Abstract

Supply chain efficiency and cost optimization have climbed up corporate agendas as the key levers for improving net income.

Consequently, organizations across industries are transforming their business models and product offerings. In the same vein, players in agriculture are discovering ways to create value with digitalized integrated business planning (IBP). However, the complexities and constraints across the agriculture supply chain are enormous.

This paper identifies the nuances of such agriculture supply chains and how IBP can improve financial and other key performance indicators (KPIs) through a neural approach. IBP can orchestrate simulations and optimizations to transform businesses, leading to better planning and, ultimately, a highly responsive, resilient, and flexible supply chain. Against the backdrop of a life-changing pandemic and the resulting emphasis on building neural operating models and networks, agribusiness companies can gain an edge in this competitive sector with an intelligent and insights-driven supply chain embedded with IBP.

Complexities of the agriculture supply chain

For an industry that has multiple functions interacting with different and potentially conflicting objectives, the agriculture supply chain is riddled with complexities.

A case in point is the oilseed supply chain, which is characterized by a complicated inbound and outbound network. Starting with the origination, shipping, and processing of oilseeds—from crush facilities and processing plants to fulfilling customer demand across different products used in food, animal feed, fuels, and industrial products—the supply chain comprises numerous challenging entities. Besides, social and environmental concerns, along with stringent regulations and policies, further intensify such an intricate network. These concerted challenges make planning and decision-making difficult at every step in the supply chain—from farmers to silos, from silos to processing plants, and from processing plants to customers.

Figure 1 shows the trade flows of oilseeds globally, clearly depicting the major exporting and importing countries. The number of flows possible at every step of the supply chain results in a fragmented network, giving rise to thousands of possible outcomes, where each outcome has a certain impact. This uncertainty can be attributed to two reasons—operational factors, such as unpredictable yields, along with external factors, comprising meteorological conditions, farmer capabilities and inputs, and pricing volatility emanating from global imbalance in supply and demand.

Companies in this business require access to far-reaching networks and the logistics knowledge and expertise that go along with it. However, the siloed operating models they have in place lead to hindered visibility, and lack of collaboration and accountability. What infrastructure is required for making and distributing a specific oil blend? What should be bought or built to use capacity to the best extent? To what degree should the seeds be crushed to maintain profitability? How much and where should crude inventory be positioned in the supply chain? These are some pressing questions that such firms can answer with supply chain models or optimization and simulation tools.

Embedding S&OP process in neural supply chain networks

Many companies in the agribusiness space continue to rely on inadequate solutions such as spreadsheets for planning, and decision-making is largely based on ‘guesstimation’.

In addition, while most processes are manual, integration with finance becomes challenging, leaving no way to measure the financial impact accurately. To build a robust and integrated business planning process, neither spreadsheets nor past experience can provide enough fuel. Moreover, depending on the knowledge of the decision-maker is a serious governance issue.

A well-executed sales and operations planning (S&OP) process can go a long way in helping companies manage supply chain performance and build the future agribusiness enterprise. To demonstrate adaptability, resilience, and purpose-driven behaviors, the supply chain model must encapsulate intelligent decision-making capabilities. We believe that the supply chain of the future will be driven by traits under Neural Manufacturing™, which helps supply chain networks and ecosystems become connected, cognitive, and collaborative. These neural traits drive agility and exponential growth, providing agribusinesses an intelligent edge. These capabilities help drive the following key decisions to manage supply chain performance: 

• Demand planning: Companies need to strategize an action plan to improve forecast accuracy, remove bias, and close gaps for achieving fiscal targets. They must determine volatility risks and alternative demand scenarios, along with identifying key forecast drivers.
• Supply planning: They need to arrive at a decision with regard to alternate supply scenarios and meet demand—potential upcoming demand—and supply imbalances. Key decision-making in supply planning also focuses on capacity utilization planning to meet target customer service levels.
• Sales and operations execution (S&OE): Organizations must reach a deeper understanding of material supply risk that may result in stockout and delays. Demand changes that may result in potential imbalances and risks to inventory levels are some of the significant elements driving key decisions in S&OE. Furthermore, it also includes prioritizing customers, locations, or commodities.
• S&OP: Decisions that rely on risk mitigation plans can help agribusiness firms respond to alternative demand and supply scenarios. Companies need to puzzle out financial and operational KPIs and targets, along with driving reconciliation decisions.

The entire ecosystem must be connected and cognitive, and this can be achieved by redefining operations, services, and customer experiences.