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Development and implementation of measures to improve environmental situation within Magnitogorsk industrial hub
References
1. Drobny O.F., Cherchintsev V.D. The specification of prevalent pollutants in the air basin at metallurgical enterprises with complete production cycle. Teoriya i tekhnologiya metallurgicheskogo proizvodstva. [Theory and technology of metallurgical production. Collected scientific papers]. Magni-
togorsk, 2003, vol. 3, pp. 216-223.
2. Estimation method of hazardous substances content in the atmosphere resulting from industrial wastes emission. All-Union regulatory document OND-86. Goskongidromet. Leningrad: Gidrometeoizdat, 1987, 92 p.
3. Glukhov V.V., Nekrasova T.P. Economical fundamentals of ecology. S-Petersburg: Special literature, 1997, 304 p.
Kolga M., De Smedt V., Van Nerom L.
PLANNING PRINCIPLES IN METALLURGY
Abstract. Metallurgy is known as one of the most challenging areas for planning. Planning systems for metallurgical production are meant to solve a wide range of problems: from day-to-day scheduling at the workshop level to the development of the company strategy for years ahead.
Each level of planning presents specific tasks, degree of abstraction and planning horizon. The modules of the planning system are responsible to find the optimal solution at each level.
A properly selected, installed and operated planning system helps to improve control over production processes and increase the profitability of the company.
Keywords: planning system, production plan, planning horizon, scheduling, material flow, bottlenecks management, strategy plan.
Introduction
In terms of industry, planning serves as the bridge between product design, production capacity and the actual production. Traditionally, planning tasks were performed manually, based on the experience of the specialists. Planning systems were designed to integrate, standardize, streamline and improve company planning, reporting and operational control capabilities.
Although the purposes and principles of the planning are generally the same, these systems vary from industry to industry and from company to company. The more complex the manufacturing, the more sophisticated planning system is required.
Metallurgy is known as one of the most challenging areas for planning. Such complexity of metallurgical production can largely be attributed to the factors as [2]:
• wide range of products (thousands of items)
• variety of routings (alternative routes between lines)
• multiple operating modes of the lines
• complex rules and constraints at the lines
• make-to-stock production and allocation of materials from the stock yard
• the capacity required is difficult to predict (since it depends on the particular mix of products)
• work-in-progress inventory is often large
• throughput times are generally long
• complex and strict quality requirements
• extremely high «cost» of the downtimes
• realtime (or «near-realtime») data exchange with the production level
• etc.
The aim of this article is to give an overview of the basic tasks which planning systems face at different levels of metallurgical production, and to present successful approaches to the problem. The latter is best done by way of example.
PSI Metals GmbH is one of the leading international companies providing IT solutions for the metals industry [1]. Planning system of PSI Metals presents a group of products which are meant to solve planning tasks at different levels.
Levels of Planning
Each level of planning varies in purpose, time span, and degree of abstraction.
At the basic workshop level, the aim of planning is optimal schedules of the production lines. The level of detail is very high; the planning horizon lies in the short-term. Given a big variety of specific rules and constraints, which are never completely independent, the conflicts are inevitable and a compromise is not easy to find.
Optimization models implemented in PSI Metals scheduling tools are based on tried-and-tested algorithms, which use the customer's priorities to range the goals and ensure the most preferable of feasible results.
Depending on the line type, the set of rules can vary greatly. For example, when talking on the continuous casting machine, one has to take into account fixed ladle batch sizes, acceptable steel grade transitions and steel grade nesting, variable geometry of the output products (slabs or billets), tundish wear and molds changes with the related stops, as well as many other factors. In case of the hot strip mill, the focus shifts to the problems like furnace-charge, width / thickness profile, run up, specific rolling rules («coffin rule»), stops for equipment readjustments and rolls changes, stockpiling, etc.
Thus, each production case requires individual approach; and this means one more challenge for planning systems - they must be «tailorable». The planning solution of PSI Metals meets this challenge due to the modular structure: a set of line-specific modules delivers the optimal results to the upper level of planning, where they are integrated and can be analyzed in the context.
The context of the higher level implies a full scope of the production lines available. The objective of the planning system is well-balanced load of the equipment with
Kolga M., De Smedt V, Van Nerom L.
respect to delivery dates of the orders. Considering alternative routes and the bottlenecks, the system must establish start and finish dates for each operation required to complete the order on time. PSI Metals planning system also takes care of successive lines synchronization to solve tasks such as hot charging, which is critical for the hot rolling. To develop a reliable schedule, the planning algorithm operates with information on routing, required and available capacity, competing jobs, and manufacturing lead times at each line involved.
The problem of materials allocation takes place at the same level. PSI Metals planning solution offers several task-specific products for assignment of orders to material. Actually this procedure deserves a separate article, because of its complexity and obvious economic effect it provides due to minimization of scrap, optimization of cutting plan, maximization of orders fulfillment, etc.
The next level deals with capacity management and material flow. The planning horizon is usually a few months; the level of detail is reduced to product groups or families. The objective is to define the target product mix per period and make the best use of manufacturing resources, while keeping inventory levels down.
The next level of planning deals with capacity management and material flow. The objective is to meet delivery dates, to make the best use of manufacturing resources, while keeping inventory levels down. It involves establishing start and finish dates for each operation required to complete the order on time.
In metallurgical production, it is very difficult to balance the available capacities of the various lines with the demand for their capacity. As a result, some lines are overloaded and some are underloaded. The overloaded lines are called bottlenecks. The bottlenecks are critical to the throughput of the whole system and usually have problems with high work-in-progress inventory level. Bottlenecks management is one of the most important advantages provided by PSI Metals planning system at this level. To develop a reliable schedule, the planning algorithm operates with information on routing, required and available capacity, competing jobs, and manufacturing lead times at each line involved.
The next in the planning hierarchy is sales and demand planning level. The planning horizon for this level is at most a year, and revised every month or quarter. The next level in the planning hierarchy deals with the sales and demand planning. The level of detail is not high; the planning horizon can be up to several months. The aim of the planning system is a plan to satisfy market demand within the resources available to the company, determine from the demand forecast, that is the best way to accept orders in order to maximize the throughput of the factory and EBIT profit of the company, reconcile the commercial demand and technical restriction of the plant. The plan is devised in terms of product groups or families.
The basic approach of PSI Metals planning solution is to find a balance between priority and capacity. It is the privilege of the customer to define the requirements and rank them in order of importance.
The result of this level must, at the same time, agree with implementing the strategic business plan. The strategic business plan is a statement of the major goals and long-term objectives of the company and this is the top level of manufacturing planning.
Strategy planning takes into account the market-, products and pricing strategies of the company, alternatives for the supply chains, and the corresponding procurement strategies.
The plan of this level is the guideline for all further decisions in planning and execution processes, and thus must be considered very carefully.
PSI Metals Planning provides the ability to model «what-if» scenarios to see the impact of various factors before making a decision. This helps a company to more thoroughly assess potential risks, more quickly identify opportunities, and more promptly react on the market changes.
Figure summarizes the preceding description of the planning hierarchy, and establishes a correspondence between the planning levels and existing solutions of PSI Metals.
Strategy V Planner Demand & Sales Planner
Planner
Order Schedjler, Matersl A ocator, Plate Comhiner, Co I Comb ner
Line Scheduler, Caster Scheduler
PSI
Planning Level Strategy Planning Capacity & Master Planning Material Planning & Order Scheduling Demand & Sales Planning Line Scheduling
Planning Levels and PSI Metals Planning Products
Advantages of Planning Systems
Depending on the scope of the planning system, the benefits can be obtained in such operational areas as manufacturing, sales and marketing, management and others [3].
In manufacturing: the planning systems provide faster identification of production process problems and bottlenecks; facilitate more accurate raw materials planning and control; improve inventory control. Besides, more thorough shop floor planning reduces waste and re-work.
In sales and marketing demand planning, the main benefits are: improved due date performance, reduced increased factory throughput and better management of supply and more flexible delivery times; improved accuracy in sales forecasts; increased accuracy in purchase orders, etc.
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Planning principles in metallurgy
In management: the planning systems help to improve the quality of decisions; faster identify and assess risks; provide more accurate and timely information. Increased structure and discipline in planning, reporting and forecasting activities improve control over production processes in general.
Conclusion
The automation of planning activities at metallurgical enterprises presents many challenges, since it involves a multitude of conflicting criteria and competing objectives
and also requires a great deal of expertise and knowledge, both of which are not easy to model and codify.
A properly selected, installed and operated planning system helps to speed up production, reduce costs and increase the profitability of the company.
References
1. www.psimetals.de.
2. Tony Arnold J.R., Chapman Stephen N., Clive Lloyd M. Introduction to materials management. 7th ed. p. cm.
3. Fawls Tom. A Business Primer for Understanding Integrated Planning, Reporting and Control Systems. www.SpinningDisc.com.
Polikarpova M.G.
FUZZY-LOGIC-BASED RISK MANAGEMENT OF M&A DEALS OUTCOME: A CASY-STUDY A LARGE RUSSIAN METALLURGIC HOLDING
Abstract. The paper researches the application of several fuzzy logic concepts to evaluating risk rating of M&A projects undertaken by a large Russian Metallurgic Holding: maxmin compression, fuzzy relationship of preferences, additive compression. The way of expert answer treatment is presented for the possibility of further fuzzy logic methods application. 20 M&A projects are used as the empirical basis for the research. The methods applied show consistency in final estimates proving the ability of their use in MA deals' risk outcomes evaluation. The proposed method can be used to evaluate risk consequences for M&A deals. Keywords: fuzzy logic, fuzzy set, membership functions, rule matrix, risk, M&A, metallurgy
Currently M&A is one of the most solicited ways of developing an industrial enterprise. Though M&A deals embed material risks they are efficient to achieve the objectives that are unattainable given other long-term development strategies.
Being highly risky M&A deals often result in losses for the acquirer. As McKinsey found in 2008 70% of M&A resulted in business value destruction. Same time Russian M&A differ in several ways from abroad ones:
■ no common regulator and no unique pricing procedure exist;
■ market is rather closed, no unified statistics available;
■ there legal blank points enabling raider activity etc.;
■ legal system drawback in corporate conflicts resolution [2].
Generally the corporate culture to dealing with M&A deal consequences is not well worked out. As the M&A deal associated risks need to be dealt with, the paper has its objective to present the way of evaluating M&A risks based on fuzzy logic concept.
M&A deal realization passes three stages of its live-cycle:
1) project integration (negotiation process);
2) company reorganization (sales-and purchase agreement execution);
3) company integration (incl. corporate cultures) [4].
The majority of M&A deals are subject to UK common law, including deals taking place in off-shore jurisdictions.
When working out methodological principles of M&A deal risks evaluation for an industrial enterprise
considering metallurgic holding as an example industry-specifics should be accounted for:
■ geographic distance of integrated companies and their corporate cultures difference;
■ high capital-intensity of metallurgy, demand for huge initial investments, long period of investment pay-back;
■ acquisition of current and developing new plants is subject to external groups of interest influence. Dealing with them is of objective necessity;
■ all large metallurgy plants in Russia provide employment for whole cities implying high social responsibility of M&A projects;
■ technological similarity needs to be accounted for when merging metallurgical companies.
The M&A process complexity is driven by a high number of involved parties. Thus different stakeholders' possible actions were analyzed to account for most of the M&A deal risks.
Figure below presents the worked out process of project gross risk evaluation. The mechanism is self-adaptive and self-regulative. As risks at each of the stages are difficult to qualify fuzzy logic is used to evaluate gross risk. The fuzzy logic permits us to treat heterogeneous factors given lack of sufficient quantitative data [5].
According to the proposed mechanism individual risks were evaluated based on expert judgments for alternative investment projects.
The research is based on expert judgments for 20 M&A deals of one of the largest Russian metallurgy enterprises. 51 risk criteria have been chosen. Nine topmanagers were questioned to obtain their expert judgments.
