Effectiveness in strategic planning

There is a considerable body of literature on how to do strategic planning effectively. There is however no concise statement of effectiveness in strategic planning. One definition of an effective strategic planning system would be a system that sets goals and achieves them within a specified time. In this paper it is argued that this goals/achievement view of effectiveness has serious deficiencies. As an alternative to an ends oriented view of effectiveness, a process orientation is adopted. Twelve attributes of effectiveness for strategic planning systems are proposed.     

OR in strategic land-use planning

An important historic strategic application of OR has been in the field of land-use and development plan production. Changes in Government policy and legislation have led to varying levels of interest in plan production. Three post-war cycles of ‘enthusiasm for plans’ can be identified. Whilst the first was rooted very much in the Architectural Design tradition, the second led to significant developments in OR, with far wider application. Subsequent reduced Governmental enthusiasm for ‘Development Plan production’ led to considerable atrophy of relevant skills in the planning community, including those derived from OR. However, the current ‘third period of post-war enthusiasm for planning’, reinforced by environmental concerns, has revived the need for relevant skills. It is suggested that, whilst the deficit in skills and their application remains high, there are some encouraging signs. Moreover, substantial progress in the field of ‘soft OR’ offers opportunities to both the OR and planning communities.     

A model for strategic planning under uncertainty

Summary In this paper the multi-period strategic planning problem for a consumer sumer product manufacturing chain is considered. Our discussion is focused on investment decisions which, are economically optimal over the whole planning horizonT, while meeting customer demands and conforming to technological requirements. In strategic planning, time and uncertainty play important roles. The uncertainties in the model are due to different levels of forecast demands, cost estimates and equipment behaviour. The main aim of this paper is to develop and analyse a multiperiod stochastic model representing the entire manufacturing chain, from the acquisitions of raw material to the delivering of final products. The resulting optimization problem is computationally intractable because of the enormous, and sometimes unrealistic, number of scenarios that must be considered in order to identify the optimal planning strategy. We propose two different solution approaches; firstly, we apply a scenario risk analysis giving the related results of experiments on a particular real data set. We then describe and investigate an Integer Stochastic Programming formulation of the problem and propose, as a solution technique, a variation of Benders decomposition method, namely theL-shaped method.     

Linking strategic and tactical forestry planning decisions

In this paper a two-level model and optimization algorithms areintroduced to assist forestry companies in simultaneously considering strategicinvestment and tactical planning decisions. A procedure to reduce thediscrepancy produced in the aggregation and disaggregation process used to linkthese two-level decisions is also presented. This procedure is based onboth cluster analysis over economic parameters defined on the standmacro-units to be harvested and on the information transmittedbottom-up and top-down between the strategic and tactical models. Wealso show a new approach for solving the tactical mixed integer model.     

A genetic approach for strategic resource allocation planning

Enterprises often implement a measurement system to monitor their march towards their strategic goals. Although this way it is possible to assess the progress of each goal, there is no structured way to reconsider resource allocation to those goals and to plan an optimal (or near optimal) allocation scheme. In this study we propose a genetic approach to match each goal with an autonomous entity (agent) with a specific resource sharing behavior. The overall performance is evaluated through a set of functions and genetic algorithms are used to eventuate in approximate optimal behavior’s schemes. To outline the strategic goals of the enterprise we used the balanced scorecard method. Letting agents deploy their sharing behavior over simulation time, we measure the scorecard’s performance and detect distinguished behaviors, namely recommendations for resource allocation.     

A network-based futures method for strategic business planning

Many strategic decisions in business are made in a context which the decision makers perceive as uncertain, complex and opaque. A method, based on Rhyne's field anomaly relaxation technique, is described of generating a network of states which characterise the environment or context in which strategic decisions are to be made. These states represent possible future conditions for the business, and knowledge of them allows improved strategic understanding and decision making to be achieved. This paper describes the method, using a representative real-life application to illustrate the process.     

Multistage stochastic programming in strategic telecommunication network planning

Mobile communication is taken for granted in these days. Having started primarily as a service for speech communication, data service and mobile Internet access are now driving the evolution of network infrastructure. Operators are facing the challenge to match the demand by continuously expanding and upgrading the network infrastructure. However, the evolution of the customer's demand is uncertain. We introduce a novel (long-term) network planning approach based on multistage stochastic programming, where demand evolution is considered as a stochastic process and the network is extended so as to maximize the expected profit. The approach proves capable of designing large-scale realistic UMTS networks with a time horizon of several years. Our mathematical optimization model, the solution approach, and computational results are presented.     

An optimization-based decision support system for strategic planning in a process industry: the case of aluminium company in India

We describe how a generic multi-period optimization-based decision support system (DSS) can be used for strategic planning in process industries. The DSS is built on five fundamental elements—materials, facilities, activities, storage areas, and time periods. It requires little direct knowledge of optimization techniques to be used effectively. Results based on real data from an aluminium company in India demonstrate significant potential for improvement in profits. We conclude with a comparison of similar studies in two other process industries.     

Supply chain network equilibrium with strategic financial hedging using futures

In this paper, we develop a network equilibrium model for supply chain networks with strategic financial hedging. We consider multiple competing firms that purchase multiple materials and parts to manufacture their products. The supply chain firms’ procurement activities are exposed to commodity price risk and exchange rate risk. The firms can use futures contracts to hedge the risks. Our research studies the equilibrium of the entire network where each firm optimizes its own operation and hedging decisions. We use variational inequality theory to formulate the equilibrium model, and provide qualitative properties. We provide analytical results for a special case with duopolistic competition, and use simulations to study an oligopolistic case. The analytical and simulation studies reveals interesting managerial insights.     

Reflections and observations on various approaches to strategic planning

Recent articles in the Operational Research Quarterly in the United Kingdom have discussed the suitability of optimality as the sole, or even the most appropriate, criterion relevant to decision making. This is perhaps particularly so in the case of strategic planning, in which decisions more likely have to be made in situations of uncertainty. Alternative criteria such as satisficing, robustness and stability have been proposed and the paper reflects on their relevance. This paper draws attention to the fact that strategic planning problems often involve multiple, often noncommensurable, objectives and stresses the importance of engaging decision-makers in an interactive dialogue in such situations. In the concluding observations, examples of two different approaches are given, both within the context of mathematical programming.     

Short-term financial planning with uncertain receipts and disbursements

The problem of short-term financial planning is to determine an optimal credit mix to meet the short-term cash needs and an optimal investment plan for excess cash. A number of linear optimization models have been developed to solve this problem, some of which are in practical use. The purpose of this paper is to generalize the assumptions of these models concerning the available information about future receipts and disbursements. It is presupposed that the financial officer has some idea as to the amount involved which, however, cannot be specified by a probability distribution. On the contrary, we assume that these ideas only permit qualitative probability statements such as the following:“That the difference between disbursements and receipts in a certain period lies in an interval I₁ is no less probable than that it lies in an interval I₂”.For this level of information we formulate a model for short-term financial planning, and we develop a solution procedure to determine the optimum financial alternatives. Finally, the entire procedure is demonstrated by a medium sized example.     

Exact methods for large-scale multi-period financial planning problems

A relevant financial planning problem is the periodical rebalance of a portfolio of assets such that the portfolio’s total value exhibits certain characteristics. This problem can be modelled using a transition graph G to represent the future state space evolution of the corresponding economy and mathematically formulated as a linear programming problem. We present two different mathematical formulations of the problem. The first considers explicitly the set of the possible scenarios (scenario-based approach), while the second considers implicitly the whole set of scenarios provided by the graph G (graph-based approach). Unfortunately, for both the formulations the size of the corresponding linear programs can be huge even for simple financial problems. However, the graph-based approach seems to be a more powerful model, since it allows to consider a huge number of scenarios in a very compact formulation. The purpose of this paper is to present both heuristic and exact methods for the solution of large-scale multi-period financial planning problems using the graph-based model. In particular, in this paper we propose lower and upper bounds and three exact methods based on column, row and column/row generation, respectively. Since the methods based on column/row generation exploits simultaneously both the primal and the dual structure of the problem we call it Criss-Cross generation method. Computational results are given to prove the effectiveness of the proposed methods.      

Hierarchical approach to the process planning problem

A hierarchical approach to the process planning problem in manufacturing systems is presented. The model developed consists of the following three subproblems: (1) the tool path selection, (2) the tool path sequencing and (3) the process selection. These problems lead to three distinct combinatorial optimization problems which are characterized and for which solution procedures are discussed.     

A comparison of different modelling approaches to strategic energy planning

In this paper we discuss the role of models in strategic planning, paying special attention to the way perceptions, values and preferences are treated. Starting from an analysis of the specific nature of long term planning we propose two criteria to evaluate the interaction between policy preferences and mathematical models. On basis of these criteria we examine four recent energy studies. The paper concludes with a comparison of these cases and recommendations concerning the interface between models and policy preferences.     

An innovative approach for strategic capacity portfolio planning under uncertainties

This research studies multi-generation capacity portfolio planning problems under various uncertainty factors. These uncertainty factors include price uncertainties, demand fluctuation and uncertain product life cycle. The objective of this research is to develop an efficient algorithm that generates capacity portfolio policies robust to aforementioned uncertainties.     

Extending the use of scenario planning and MCDA for the evaluation of strategic options

Multi-criteria decision analysis (MCDA) is well equipped to deal with conflicting, qualitative objectives when evaluating strategic options. Scenario planning provides a framework for confronting uncertainty, which MCDA lacks. Integration of these methods offers various advantages, yet its effective application in evaluating strategic options would benefit from scenarios that reflect a larger number of wide-ranging scenarios developed in a time-efficient manner, as well as incorporation of MCDA measures that inform within and across scenario comparison of options. The main contribution of this paper is to illustrate how a more diverse set of scenarios could be developed quickly, and to investigate how regret could be used to facilitate comparison of options. First, the reasons for these two areas of development are elaborated with respect to existing techniques. The impacts of applying the proposed method in practice are then assessed through a case study involving food security in Trinidad and Tobago. The paper concludes with a discussion of findings and areas for further research.     

An application of a network-based futures method to strategic business planning

An analysis of the possible future development paths for the business environment of a medium-sized company operating in the advertising and media sector has been carried out using extended field anomaly relaxation (EFAR). It indicates that a number of development paths exist for the company, the main issues confronting it being location and share structure. The constraints imposed by the environment on the choice of these two conditioning factors are severe and less freedom of action is evident than was perceived to be present before the analysis. For example, the option of relocating to Central London can be seen clearly to be consistent with only a major share sale amounting to exiting the business. The option of a majority or minority share deal together with a relocation to a single site and retaining a London front office emerged as an attractive option for the company. Certain structural characteristics surrounding the share structure are observed. Development of the situation continued after the analysis was reported, leading to confirmation of the validity of the method.     

Integrating effective flexibility measures into a strategic supply chain planning model

This paper develops models for capacity, product mix, distribution and input supply flexibility and integrates them in a strategic level, mixed integer supply chain (SC) planning model as a way of addressing demand and supply uncertainty, as well as improving market responsiveness. Capacity flexibility is modeled via the SC’s production capacity planning to address budgeted demand and ensure the fulfillment of prospective demand increases when considering various market scenarios. This model selects an optimal number of products from fast moving and extended product range options—based on the product mix flexibility. The model confirms a quick response to a changing marketplace by considering elements like transportation and supply lead time along with the probabilities of stock out options when addressing input supply and distribution flexibility. This paper proposes a solution procedure to solve the model for real world problems, and investigates the sensitivity of the model outputs with respect to changes in flexibility measures.     

Computer aided decision support system for water strategic planning in Jordan

The Nominal Group Technique and a multi-criteria decision aid software are utilised to develop a decision support system for strategic planning of water resources in Jordan. The system described is novel in that it integrates the various decision analytical management techniques in order to increase the flexibility and efficiency of the decision making process.