and

the OFR Vˆp k, +1 in the next period (Table 3-5). Two cases

whose OFRs are less than 100%, i.e. cases Vˆ1,k+1(1)=88%

andVˆ2,k+1(2)=95%, need to be issued and improved.

11/28 12/1 12/3

1

p= _Xˆ_1,k+1(1)=90 0 _Xˆ_1,k+1(2)=84

2

p= 0 _Xˆ_{2,k+1(1)=90 X}ˆ_2,k+1(2)=50

Table 3. The Feasible Input Plan

12/8 12/10

1

p= _Yˆ_{1,k+1(1)=86 Y}ˆ_1,k+1(2)=80

2

p= _Yˆ_{2,k+1(1)=87 Y}ˆ_2,k+1(2)=49

Table 4. The Feasible Output Plan

12/8 12/10 1 p= ˆ_{1, 1(1) 88%} ( (86 20) / 120) k V + = = + 1, 1 ˆ _{(2) 100%} ( 80 / 80) k V + = = 2 p= ˆ_{2, 1(1) 100%} ( 50 / 50) k V + = = 2, 1 ˆ _{(2) 95%} ( (87 50 49) / 90) k V + = = − + Table 5. The Estimated Fill-Order Rate

Next, causes of the two cases are discussed as follows: A. Vˆ1,k+1(1)=88%case:

As the sum of capacity H1,k+1 =90 and initial WIP 0

1 20

W = are less than the shipping plan S1,k+1(1) 120= ,

supplier capacity is insufficient, and the additional capacity for the supplier is 11 (=(120−90−20)/(1−5%)).

B. Vˆ2,k+1(2)=95%case:

The capacity H2,k+1=100is sufficient for the shipping

plan S2,k+1(1)=90 . However, according to the feasible

input Xˆ2,k+1(2)=50 , total input plus production loss is

2, 1 2, 1 2,*

ˆ ˆ

(X k+ (1)+X k+ (2)) (1× −e ) 136= , which is less than the

accumulating shipping plan of product 2

2,k 1(1)

S + +S2,k+1(2)=140 by 2 days. Hence, the input must be

added 4 (= (140−136)/(1−3%)) parts in the period k+1. Besides, although feasible input time Xˆ1,k+1(1)=90was on 11/28, the current time was assumed 11/30, implying that the supplier could not produce product 1 within a normal cycle time derived from history. Hence, to fulfill the order, the manufacturer should request that the supplier prioritize production to shorten the needed processing time for producing product 1.

III. CASE STUDY OF THE VPCS

A case study of the TFT-LCD IC driver IC industry demonstrates the applicability of the proposed VPCS, whose application architecture is shown in Fig. 4. During communication between a manufacturer, i.e. IC design house, and a supplier, i.e. IC packaging and testing factory, the VPCS adopts RosettaNet protocol as the communication standard, in which material demands, shipping demands, and transaction-based WIP information are defined and complied with RosettaNet PIP 4A4, 4A5, and 7B1, respectively.

12/8 (i=1) 12/10 (i=2) 1 p= _{1, 1} 1, 1 (1) 90 (1) 86 k k X Y + + = = 1, 1 1, 1 (2) 84 (2) 80 k k X Y + + = = 2 p= _{2, 1} 2, 1 (1) 90 (1) 87 k k X Y + + = = _{2, 1} 2, 1 (2) 50 (2) 49 k k X Y + + = =

Table 2. The Latest Inputs and Outputs

The VPCS uses a production planning integration server (PPIS) as an information bridge between a manufacturer and a supplier based on the RosettaNet information framework.

Through the PPIS, shipping plans and WIP transactions can be collected from a supplier. In the VPCS, the data preprocessing module qualifies the collected data. The qualified information then is transferred as inputs to the WIP monitoring module and the output estimation module through the VPCS interface. Similarly, output of modules is shared among modules through this interface. Finally, the decision integration module analyzes the issued data quality which is identified by the data preprocessing module, and conflicting information is exported as the delivery profile, inventory profile, and data quality report.

In this case study, the IC vendor supplies the manufacturer, an IC design house, with 20 products and sent the transaction-based WIP and the shipping plan to the manufacturer four times daily for 3 months. Based on use of the initial inventory of the last month data, 30 days of production were simulated with nearly 100% OFR. From the decision integration module, our results indicated the material slack time, where slack time equals the difference between the latest input time and current time. If a slack time is negative, it implies that the material should be specifically controlled and need to prioritize its production to shorten the required processing time. In Figure 5, the material ID *CN1500 indicates that its slack time is negative representing a situation in which its normal production causes a delay in delivery.

Simulation results of using the VPCS for 30 days

indicated the reduction effect of the inventory level. In Figure 6, the red line represents the actual inventory level for 20 products, the green line denotes 3 days of buffering with ideal processing time, and the blue line refers to 3 days buffering with the fitted processing time. While maintaining the 100% OFR condition, the original inventory level is reduced by 13.8% in terms of the VPCS estimates.

IV. CONCLUSIONS

The case study results illustrate that the proposed VPCS (virtual production control system) allows a manufacturer to check the progress and feasibility risks of the supplier production, and supports the manufacturer with issued risks to ask the supplier for solving in time to ensure the demand fulfillment. Moreover, the VPCS is able to simulate and evaluate the actual supplier production capabilities, and it supports the supplier to reduce the WIP levels using a feasible production plan, which is planned to cover production variations. This proposed system is especially suitable for users to control their outsourcing production. For example, since a fabless is suffered from weak outsourcing control of IC manufacturing, using the proposed VPCS would assist the fabless users to effectively reduce inventory levels with demand fulfillment.

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Fig. 5. The Slack Time Analysis