Value chain management for commodities: a case study from the chemical industry

Abstract

We present a planning model for chemical commodities related to an industry case. Commodities are standard chemicals characterized by sales and supply volatility in volume and value. Increasing and volatile prices of crude oil-dependent raw materials require coordination of sales and supply decisions by volume and value throughout the value chain to ensure profitability. Contract and spot demand differentiation with volatile and uncertain spot prices, spot sales quantity flexibility, spot sales price-quantity functions and variable raw material consumption rates in production are problem specifics to be considered. Existing chemical industry planning models are limited to production and distribution decisions to minimize costs or makespan. Demand-oriented models focus on uncertainty in demand quantities not in prices. We develop an integrated model to optimize profit by coordinating sales quantity, price

Connectedness and risk spillover in China's commodity futures sectors

“…Chemical is still the most central node most of the time (83.65%), while Non.Metal and CCSM are occasionally central nodes (6.69% and 9.58%, respectively). China is a large manufacturing country, and Chemical, which is the byproducts of oil and natural gas that provide intermediate goods for industrial manufacturing (Kannegiesser et al, 2009), plays an extremely important role in the economy. It is therefore conceivable that Chemical will occupy a central position in the commodity market for a long time.…”

Verbund‐Simulation – Strategic Planning and Optimization of Integrated Production Networks

“…From the Verbund-Model perspective, such ERP-based approaches meet requirements of usability and specific user roles but face difficulties when it comes to complex, nonlinear, and strategic planning problems. Several other authors model planning problems using linear and nonlinear programming in combination with further approaches such as model predictive control for production planning under uncertainties [8], planning of multiproduct systems [9], value chain modeling under demand uncertainties [10], interlinking operative and strategic production [4,11], or enterprise-wide optimization [12]. All these models allow for mathematical optimization but require a highly sophisticated understanding of mathematical modeling and do neither support an iterative and intuitive development of a VerbundModel by several users nor comprehensive visualization of the model and its results.…”

Enhanced Procurement and Production Strategies for Chemical Plants: Utilizing Real-Time Financial Data and Advanced Algorithms

“…The study found that financial hedging strategy ties closely to, and can have both quantitative and qualitative impact on, the firm’s operational strategy. The publication by Kannegiesser et al presents a planning model for coordinating sales and supply decisions for commodities in a chemical industry. The impact of elasticities, variable raw material consumption rates, and price uncertainties on planned profit and volumes are demonstrated.…”