Integrated paper production and Energy planning is a fresh approach to production and logistics optimisation, based on minimizing the overall enhanced schedule cost of paper production, while improving co-ordination of pulping and energy operations.

The most important variables here are:
o Direct production costs on different machines
o Cost of grade changes
o Cost of trim waste
o Cost of transporting finished product
o Cost of delayed customer orders
o Cost of warehousing (order produced too early)

Electricity market price variations play an important role in the enhanced cost model. Figure 1 shows the daily average electricity price development in Germany and Finland in 2003. At its highest, the energy price can account for up to 40% of the selling price of woodcontaining paper. Fortunately, companies secure energy supply and prices with long-term contracts: energy secured at low prices can generate profits when sold during price peaks.

Electricity Market Prices Behaviour

Market electricity prices vary according to season, particularly extremes of hot or cold. But electricity price variation is seldom in sync across regions - see Figure 1. Another dimension is cyclical patterns during a single day, illustrated in Figure 2, which offer an opportunity for short-term energy optimisation. In India, the nonavailability of power during peak hours and the opportunity to sell excess power to electricity boards can be a comparable opportunity.

New Optimization Algorithms
The key difference between the new optimization approaches and traditional point solutions is that it considers all the relevant cost contributing factors.

Multi-machine/Multi-mill Production Planning and Optimization

Typically, the first part of the new optimization toolkits available - commonly referred to as Multi-mill and Multimachine Production Planning and Optimization - takes into account the total cost of production. This includes production and energy costs on paper machines in different locations, simultaneous calculation of trim efficiency, inventory, and transportation costs. It then assigns the order to the mill which contributes the highest supply chain profit.

The solution for this global production scheduling problem is a combination of mathematical methods, including algorithms, which re-sequence orders periodically or to reflect a significant change in costs.

In situations with significant cost base differences between mills producing the same product, global profitability might best be served by switching production temporarily to a lower cost mill.

Dynamic Profitable-to-Promise (PTP) evaluations
Before accepting a new order, it is important to know that it can be fulfilled on time. Using production allocation data and available information along with the real time schedules from Multi-mill Production Planning, a commitment can be made with confidence.

In Central Europe it is common to accept orders just hours before production, yet hard to calculate their bottom line contribution. But the Profitable-To-Promise (PTP) evaluation can assess the impact of a new order on the dynamic production schedule. This complements traditional Capable-to-Promise (CTP) analysis with enhanced schedule cost information. The calculation provides a list and associated costs of possible shipping dates from each available production line.

Pulp Production Planning
An accurate energy balance forecast for pulp production is essential for accurate energy management resource planning and cost optimization.

As part of the Pulp Production Planning solution, pulp consumption is forecast from paper and board production schedules. Pulp production is then planned line by line, and forecasts for process variables made. Real-time integration of these factors within paper and board planning also provides feedback for the PTP check and Multi-machine/Multi-mill Production Planning.

Energy Management and Optimization
Energy Management and Optimization completes production chain integration. The real-time demand forecast is based on production plans for both paper and pulp, and viewed against current supply side structure and market prices. The model is then optimized to minimize energy costs.

Results and Discussion
Multi-machine/multi-mill Optimization benefits One such commercially available algorithm has been benchmarked against three paper machines with combined annual production of 400,000 tons. Real production plans and corresponding customer orders for one month were fed into the system. After this, the optimization was left to re-organize production and sequence orders (results in Figure 4). The annual savings from using the new algorithm in this case can be estimated at $ 1,980 000.

Pulp Production Planning and Energy Management benefits
Benefits of integrating paper or board production planning with energy management depend on factors such as the type of pulp, and the mill's complexity.

Our example considers energy saving potential. The test case is a 600,000 ton integrated mechanical pulp and paper mill. Accurate electricity consumption and steam production forecasting decreases energy costs, and halves the penalty fee for using settlement electricity. The standing charge related to peak electricity purchase is reduced by 2.5 MW, because better planning allows alower safety margin. Energy consumption per produced ton (SEC) of pulp is down more than 1%, as performance of key components is accurately monitored and maintenance operations better timed. The annual electricity cost savings estimate is $700,000.

Conclusion
Integrated Pulp and Paper Production and Energy Planning solutions can handle the entire chain from order fulfilment to energy contracting. These can be deployed at a single site or extended to cover multiple mills.

Fast re-scheduling offers the tools to manage orders in the most cost efficient way, provides flexibility and visibility in the supply chain, and helps reduce production costs.

Pulp Production Planning integrates paper and board production planning with energy management and provides real-time forecasts for pulp, water, chemicals, and energy balances.

Based on dynamic production plans, integrated energy management and optimization functions forecast the total real-time demand for different energy resources and optimise energy production, purchase and sales operations for the mill or the entire corporation.

The resulting solution releases additional profit potential in today's typical industrial environment.

- Simo Saynevirta