ModelEnterprise case: supply chain optimization

Optimizing cash flow for agrochemicals production

Fig. 1 – Supply chain – click to enlarge

Supply chain integration schematic – click to enlarge

Fig. 2 – Supply chain integration strategy

Resource-Task Network (RTN) for financial optimization – click to enlarge

Fig. 3 – Resource-Task Network (RTN) for financial optimization

On-Time-In-Full (OTIF) demand fulfilment – click to enlarge

Fig. 4 – Distribution model – cash accumulation for two different levels of responsiveness

AI manufacturing model for cash flow with and without investment – click to enlarge

Fig. 5 – AI manufacturing model – cash accumulation with and without investment in new assets

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.

Following delivery of a campaign planning tool for "Site S" (see Case: Investment validation), PSE was asked to optimise the existing supply chain of a group of 8 products sharing the same active ingredient (AI).

Optimisation had to take into account the entire operation from raw materials provision down to the distribution of final products (formulated, packed and labelled) to final customers.

Operations

A schematic representation of the supply chain is displayed in Fig. 1. It starts with the provision of raw materials which then are used in the production of the AI in several manufacturing steps in "Site S".

From here the AI is distributed to all internal customers, including formulation site "S". At this location several formulations of the AI, each with different labels, are produced to supply European markets.

Final customers are key accounts in four countries. Shipments to final customers are done either directly from the formulation site or through the distribution centres in each country.

Objectives

The objectives for the project were to:

determine an optimal 3 year plan for the entire supply chain while guaranteeing fulfilment of 100% of the orders placed by final customers
investigate the impact of increasing demand levels in resource utilisation and inventory levels throughout the system
assess the need to invest in dedicated manufacturing assets
optimise (minimise) working capital costs and time-to-serve.

PSE approach

The whole supply chain is decoupled in two sub-networks corresponding to two different models (Fig. 2):

Distribution model, comprising formulation at "Site S" and distribution to final customers
AI manufacturing model.

The distribution model, driven by the schedule of orders placed by final customers, generates the optimised campaign planning at "Site S".

This is translated into a set of AI orders which, in addition to orders placed by other internal customers, will work as input to the AI manufacturing model.

If the AI manufacturing model is feasible, then the process terminates here; otherwise the internal orders are slightly relaxed and the AI manufacturing model is run again. The new campaign planning at "Site S" is fixed in the distribution model which is rerun using a certain degree of order relaxation if necessary.

Financial modelling

Financial "tasks" were also included in the models using the Resource-Task Network (RTN) approach in order to optimise working capital costs (Fig. 3).

These included

Cash generation
Cash expenditure
Cash investment
Cash borrowing
Interest revenues generation
Interest costs generation.

Cash flow results are shown in Figures 4 and 5.

Benefits

Assessment of the responsiveness costs (Fig. 4)
Minimisation of the inventory levels throughout the supply chain without compromising order fulfilment and with very positive impacts in the working capital costs
Confirmation of the need to invest in a new manufacturing plant for intermediary 2 to face increasing demand levels (Fig. 5).