Information Coordination in Supply Chain Systems

Information involved in literature regarding production planning and inventory control is mostly order and inventory indicated that information about th e state of order and the point of Sale are also should be shared. Some researches focus on the information on distributing project scheduling. The distributed project scheduling problem (DPSP) is concerned with configuration and scheduling of multiple projects in a network of enterprises which consist of project managers and contractors. Lau et al. emphasized how to improve the convergence and quality of the solution by taking advantage of inter-enterprise information sharing especially the sharing of schedule flexibility information.

Huang et al. presented a model of production information, including six categories of production information that are often enco untered in the analysis of information sharing. The six categories are product, process, resource, inventory, order and planning.

When businesses in a supply chain focus upon the end-user there are many metrics that can be considered. However, they may be aggregated as Service, Quality, Cost and Lead-time.

There has been a considerable amount of literature on information’s impact on cost and service. Angulo et al. indicated that forecasting information sharing between retailer and supplier can notably increase the ratio of order-fulfillment in the situation of non-stable demand. Gerard and Marshall studied the value of sharing demand and inventory data in a model with one supplier, N identical retailer, and stationary stochastic consumer demand. They found that implementing information sharing can reduce cost (consisting of inventory holding cost and back-order penalty cost) up to 13.8%.

Since Forrester discovered the fluctuation and amplification of demand from downstream to upstream of the supply chain, a large amount of literature analyzed this phenomenon. This phenomenon is also well known as bullwhip effect. Several causes have been proposed to explain the appearance of the bullwhip effect, such as demand signal processing which uses forecasting methods not perfectly accurate, gaming among companies when demand exceeds supply, order batching which discretizes orders, and price variations which incite clients to over-order when price is low. The consequences of bullwhip effect include higher inventory levels, supply chain agility reduction, decrease of customer service levels, ineffective transportation and missed production schedules. Lee et al. pointed out that sharing sale data and inventory information can reduce this effect. Chen et al. quantified this effect for simple, two-stage supply chains consisting of a single retailer and a single manufacturer. They extended the results to multiple-stage supply chains with and without centralized customer demand information and demonstrated that the bullwhip effect can be reduced, but not completely eliminated, by centralizing demand information.

The policy of information sharing includes essentially information centralization, Vendor Managed Inventory (VMI), and Collaborative Planning Forecasting and Replenishment (CPFR). The content of information and the way to share them in the policy is different. Information centralization is the most basic policy of information sharing in which retailers broadcast the market consumption (approximated as their sales) to the rest of the supply chain to reduce bullwhip effect. VMI means that upstream suppliers manage and control downstream inventories based on their production and inventory information. CPFR shares more information (e.g. history sales data and forecast information) than only demand information. This allows the participants to coordinate joint forecasts by focusing on differences in forecasts. They also jointly define plans to follow when specific contingencies occur.