Predictive planning with neural networks.

Critical for successful operations of service industries, such as telecoms, utility companies and logistic companies, is the service chain planning process. This involves optimizing resources against expected demand to maximize the utilization and minimize the wastage, which in turn maximizes revenue whilst minimizing the cost. This is increasingly involving the automation of the planning process. However, due to unforeseen factors, the calculated optimal allocation of resources to complete tasks often does not match up with what is actually occurring on the day. This factor highlights a requirement for a method of predicting accurately the number of tasks that will be completed given a known amount of resources and demand in order to produce a more accurate plan

Predicting service levels using neural networks.

In this paper we present a method to predict service levels in utility companies, giving them advanced visibility of expected service outcomes and helping them to ensure adherence to service level agreements made to their clients. Service level adherence is one of the key targets during the service chain planning process in service industries, such as telecoms or utility companies. These specify a time limit for successful completion of a certain percentage of tasks on that service level agreement. With the increasing use of automation within the planning process, the requirement for a method to evaluate the current plan decisions effects on service level outcomes has surfaced. We build neural network models to predict using the current state of the capacity plan, investigating the accuracy when predicting both daily and weekly service level outcomes. It is shown that the models produce a high accuracy, particularly in the weekly view. This provides a solution that can be used to both improve the current planning process and also as an evaluator in an automated planning process

Methods of modeling the demand for resources in the logistics sector

PURPOSE: Within the framework of this article, the authors review and analyze selected methods of modelling the demand for resources in the logistics industry and application of these methods as a tool to improve efficiency of logistics entities.DESIGN/METHODOLOGY/APPROACH: The general part contains a definition of the logistics sector and its basic characteristics, as well as issues related to determining demand for resources in the broader context of organization management. The detailed part discusses selected methods of modelling the demand for resources, presenting the context of their application and development perspectives of theoretical models for estimating demand for resources.FINDINGS: According to the principle of rational management, enterprises strive for the best possible use of their resources. One of the means to achieve this goal is the proper determination of demand for these resources, resulting from the current scale of operations and expected changes in this area. In the logistics sector, which is an increasingly important element of trade, and in many countries one of the key areas of the economy, the right selection of resources affects both achieved financial results and operational possibilities of providing services.PRACTICAL IMPLICATIONS: Results of the review indicate that correct determination of the demand for resources is one of the key factors determining development of a market advantage by individual participants in the exchange of goods.ORIGINALITY/VALUE: From the point of view of implementation of long-term goals of logistics companies, strategic analyzes are of key significance, however, short-term analyzes are also important, especially in ensuring current operational efficiency. Regardless of the time perspective, planning models that limit the risk of inappropriate adjustment of resources to the scale of operations, turn out to be useful.peer-reviewe

Bi-level optimisation and machine learning in the management of large service-oriented field workforces.

The tactical planning problem for members of the service industry with large multi-skilled workforces is an important process that is often underlooked. It sits between the operational plan - which involves the actual allocation of members of the workforce to tasks - and the strategic plan where long term visions are set. An accurate tactical plan can have great benefits to service organisations and this is something we demonstrate in this work. Sitting where it does, it is made up of a mix of forecast and actual data, which can make effectively solving the problem difficult. In members of the service industry with large multi-skilled workforces it can often become a very large problem very quickly, as the number of decisions scale quickly with the number of elements within the plan. In this study, we first update and define the tactical planning problem to fit the process currently undertaken manually in practice. We then identify properties within the problem that identify it as a new candidate for the application of bi-level optimisation techniques. The tactical plan is defined in the context of a pair of leader-follower linked sub-models, which we show to be solvable to produce automated solutions to the tactical plan. We further identify the need for the use of machine learning techniques to effectively find solutions in practical applications, where limited detail is available in the data due to its forecast nature. We develop neural network models to solve this issue and show that they provide more accurate results than the current planners. Finally, we utilise them as a surrogate for the follower in the bi-level framework to provide real world applicable solutions to the tactical planning problem. The models developed in this work have already begun to be deployed in practice and are providing significant impact. This is along with identifying a new application area for bi-level modelling techniques