Modern optimization methods of production planning for small businesses in the post-crisis period

Ing. Ekaterina Sutormina

Brno University of Technology, Kolejní,4,Brno, Czech Republic, sutormina@fbm.vutbr.cz

AbstractThis article will be provided with characteristic features for small businesses running order in the post crisis period. Will then be given the basic requirements for supply planning and production, based on the characteristics. As a solution to contemporary problems of planning are invited to use heuristic factory planning algorithm (HFPA), Build-to-order- Supply Chain Management (BTO-SCM), Advanced Planning & Scheduling (APS).

Key wordsHFPA, BTO-SCM, APS, factory planning, SCM

Introduction and research objectives

Along with obvious benefits for small businesses, such as: 1. Relative mobility on changes in the competitive environment 2. High speed of adaptation to the needs of the customer 3. Low levels of "freeze" of resources, the possibility of relatively rapid changes in

activity, if necessary, on the environment 4. Relative ease of analysis of internal equipment 5. A small number of items of responsibility 6. Increased transparency of processes in the organization

Recent developments in the operative management of small businesses: 1. Relatively low level of development and low prevalence of media support

because of the relatively small amount of information 2. High load currents of several areas of information 3. A high probability that the existence of chaotic, but the optimal set of

information flows 4. Low level of interest in the operational objectives and strategic objectives and

operational decisions 5. Lack of clearly established information base and technical support for procedure 6. Is not uniquely overhead (administrative) costs of special production of added

value 7. And so on. [3]

It is also noteworthy that the above-mentioned problems are concentrated in small manufacturing enterprises, enterprises operating under the order. A characteristic feature of small enterprises in this regard can be regarded as a lack of financial resources for the use of existing information tools (SCM and APS systems are integrated into a well-known ERP system). Characteristic features of post-crisis period for small and medium-sized companies:

1. Small coefficient of shifts 2. A small amount of free financial resources, weak financial stocks 3. Uncertainty in planning contracts 4. A smaller quantity of permanent contractors 5. A smaller quantity of regular customers 6. The need for higher salaries before the completion of the contract 7. Full prepayment from suppliers to manufacturers 8. High level of competition

Therefore, we can provide the basic conditions for production planning in post-crisis period:

1. Need to strengthen relationships with suppliers 2. Maximum use of enterprise resources

Used methods and research results

This part of the paper discusses the possible solutions of the research problem.

1) Despite HFPA So the article proposes a heuristic algorithm, called heuristic algorithm for production planning (HFPA). HFPA is very effective in addressing these problems and plan production usually can generate solutions close to those obtained with the MIP models but in less time. Despite its advantages, HFPA also allows unlimited preemption of a single scene, if necessary. [1] Heizer and Render (2006, p. 432) suggest that the key to effective SCM is the ability to forge long-term, strategic relationships with partners in the supply chain to "maximize the value for the end customer.

I present results support this general statement. Separate analysis of the manufacturing sector and service samples provide insight on the importance of variables, depending on the type of organization and the organization, position in the supply chain. For the manufacturing sector is too dominant element in the service sector, dominated by risk reduction. SCM stresses cooperation rather than control. Our research results have potential implications for both manufacturers and services. For example, consider a minimum of supplier-customer pair. [4]

BOSA-bottleneck-oriented scheduling algorithmTo schedule as many jobs as possible on time, HFPA must maximize the use of the most overloaded work center, called the bottleneck work center, before scheduling the jobs. Therefore, HFPA has three phases—work center sorting, job sorting and job scheduling—with a total of five steps, one in each of the first two phases and three in the third.

These steps are listed below: (P1) Sort work centers using the HFPA sorting mechanism. (P2) Group and sort jobs using the HFPA grouping and sorting mechanism. (P3) Scan the queue three times, each time scheduling some of the jobs using the bottleneck-oriented scheduling algorithm (BOSA). (P3-1) Activate BOSA to schedule jobs one by one according to the sequence determined in (P2). If a job can be scheduled in its preferred interval without being preempted, schedule the job accordingly. Otherwise, put the job back into the queue. (P3-2) After the regular scheduling, activate BOSA to schedule the jobs that were put back into the queue in (P3-1). If a job can be scheduled in its preferred interval with pre- emption, schedule the job accordingly. Otherwise, put the job back into the queue. (P3-3) After the regular scheduling and the first rescheduling, activate BOSA to schedule jobs that were put back into the queue again in (P3-2). Advance and/or delay each job in order to find sufficient work center capacity for it.In step (P1), HFPA sorts the work centers based on a two-step rule-based mechanism, which essentially finds the bottleneck work center and sorts work centers according to their loads. In step (P2), HFPA then groups and sorts jobs using a rule-based mechanism, which groups/sorts the jobs according to their relationship with the bottleneck work center; the jobs relying most on the bottleneck work center are assigned the highest priorities. At the end of step (P2), the sorted jobs are placed in a queue. In step (P3), the queue is scanned three times and the jobs are scheduled with respect to certain constraints using the BOSA.

In step (P3-1), BOSA schedules the jobs that can be scheduled in their preferred time buckets without preemption. This type of scheduling is done first because preemption results in longer cycle times and multiple setups, which waste overall work center capacity. The jobs that cannot be scheduled in their preferred time buckets without preemption are put back into the queue for later scheduling. In step (P3-2), BOSA schedules the jobs that were put back into the queue in step (P3-1), but that can be scheduled in their preferred time buckets if preemption is allowed; the total delay time is minimized, but the cycle time may be longer. The jobs that cannot be scheduled in their preferred time buckets even with preemption are again put back into the queue for later scheduling. In step (P3-3),BOSA schedules the jobs that were put back into the queue in step (P3-2) in the earliest or latest time buckets possible, again allowing preemption. The total advance time being the least important objective, BOSA delays a job only when there is no earlier time bucket available.[1]

2) Despite BTO-SC BTO-SC (Build-to-order-supply chain)-supply chain of manufacturing to order. Some of the major production decisions in BTO-SC are: production planning, scheduling and inventory. Level issues in BTO-SC Material flow decisions BTO-SC is to be viewed differently from those in the traditional supply chain. Most of these activities BTO-SC are using IT / IS and the virtual enterprise system. Therefore, control of material flow is determined by customer orders. These trigger the production of contracts for assembly line and assembly line will schedule orders to suppliers of components and parts, as well as other support services, including logistics. Some BTO-SC material flow decisions include

1. planning, 2. inventory control,3. MPS, 4. MRP / ERP, and 5. process control.

Suitable performance measures and metrics for BTO-SCM should help managers make better use of their available resources. Now the question is whether the traditional metrics can be used and what new measures / metrics will be needed for BTO-SC environments. Once these issues have been resolved, then the appropriate models using operations research and simulation techniques must be developed. Biswas and Narahari (2004) developed a decision-support system, DESSCOM (decision support for supply chains through object modeling) which enables strategic, tactical and operational decisions in supply chains. Their work is certainly a positive step towards an integrated decision-support system for SCM. Coordination in BTO-SC includes a decision regarding the anticipation, planning, material flow and management. Type of planning activity in BTO-SC includes determining requirements for components and the timing and volume of orders for suppliers. Since BTO-SC is ''pull "system or make-to-order system, the materials used is pulled through the system based on customer orders. Inventory buffer and issues arise in partnership firms, especially for components, as well as raw materials. This must be addressed with advanced planning. Obtaining the necessary resources will be the next step in the management of operations in BTO-SC. Such resources include raw materials, components and other support services. Once the production process has been initiated should be monitored in online mode using on-line information system. [2]. Simulations can be used to convert customer requirements into the design parameters of BTO-SC environments.

A method for determining the nature of the contract between customers and suppliers should be developed through optimization models, such as offices and the theory of employment contract, and linear programming models, as far as possible the benefits of both OEM and supplier companies. Simulation models must be created for this type of situation, in particular, to pull type of production in BTO-SC. From BTO-SC invites customers that their orders through Internet, it is necessary to have an appropriate system for developing and maintaining effective customer relationship management (CRM). There is a lack of mathematical and simulation models to determine the optimal architecture and selection of appropriate information technology / system for BTO-SCM [2].

The following is a summary of the main findings of this research for modeling and analysis of BTO-SC:

1. Only limited analytical and simulation models are available to deal with the problems specific to BTO-SC.

2. The existing models for traditional supply chain environment can be adapted for BTO-SC, provided that all points of the chain are taken into account.

3. Limited mathematical and simulation models are available for modeling and solving problems at the level of coordination-problems in BTO-SC.

4. In view of the architecture of networks and integrated business system characteristics of BTO-SC, simulation models, finds an important role in modeling BT-SC.

5. General inventory and distribution models, it appears that further applications in BTO-SC, provided that appropriate criteria are considered in their optimization [2].

3) Despite APS APS is a new revolutionary step in enterprise and inter-enterprise planning. It is revolutionary, due to the technology and because APS utilizes planning and scheduling techniques that consider a wide range of constraints to produce an optimized plan:

• Material availability• Machine and labor capacity• Customer service level requirements (due dates)• Inventory safety stock levels • Cost• Distribution requirements• Sequencing for set-up efficiency

This paper also discusses the basic functionality of planning and scheduling in Advanced Planning and Scheduling systems (APS). Three basic planning options - concurrent planning (or unconstrained planning), constrained planning and optimization - are analyzed. The planning functionality is radically improved compared to MRP and MRP II. APS is relevant for production-organizations. Also distribution-organizations can benefit from implementing APS for supply chain management. The key success factors, which are necessary to implement an APS system successfully, are as follows:

• Supply chain management concept• Experience• Nervousness• Human factor• Complexity• Financial resources• Data accuracy

An APS system can function in a number of environments and types of complexity. When companies start to integrate within their organization an APS tool can be helpful, because the MPS-MRP-CRP planning process can take place simultaneously. An APS tool really benefits companies integrating with outside organizations. The customer and suppliers are involved in driving the organization’s logistical chain. Logistical planning and sales are merging in order to be able to respond rapidly to market requirements. The APS tool can be helpful in dynamic environments, because it has the advantage of being really fast in recalculating the plans whenever necessary. Another benefit of this system is that it facilitates the combination of information of multiple sites and that it calculates an optimal plan for a complete supply chain[4].

ConclusionsUsing these algorithms will greatly assist in the planning of production "in order" in the post crisis period. Active collaboration with suppliers, on-line supervision of the execution of each order (BTO-SCM) aimed at streamlining the planning of supply for each order. APS system provides users a convenient way to planned analysis of information - various charts and diagrams (such as an interactive Gantt charts, graphics, machine load, etc.) And large built-rate report (which you can customize the user) in the form of reports from users. APS system can integrate with external applications of buildings, such as Crystal Reports. APS system can be integrated with existing inflationary center of the company (ERP-MES-APS). APS helps the community improved access to supply chain management. Using HFPA assists planning optimal production schedule.

References

Periodicals1. Ling-Chieh Kung,Ching-ChinChern,2009 Heuristic faktory planning algorithm

for advanced planning and scheduling Computers & Operations Research 36 (2009) 2513 -- 2530

2. Angappa Gunasekaran a,*, Eric W.T. Ngai, 2009 Invited ReviewModeling and analysis of build-to-order supply chains European Journal of Operational Research 195 (2009) 319–334 27

Conferences, symposia3. Sutormina Ekaterina, 2009 OPTIMIZATION WORK WITH SMALL

BUSINESS EMPLOYED KAIZEN PHILOSOPHY AND ELEMENTS OF APS AND SCM PLANNING. Brno University of Technology, Faculty of Business and Management, International PhD Conference, Brno, 27th May 2009

Books4. Marjolein van Eck, 2003 Advanced Planning and Scheduling Is logistics

everything? A research on the use(fulness) of advanced planning and scheduling systems. BWI paper, April Vrije Universiteit Amsterdam Faculty of Sciences Mathematics and Computer science departments Paper for Business mathematics and Informatics De Boelelaan 1801a1081 HV Amsterdam