PRODUCTION PLANNING Planning Hierarchy

One of the main difficulties in the compiling of production plans is the uncertainty of the prediction process, which in-creases with increase of the planning horizon. To overcome partly this difficulty, the planning phase is divided into several stages, each with a progressively shorter time hori-zon, thus forming a planning hierarchy. The main underlying characteristic of this hierarchy is to take into account avail-able data in time to consider several allocative strategies, to update plans on a time scale which reflects both the need to take decisions and the availability of further or more accurate data, to transform plans into working schedules as

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-orders are received and to instruct the plant processes accord-ingly.

The time scales of the different stages of the hierarchy are estimated, considering not only the timing of decision-making but also the time required to fulfill these decisions (e.g.

order raw material and semi-finished products, equipment pre-paration, labour training, and so on). The planning hierarchies used by different steel producers are very similar, although the level of detail varies between different systems.

There are three main points of divergency among eXisting systems:

a) the so-called medium term planning is founq only in companies who prepare less formal IO-year plans and can be viewed as a refined, more detailed version of the closest 5 years in a IO-year plan.

b) The transition between an annual and a monthly product-ion plan tends, in practice, to be gradual rather than a step function. The points in time at which companies make a formal restatement of the plan varies.

c) The critical process when transforming an allocated production plan into individual process schedules is often considered to be the steelmaking process. Prior processes like the blast furnace tend to work steadily at rates only occasionally changed. The basic steel-making schedule is usually based on some fraction of a month, ranging from 7 to 14 days, with some plants preferring 10 days. The latter is a neater subdivision but, otherwise, no overwhelming evidence was found in favour of a specific period.

Table 3.1 gives the main planning time table and time scales.

On the annual repetition frequency, the activities are perform~

ed in the sequence: long-term, medium-term and annual planning so as to allow each successive planning activity to be a

develop-Table3.1.PLANNINGHIERARCHY FrequencyPeriodCovered Adhocvarious Annual10-15years Annual5years AnnualIyear Monthly6,3,andImonth Activity Businessstrategy Long-termplanning Medium-termplanning Annualplanning Productionplanning

Result Basicguidelines Plantdevelopmentplans,long-term contracts,financingplans Refinedplantdevelopmentplans, marketingstrategiesandbudgets Detailedoperatingplanandbudget Increasinglyfirmproductionplans Dailyupto3monthsOrderentry,allocat- iontoworksWorksorderfile\0 \..N Dailyupto14daysProcessscheduling (dependson process) Realtimeupto24hoursOperationscontrol RealtimeuptoafewhoursProcesscontrol (dependson process) AdhocinImmediateRe-scheduling realtime

Sequenceofprocessingin~tructions Issueofinstructions,tracking andreceiptoffeedback Directcontrolofprocessing Recoveryfromerrors

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-ment and refine-ment of the previous one. Thus, the planning process as described is essentially feed fonlard in nature.

There are feedbacks, however, which derive from actual plant capabilities, operating rates, efficiencies, costs, etc. which might quantitatively alter possible production plans. These feedbacks are also manifest through updating of the planning models.

The step by step feed forward approach continues until the point when actual orders arrive. These represent the first real feedback by which the prediction process can be judged, and the plans over time scales of up to one year may well be

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adjusted in the light of reality.

When actual orders are received, the production assignment is estimated in detail and the preparation procedure for compiling the plan, called "order processing", is carried out. The aim of this procedure is to estimate the technolog-ical routes and instructions for the material processing to meet the order requirements (with maximum efficiency) and to perform the necessary production operations.

The second aim of the "order processing" is an estimation of the times required for the orders to be fulfilled through each stage of the technological route. Comparing these times with the equipment availability times, the "plant loading"

is performed, which is the basis of the production scheduling.

If the manufacturing time required on one of the stages exceeds the available working time, some of the orders should be rejected

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-from the assignment or special measures should be taken (for instance, to postpon'e equipment repairs, to order semi-finished products from other plants, to prolong the work shift, etc.).

The "plant loading" procedure is achieved by means of the production stages models, which gives the time required by different production stages to manufacture different types of product. The models include allowances for the possibility of non-coordination of separate production operations. The nature of these allowances depends on the scope of the operations considered by the model and on the controllability of the production processes. Thus, by improving the control, the models can reflect these processes more precisely, and theI

plan compiled by them will be more effective.

A detailed model can perform plant loading with less probabil-ity of occurrence of non-coordinated operations and, in ef-fect, the scheduling is reduced to a "job-shop" problem. But increasing the detail of the model makes the problem solution more complicated. Since the presence of disturbances (unpre-dicted by the model) causes this precise sequence to deviate very quickly from the actual process situation, the degree of detail of the model should be selected considering the plant loading time horizon.

There are other factDrs that depend on the planning time horizon. Specifically, if the scheduling is treated as an optimization problem (e.g. to maximize mill productivity or process efficiency, to minimize mean inventory levels of

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-finished products, etc.), increasing the time horizon tends to increase the degree of optimality of the solution. For example, maximizing performance over one 'shift may lead to a shortage of resources available for the following shift with a consequent reduction in the overall performance of the process for the two shifts. There is a tradeoff, however, due to the increased

divergency between the real and the scheduled process as the horizon increases.

To partly overcome this conflict, several levels of the

pro-duction scheduling with different time horizons are used, analog-ously to the planning hierarchy. The production plan is the

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assignment for the given time duration (year, quarter, month, week, day, etc.) and the production schedule is the ~ivision of this assignment into the shorter time intervals. For instance, the monthly plan being divided into weekly intervals provides the basis for a monthly production schedule, the daily plan, being subdivided into shifts and process cycle periods provides the basis for the daily production schedule. With each decrease of the time horizon, a more detailed model is used, thereby, permitting more precise schedules to be obtained.

In addition, the sliding time horizon is used in many schedul-ing applications (as in the planning process): the scheduling is carried out over a rather long time horizon but when the initial segment interval has passed, the rescheduling is re-peated with the same time horizon, but based on the new inform-ation obtained up to this moment. This method permits some 1n-crease in optimality of the production schedules under the

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-condition of uncertainty concerning future process deflections.

In the case where there are several technological routes that can satisfy the orders requirements (for instance, steelmaking can be performed either in the op~n hearth furnace or in the oxygen converter; bars can be produced by merchant mills of different types), an optimization problem of the assignment's allocation among these alternative routes can be formulated.