ModelEnterprise case: Investment validation

Agrochemicals facilities capacity planning and design validation

Fig. 1 – Production facilities (click to enlarge)

Relationship between campaign planning and detailed scheduling – click to enlarge

Fig. 2 – Relationship between campaign planning and detailed scheduling

Gantt chart: Production scheduling and capacity assessment for a single product – click to enlarge

Fig. 3 – Production scheduling and capacity assessment for a single product

Monthly capacity and demand volumes – click to enlarge

Fig. 4 – Monthly capacity and demand volumes

Fig. 5 – Monthly capacity and demand volumes with reduced processing capacity

Gantt chart: Higher utilisation and longer campaigns – click to enlarge

Fig. 6 – Higher utilisation and longer campaigns result in higher stock levels but ensure satisfaction of orders

The client for this study project was one of the largest agrochemicals producers in the world, with a turnover of more than $8bn and presence in nearly 100 countries.

"Product Family F" Engineering Group requested an independent study and quantitative analysis of "Site S" to establish the maximum capacity available and an optimised production plan.

The study had to validate the proposed investment strategy for a new formulation line and determine whether the production plan could incorporate an additional 19 products of "Family F" without any further investment in plant equipment.

In addition, it was necessary to establish whether the site required additional operator resource at an estimated cost of EUR 60,000 per annum.

Objectives

The study was required to deliver an optimal plan over a horizon of twelve months, accommodating the additional formulations and integrating the new production line.

In addition the operating conditions of the proposed new line for the total of 38 products of "Family F" needed to be determined, as well as manning levels / resources.

The objectives for the study were set as:

define the capacity and manning level of the new production line for each product
identify whether the use of both lines at "Site S" will satisfy the expected demand levels
deliver a plan that takes into account demand profile, cycle times for each product, peak production demand, change-over and clean downs.

PSE approach

In order to evaluate production capacity of the new line for each product of "Family F", a detailed model of the operations required to produce each final product had to be generated.

This involved 38 models in total using a time discretization of one hour.

The models were built in ModelEnterprise based on information provided by "Site S", incorporating specific assumptions agreed with the engineering team in advance, to model the process shown in Fig. 1.

The 38 lower-level models assessed the maximum production capacity of each product of "Family F" in terms of finished (packed) product. The derived results were then used as inputs to the planning model in order to determine the capability of the plant to meet the required annual demand profile (Fig. 2).

The model developed for the Production Plan did not require the degree of detail of the lower-level models generated as it had to cover a longer time horizon (more than one year). The main purposes of this higher level model were to identify the campaign plan – i.e. the total number of campaigns run to meet demand – and minimise total cost.

The plant performance is measured in terms of the ability to satisfy customer demand (as forecasted) and respond to orders. This is the primary key performance indicator / metric for plant managers and site personnel. Thus, meeting all orders was a fixed objective (or hard constraint) for optimisation (Fig. 3).

Benefits

From this study, the following could be deduced:

that the current configuration is sufficient to cover the demand profile as it has been forecasted (Fig.4)
the plant can accommodate further demand if the low utilisation of line 1 and the limited availability of line 2 during the last months of the horizon are sorted out (Fig. 5)
higher utilisation and longer campaigns will result in higher stock levels but will ensure satisfaction of orders (Fig. 6).