Scheduling production to match sales forecasts

By |  January 19, 2016
Photo: iStockphoto.com/vm

Photo: iStockphoto.com/vm

All aggregate producers must plan production to some degree. But how thorough are their methods, and how accurate are their plans? Because different plants can make products in different combinations and at different rates, operations need to have a plan to match production to their sales volumes.

The goal, of course, is to schedule production based on a sales forecast – so as to meet sales demands within scheduled hours. In order to make a profit, producers must reduce costs, waste, byproducts and unsalable inventory, while they produce the best mix of all salable products – and sell what they produce.

Producers use a range of production scheduling practices.

Some producers schedule production based on the best-guess approach, and then see if there are profits down the road. With this style of scheduling, inventory levels become a problem that lead to re-handling costs. There is contamination risk in handling excess inventory, and producers often experience shortfalls of certain products. The result is that the operation is ultimately driven by daily sales demands – forcing the operation to run extra hours of overtime to meet those demands.

Other producers will tour their plants and visually size up the stockpiles. If a pile is low, they switch to a different operating mode to produce more; if a pile is high, they cut back on production of that product. This is a short-term, reactive approach that doesn’t consider future sales fluctuations or allow for other activities, such as plant maintenance.
A better, but by no means ideal production scheduling method often used involves the use of Microsoft Excel spreadsheets, where the producer enters possible production schedules against a sales forecast. Using formulas in the spreadsheet, if a cell turns red, the plant has not made enough of that product and inventory shortfalls are expected.

The user must repeatedly enter different production hours until the amount of red cells is minimized. These spreadsheet methods do not result in the optimum solution of production scheduling to a sales forecast.

Hurdles to accurate scheduling

Operating a plant can be complicated. Plant managers must decide which operating mode to use in running the plant, what combination of products to make, what hours the plant needs to run – and more. The operation cannot just produce the highest profit-maker, as this creates byproducts that must be sold at low prices or that are turned into non-valued production.

Many producers don’t have the time or knowledge to create reliable tools to accurately schedule production for sales. And few have an accurate picture of their plant or know what it truly can do.

The difficulty can be attributed in part to the fact that few plants are simple; they do not run the same way at the same rates every day. Most plants, in fact, have a high level of complexity as to how they can be operated. There are stages in which sections of the plant can run at different rates and make different products, and within those stages, there can be several modes of operation.

Flop gates, reversing belts, screen changes or crusher-setting changes can result in different product mixes at different rates. And production stockpiles can be blended together to meet required specifications for a sales product. Trying to build a spreadsheet that considers all the ways a plant can be run to meet a sales forecast can become a never-ending task.

In order to accurately schedule production to meet sales forecasts, producers need benchmarks that include availability, utilization and efficiency within the plant. Additionally, they must know the products and blends the plant can make, inventory in their stockpiles, measured production rates by product and forecast sales by product and by month.

Automated software

Given all of these complex requirements and constraints, how can a producer hope to accurately schedule production to meet sales demands while optimizing profit? There is a way to quickly calculate the absolute best production schedule. It is referred to as linear programming – and it is a mathematical technique used in computer simulations to find the best possible solution in allocating limited resources (energy, machines, personnel, space, time, etc.) to achieve maximum profit or minimum cost.

For the past 10 years, BedRock Software, the creator of AggFlow, has worked to design a solution to the challenge of optimum production scheduling using linear programming.
Launched recently as part of AggFlow DM Manage projects, the Production Scheduler module for AggFlow DM is designed to provide a simple, reliable method for plant managers to plan production for the year.

This type of linear programming plans production based on forecasts – up to 24 months out. It determines the best combination of operating modes to schedule, and solves for sales shortfalls and inventory fluctuations starting from opening inventory amounts – ultimately balancing production to sales while minimizing production of unsalable or waste products.

How does it work?

The first step toward using such software is to build an accurate plant simulation model. An accurate model will include all equipment and settings, feed gradations, specifications for products, blended product options and benchmarks of the plant’s actual performance – along with measured rates. The model also must include the plant stages and operating modes.

In and of itself, this process flow simulation model is a benefit for producers because it helps to optimize their plant by identifying inefficiencies and bottlenecks in order to maximize production.

Software provides an easy method to enter sales forecasts matched to the production stockpiles or blends from the stockpiles. Multiple sales products can be sold from a single stockpile by using additional IDs and/or names. The user can also enter the required sales, which will be prioritized over higher revenue products, and can also enter monthly prices by sales product.

The production and inventory constraints can then be entered, using inventory minimums, maximums, cap limits and values. The user enters plant availability and schedule in hours per day and days per month, selects available modes by month and enters the previous month’s closing inventory. The user also can also enter the previous 12 months’ forecast and production to use for comparison.

At that point, when all data and constraints have been entered, the software automatically solves for the optimal schedule, with a goal of attaining the highest revenue, while meeting required sales commitments and inventory constraints. It calculates the shortfalls by month for each sales product, selects the best combination of operating modes in the available scheduled time, and shows the production by mode and stage.

The software also shows inventory trends over the sales forecast and provides results both by stage and for the entire plant. For reporting purposes, the user can export results to Microsoft Excel for each stage.

Planning for profits

Of course, generating the most revenue does not always result in the highest profit. Revenue does not take into account operating costs; it is only the value from sales of products at the sales price. Profit is sales revenue less the operating costs.

Typically, inventory is built to address future high sales demands and offset production shortages. Producers must keep in mind that inventory is actually tied-up capital; there is an operating cost to make the products that become inventory – and that cost is not recovered until the inventory is sold.

So the goal is to make right amount of inventory to reduce capital, but not lose future sales. Producers can also use this solution to take a plant to the next level by including operating costs and determine the best combination of sales that can be achieved to generate the highest profit.


Program steps

  • Step 1: Build an accurate simulation of a plant with stages, modes for each stage, blended products and measured rates.
  • Step 2: Enter the sales forecast with sales products and map products to production stockpiles and blends; enter starting inventories.
  • Step 3: The program automatically solves for the highest revenue (or profit), while meeting required sales commitments and inventory constraints.

Definitions

  • Stage: A section of a plant that can run independently of the rest of the plant. Stages are linked with surges.
  • Operating mode: Changes to a plant stage’s settings, as applied to make different products.
  • Availability: The percentage of time the plant actually operates, compared to the time the plant is scheduled to operate.
  • Utilization: The feed rate at which the plant actually operates, compared to its theoretical (calculated) rate.
  • Efficiency: The resulting rate at which the plant produces on average, compared to its best calculated rate.

Take note

The goal of the automation software is to attain the highest revenue while meeting required sales commitments and inventory constraints.


Bryan Lewis is the president and founder of BedRock Software, the creator of the AggFlow process flow simulation software. www.aggflow.com

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Allison Kral

About the Author:

Allison Kral is the former senior digital media manager for North Coast Media (NCM). She completed her undergraduate degree at Ohio University where she received a Bachelor of Science in magazine journalism from the E.W. Scripps School of Journalism. She works across a number of digital platforms, which include creating e-newsletters, writing articles and posting across social media sites. She also creates content for NCM's Portable Plants magazine, GPS World magazine and Geospatial Solutions. Her understanding of the ever-changing digital media world allows her to quickly grasp what a target audience desires and create content that is appealing and relevant for any client across any platform.

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