13 – Logistic Design and Operational Planning

Introduction

The logistics environment is continuously changing due to changes in markets, rivals, suppliers, and technology. A systematic planning and design approach is required to properly analyse alternatives and establish and focus the enterprise strategy to match this changing environment. This unit offers a broad methodology that includes an overview of logistics planning techniques.

13.1 Planning Methodology 

Even in well-established industries, a company’s markets, demands, pricing, and service requirements can change quickly due to customer and rival behaviour. Firms frequently confront questions in reaction to these changes, such as:

(1) How many distribution warehouses should be used, and where should they be located?

(2) What are the inventory/service trade-offs for each warehouse?

(3) What transportation equipment should be employed, and how should trucks be routed? and

(4) Is it worthwhile to invest in new materials handling technology?

These types of inquiries are typically classified as complicated and data-intensive. The complexity stems from the vast number of factors that influence the total cost of logistics and the diversity of viable solutions.

Typical information studies must consider potential service alternatives, cost features, and operational technology. These analyses necessitate a well-structured process and powerful analytical tools.

Research Process

The approach for discovering and evaluating various logistics methods might vary greatly, just as no ideal logistical system is acceptable for all businesses. However, there is a generic procedure that can be applied to the majority of logistics design and analysis situations.

13.1.1 Phase I: Problem Definition and Planning

Phase I of logistics system design and planning provides the basis for the entire project. Everything that follows relies on a clear and well-documented problem definition and plan.

Feasibility Assessment

Logistics design and planning must start with a thorough assessment of the present logistics scenario.

The process of assessing the need for change is known as feasibility evaluation, and it involves tasks such as situational analysis, supporting reasoning formulation, and cost/benefit estimation.

Situational Analysis

Situational analysis involves gathering performance measurements and attributes that reflect the current logistical environment. A typical appraisal includes an internal examination, a market assessment, a competitive evaluation, and a technological assessment to establish improvement potential and opportunities.

Internal auditing is required to thoroughly grasp existing logistical procedures. It examines previous performance, data availability, strategy, operations, and tactical policies and practices. The review typically covers both the overall logistics process and each logistics function.

A comprehensive self-assessment for an internal evaluation looks at all essential resources, including the staff, equipment, buildings, connections, and information. The internal study should, in particular, focus on a thorough assessment of the existing system’s strengths and shortcomings. Each component of the logistics system should be thoroughly assessed in terms of its stated aims and its ability to achieve those objectives.

For instance, does the logistics management information system constantly provide and measure the customer service goals set by the marketing department? Similarly, can the material management process adequately serve production requirements? Is the current network of distribution centres successful in meeting customer service goals? Finally, how do the capabilities and measurements of logistics success differ among business units and locations?

These and other comparable questions serve as the foundation for the self-evaluation required for the internal analysis. The complete assessment seeks to discover opportunities that may drive or justify the redesign or improvement of the logistics system.

The suggested format is not the only way, but it emphasises the need to consider the processes, decisions, and essential measurements for each significant logistical task. Process considerations focus on physical and information fluxes through the value chain. Decision considerations focus on the reasoning and criteria currently employed for value chain management. Measurement considerations focus on the leading performance indicators and the firm’s ability to measure them.

The breadth of the analysis determines the particular content of the review. Unusually, the needed information is readily available. The internal assessment is not intended to collect detailed data but to provide a diagnostic look at present logistics processes and procedures and a probe to determine data availability. Most importantly, the internal assessment aims to identify areas where significant room for improvement exists. The external assessment is a review of customer trends and service needs. The market evaluation seeks to capture and formalise client perceptions and wishes in light of changes in the firm’s logistics capabilities. Interviews with select customers or in-depth customer surveys could be part of the evaluation.

Technology assessment involves evaluating the use and capabilities of significant logistics technologies, such as transportation, storage, materials handling, packaging, and information processing. The evaluation considers the firm’s skills in current technologies and its potential for implementing new technologies.

For instance, can improved materials handling capabilities provided by third-party vendors improve logistical performance? What role do modern information technology, communication, and decision-making systems play in guiding responsive logistical capabilities?

Finally, how might satellite and scanner communications technology help logistical systems? The technology evaluation aims to find technological advances that can effectively complement other logistics resources like transportation or inventory.

Supporting Logic Development

The second feasibility assessment assignment is to construct a supporting rationale to combine the findings of the internal review, external assessment, and technological study. The most challenging component of the strategic planning process is often the formulation of supporting arguments. The situational analysis offers senior management the most comprehensive picture of the strengths and shortcomings of the current and future logistical capabilities. In three ways, supporting logic development expands on this extensive study.

First, it must decide whether there is enough potential for logistics improvement to warrant in-depth research and analysis. In some ways, developing supporting logic necessitates critically examining prospective prospects and determining whether more inquiry is justified. The logistics principles (e.g., tapering principle, principle of inventory aggregation) are used to establish the feasibility of conducting extensive analysis and the potential advantages. While completing the remaining steps in the managerial planning process does not commit a firm to implementation or even guarantee the design of a new logistics system, the possible benefits of change should be clearly defined while establishing the supporting reasoning.

Second, to remove perceptual biases, supporting logic development thoroughly assesses present procedures and practises using a comprehensive factual analysis. Identifying areas with potential for improvement and those with good operations offers a framework for determining the need for strategic adjustment.

For example, it may be evident that excess inventory is a big problem, and there is great potential to reduce costs and improve service.

While the evaluation process typically indicates that many features of the current system are more correct than incorrect, the conclusion should be based on improvement. If supporting logic confirms distribution centres’ present number and location, future analysis can focus on streamlining inventory levels without the danger of under-optimization. This evaluation process’s deliverables include classifying planning and evaluation concerns into primary and secondary categories throughout short—and long-term planning timeframes.

Third, unambiguous assertions of prospective redesign alternatives should be included in building supporting arguments. The statement should include the following components:

(1) a definition of current procedures and systems,

(2) identify the most likely system design alternatives based on leading industry and competitive practices and

(3) suggest innovative approaches based on new theories and technologies. Alternatives should challenge old practices while also being realistic. The less frequently a re-design project is undertaken to re-evaluate current procedures and designs, the more necessary it is to identify a range of possibilities for consideration.

For example, evaluating a total logistics management system or distribution network every five years should assess a broader range of possibilities than every 2 years.

It is well worth the effort in the planning and design phase to create flow diagrams and outlines explaining the basic principles connected with each possibility. The images highlight prospects for flexible logistics techniques, clearly outline value-added and information flow needs, and provide a thorough overview of the options. Due to their refinement or segmentation, some logistics methods are challenging to depict in a single flow diagram.

Regional variances, product-mix changes, and differential shipment rules, for example, are challenging to represent, but they serve as the foundation for design possibilities.

When segmental strategies are proposed, each alternative can be depicted individually. A recommended approach requires the manager to review the logistical strategy and develop a logical explanation and justification of prospective benefits.

Cost/Benefit Estimate

The cost/benefit estimate, the final feasibility assessment task, estimates the possible benefits of conducting a logistical study and implementing the recommendations.

The categories are not mutually exclusive since an optimal logistics strategy may incorporate any combination of all three benefits simultaneously.

Results that improve availability, quality, or capability are examples of service improvement. Improved service enhances client loyalty and may attract new companies.

Cost-cutting benefits can be seen in two ways. First, gains may accrue due to a one-time reduction in the financial or management resources necessary to run the logistics system.

For instance, a logistical redesign may enable the sale of of distribution facilities, materials handling devices, or information technology equipment.

Reducing capital deployed for inventory and other distribution-related assets can significantly improve a firm’s performance by eliminating recurring expenditures and freeing up funds for alternative development. Second, cost savings can be realised through out-of-pocket or variable expenses.

For example, new materials handling and information processing technologies frequently lower variable costs by enabling more efficient processing and operations.

Cost prevention reduces participation in programmes and operations that are facing cost increases. For example, many material handling and information technology enhancements are partially justified by a financial consideration of the consequences of future labour availability and wage levels. Naturally, any cost-cutting explanation depends on a projection of future conditions and is thus subject to some mistake. Because of this uncertainty, logistics system redesign may not be approved purely based on cost prevention, yet these preventative measures are still necessary to examine.

There are no standards for determining when a planning scenario has sufficient cost/benefit potential to warrant an in-depth effort. Some assessments should be done regularly to ensure the viability of existing and future logistics operations. Ultimately, whether or not to engage in in-depth planning will be determined by how strong the supporting rationale is, how credible the expected benefits are, and whether or not the estimated benefits provide a significant return on investment to warrant organisational and operational change. These possible benefits must be weighed against the out-of-pocket costs of completing the process.

Although quick improvement opportunities are not necessarily the goal of a planning and design project, they are a common feasibility assessment conclusion. Immediate improvements in logistics performance can frequently raise revenue or cut costs enough to justify the remainder of an investigation.

Project Planning

The second Phase I action is project planning. Because logistics systems are complex, any effort to discover and assess strategic or tactical alternatives must be meticulously prepared to create a solid foundation for change. Project planning consists of five distinct components: a statement of objectives, a statement of constraints, measurement standards, analytical techniques, and a project work plan.

Statement of Objectives

The statement of objectives outlines the anticipated costs and service levels for the logistics system changes. They must be articulated precisely and in terms of quantifiable factors. The objectives include market or industry segments, revision timelines, and particular performance standards. These specifications often outline the specific service levels that management is aiming for. For example, the following are some measurable objectives that could be used to lead a logistics analysis:

Inventory availability: 99 percent for category A products, 95 percent for category B products, and 90 percent for category C products; desired delivery of 98 percent of all orders within 48 hours of order placement; minimise customer shipments from secondary distribution centres; fill mixed commodity orders without backorder on at least 85 percent of all orders; hold back orders for a maximum of 5 days; and provide the 50 most profitable customers

The precise description of these goals focuses system design efforts toward specified customer service performance levels. The total system cost to achieve the service objectives can be computed using the appropriate analytical method. If the total logistics cost exceeds management expectations, different customer service performance levels can be explored using sensitivity analysis to identify the influence on overall logistics cost.

Alternatively, performance criteria can be used to create maximum total cost limitations, and then a system that offers the highest degree of customer service while staying within an acceptable logistics budget can be designed.

Statement of Constraints

The second factor to consider when designing a project is design constraints. Based on the scenario analysis, senior management intends to limit the scope of authorised system adjustments. Each firm’s unique circumstances will determine the nature of such limits. However, two common instances demonstrate how restrictions might affect the overall planning process.

One major limitation in distribution system design is the network of manufacturing sites and their product mix selection. Management frequently holds existing production facilities and product mix constant for logistics system reform to simplify the study. Such limits may be justified by considerable financial investments in current manufacturing facilities and the organization’s ability to absorb change.

Another example of a restriction is the marketing channels and physical distribution activities of different divisions. Management may include some divisions while excluding others from redesign consideration in organisations with a typical pattern of decentralised profit responsibility. As a result, some divisions are designated as candidates for reform by management while others are not.

All design limitations help to limit the plan’s scope. However, as one executive said, “Why study something we’re not going to do anything about?” Unless there is a reasonable probability that management will accept recommendations to improve logistics strategy or operations significantly, their restrictions should be considered a study constraint.

The goal of creating a statement of restrictions is to provide a clear beginning point and broad perspective for the planning process. If computerised analytical techniques are applied, significant constraints may be revised later. Unlike the situation assessment, the restrictions statement specifies which organisational elements, buildings, systems, processes, and practices must be kept from the existing logistical system.

Measurement Standards

The feasibility analysis frequently emphasises the requirement for formulating managerial performance standards. Such standards guide the project by defining cost structures and performance penalties and offering a way to measure success. Management must establish measuring standards and objectives for each category as a prerequisite to plan formation. It is critical that the standards accurately represent overall system performance rather than a narrow, inadequate focus on logistics functions. Once established, such standards must be monitored and maintained during system development to benchmark the impact of modifications. Although there is substantial administrative discretion in the design of standards, care must be taken not to undermine the validity of the analysis and subsequent results by establishing unrealistic goals.

A key measurement requirement is quantifying assumptions that underpin or provide the logic for the standards. Top management should approve these assumptions because they have the potential to influence the strategic plan’s outcomes dramatically.

For example, a minor change in the standard cost and technique for appraising inventories can significantly change the strategic plan.

Measurement standards should include detailed financial account references and describe how cost components such as transportation, inventory, and order processing are determined. They must also specify essential customer service measures and calculating methodologies.

Analysis Technique

When the critical issues and alternatives have been outlined, a suitable analysis technique should be decided upon. Techniques range from simple manual analysis to sophisticated computerised decision-support tools.

Models incorporating optimisation or simulation algorithms are standard when evaluating and comparing alternative logistics warehouse networks.

On the other hand, many planning and design tasks can be accomplished by utilising solely manual or spreadsheet-based analyses. After defining the project’s objectives and restrictions, project planning must find different solution strategies and choose the optimal approach. Accenture publishes information about software products for logistics decision support annually.

When selecting an analytical technique, it is essential to consider the information needed to evaluate project issues and options. This includes identifying critical performance measures and reviewing the logistics system’s scope. Additionally, the availability and format of essential data should be considered.

Project Work Plan

A project work plan and the resources and time required for completion must be determined based on the feasibility assessment, objectives, limitations, and analysis technique. The alternatives and opportunities identified during the feasibility assessment serve as the foundation for selecting the study’s scope, which, in turn, dictates the completion time.

Project management is responsible for achieving projected results within schedule and financial restrictions. One of the most typical strategic planning mistakes is underestimating the time needed to finish a specific job. Overruns necessitate increased financial expenses and undermine project confidence. Fortunately, various PC-based software tools are available to help structure projects, guide resource allocation, and track progress. These approaches highlight deliverables and the interdependence of tasks.

13.1.2 Phase II: Data Collection and Analysis

Phase II focuses on data collecting and analysis after the feasibility evaluation and project plan have been completed. This covers defining assumptions, collecting data, and analysing options.

Assumptions and Data Collection

This activity extends the feasibility assessment and project plan by

(1) establishing analysis approaches and procedures,

(2) defining and revising assumptions,

(3) identifying data sources,

(4) collecting data, and

(5) gathering validation data.

Defining Analysis Approaches and Techniques

Although it is not often the first task, determining the proper analysis strategy and acquiring the requisite analysis tools is an excellent place to start. While other methodologies are available, analytical, simulation, and optimization are the most frequent. The analytical technique employs standard numerical tools, such as spreadsheets, to evaluate each logistics alternative. The availability and power of spreadsheets have boosted the use of analytical tools for distribution applications.

A simulation approach is analogous to a laboratory for evaluating supply chain alternatives. Simulation is commonly employed, especially when there is a high level of uncertainty. The testing environment can be physical, such as a scaled-down model materials handling system that physically depicts product flow, or numerical, such as a computer model of a materials handling environment that depicts product flow on a computer screen. With today’s software, simulation is one of the most cost-effective methods for analysing dynamic logistics solutions.

A PC-based simulation, for example, can represent the flows, activity levels, and performance attributes.

Many simulations can also graphically depict system features.

Supply chain dynamic simulation, for example, can demonstrate the trade-off between inventory allocation strategy and supply chain performance.

Optimization evaluates options and chooses the best one using linear or mathematical programming. While it offers the advantage of selecting the optimal choice, optimization applications are frequently more limited in scope than traditional simulation approaches. Due to its significant capabilities, optimization is often used to analyse logistics network alternatives, such as the number and location of distribution centres.

Defining and Reviewing Assumptions

The definition and revision of assumptions are based on the scenario analysis, project objectives, restrictions, and measurement standards. The assumptions establish the key operating characteristics, variables, and economics of present and alternative systems for planning purposes. While the format will vary depending on the project, assumptions are often divided into three categories:

(1) business assumptions,

(2) management assumptions, and

(3) analysis assumptions.

Business assumptions explain the broad business environment’s features, such as relevant market, consumer, and product trends and competitive actions. They define the general context in which an alternative logistics plan must operate. Business assumptions are often beyond the firm’s power to change.

Management assumptions establish the physical and economic aspects of the existing or alternative logistics environment and are typically changeable or refined by the organisation. Typical management assumptions include defining alternative distribution facilities, modes of transportation, logistical processes, and fixed and variable costs.

The constraints and limitations that must be provided to match the problem to the analysis technique are defined as analysis assumptions. These assumptions frequently affect the scale of the problem, the level of analysis information, and the solution approach.

Identifying Data Sources

In practice, the data collection process begins with a feasibility evaluation. Furthermore, a reasonably precise data specification is required to create or adapt the analytical technique. However, specific data must be collected and structured at this point in the planning procedure to support the analysis. Sensitivity analysis can be used to discover data gathering needs when data collection is exceedingly difficult or when the required level of accuracy is unknown.

Example: An initial study can be performed by estimating transportation costs using distance-based regressions. If the research shows that the optimal response is highly dependent on actual freight prices, more significant effort should be made to collect more precise transport rates from carrier bids.

Once operational, sensitivity analysis can be utilised to identify the key elements. When certain factors, such as outward transportation costs, are discovered, more effort can be devoted toward improving transportation accuracy, while less effort can be directed toward other data requirements.

Most of the information needed for a logistical analysis can be collected from internal records. Although some digging may be required, most information is widely available.

Sales and customer orders are the first significant data category. The annual sales projection and percentage of sales by month and seasonality patterns are frequently required to determine logistics volume and activity levels. Historical samples of client invoices are also required to identify market and cargo size delivery trends. The aggregate indicators of demand and shipment profiles must meet the logistical needs.

Specific client data are also required to add a spatial component to a logistics analysis. The spatial dimension emphasises that effective logistics must consider the cost and time of transporting goods over long distances. Customers and marketplaces are frequently aggregated by region, kind, size, order frequency, growth rate, and unique logistical services to reduce complexity while maintaining analysis accuracy.

Identifying and tracking the expenses connected with production and purchasing is required for integrated channel analysis. This frequently necessitates further classification using a bill of materials. While the location of manufacturing facilities is not a variable component in the design of a logistical system, it is often required to consider the number and location of plants, product mix, production schedules, and seasonality. Inventory transfer, reordering, warehouse processing policies and expenses must be specified. Inventory control rules and product allocation methods, in particular, are frequently crucial features. Finally, operational costs, capacities, product mix and storage levels, and service capabilities must be determined for each current and proposed warehouse.

Transportation data requirements include the number and type of modes used, modal selection criteria, rates and transit times, and shipping rules and policies. If private transportation is included in the analysis, corresponding data for the private fleet are also required.

The preceding section provides context for the data required to evaluate logistics alternatives. The primary reason for placing the formal data collection process after the analysis technique selection process is to allow data collection to match specific analysis technique requirements. In other words, the design solution can only be as good as the data it is based on.

Market data helps evaluate future scenarios in most logistics analysis applications. In most cases, management can estimate expected sales for future planning horizons. The challenge is obtaining market-by-market projections.

One way is to employ demographic estimates that are highly correlated with sales.

Assume that sales or usage are highly correlated with population. Using such a correlation and government population projections, estimating future demand levels and, thus, future logistics requirements is possible.

Various government agencies and institutions regularly produce a range of demographic estimates. Several zip code sites provide essential data for logistical planning, making a reasonable data bank of environmental information easily accessible.

Documenting competition logistics system designs and flows can also be beneficial for learning about competitor tactics and capabilities. This information is usually readily available through published material, annual reports, and general company executive knowledge. The primary goal of gathering such information is to provide competitive benchmarks that compare customer service, distribution networks, and operating capabilities.

Data Collection

The data-collecting procedure can begin once alternative data sources have been identified. The method entails gathering the necessary data and converting it to the proper forms for the analysis tool. Because this is frequently a tedious and time-consuming procedure, mistakes are likely. Potential errors include collecting data from an incorrect period and overlooking data that does not accurately reflect significant components of logistics activity, such as customer pickup volume. As a result, the data collection process should be meticulously documented to aid in identifying errors that may reduce analysis accuracy and determining any changes required to achieve acceptable accuracy.

Collecting Validation Data

In addition to gathering data for alternative analyses, base case or validation data must be collected to ensure the results reflect reality. When evaluating distribution procedures and operating settings, the challenge is whether the chosen analytical approach accurately replicates historical outcomes.

The goal of validation is to build management trust in the analysis process. Management will have little faith in alternative analysis if the method does not produce reliable outcomes. Efforts to collect data must include inquiries into why analytical results may not correctly reflect the past.

Changes in distribution centre operating practise, for example, or a one-time event such as a strike, may make it impossible to replicate the past precisely. When such situations arise, the validation data-gathering process should include an assessment of the likely impact of such modifications to make suitable decisions.

Analysis

The analysis activity evaluates strategic and tactical logistics alternatives using the technique and data from the preceding activity. The following exact tasks are included in this four-step activity:

(1) establishing analytic questions,

(2) conducting and confirming a baseline analysis,

(3) doing alternative analyses, and

(4) conducting sensitivity analysis

Defining Analysis Questions

The first duty is to define precise analytical questions about alternatives and the allowed range of uncertainty. The specific inquiries expand on the research aims and limits by identifying precise operating policies and parameters.

For example, the distribution centre site analysis questions must identify the exact location combinations to be examined.

In the case of an inventory analysis, questions about alternative services and uncertainty levels may be asked.

Assume that a strategic planning effort is centred on identifying an appropriate network of distribution facilities to service the domestic market in the United States. The network employs four distribution centres in Newark, New Jersey; Atlanta, Georgia; Chicago, Illinois; and Los Angeles, California. Shipment volume is defined in terms of weight shipped, transportation cost, inventory carrying expenses, and service level in the percentage of sales volume within two days of the distribution centre’s transit. The following are likely sample analysis questions:

(1) What is the performance impact of removing the Chicago distribution centre?

(2) What is the performance impact of closing the distribution centre in Los Angeles?

(3) How does closing the Atlanta distribution centre impact performance?

These questions represent a small portion of the possible evaluation choices. Other options include fewer or more distribution centres and a comparison of different sites.

It is critical to recognise that the analysis questions must be carefully defined so that a wide range of options can be evaluated without requiring time-consuming analysis of options with little likelihood of implementation.

Completing and Validating Baseline Analysis

The second task uses the appropriate method or tool to complete the baseline analysis of the current logistics environment. The results are compared to the validation data acquired before to establish the degree of match between historical and analytical findings. The comparison should concentrate on finding significant discrepancies and determining potential causes of mistakes. Incorrect or erroneous input data, improper or inaccurate analysis processes, or unrepresentative validation data can lead to errors. Errors should be addressed and repaired as inconsistencies are discovered. Sometimes, the error cannot be reversed, but it can be explained and justified. Once the disparities have been eliminated or explained to 2%, the application can be considered genuine, and the analysis can proceed.

Completing Analyses of Alternatives

Following the approach’s validation, the next stage is to evaluate supply chain options. The analysis must be completed to discover the relevant performance attributes of each alternative design or strategy. Changes in management policies and practices affecting aspects such as the number of distribution centres, inventory target levels, or the transportation shipment size profile should be included in the alternatives.

Completing Sensitivity Analysis

Following the completion of this analysis, the best-performing alternatives can be targeted for further sensitivity evaluation. In this case, uncontrollable factors such as demand, factor costs, and competitive actions are varied to evaluate each alternative’s ability to operate under various conditions.

Example: Assume that the alternative analysis shows that five distribution centres offer the best cost/service trade-off for the firm’s market area, given the base demand level.

Sensitivity analysis investigates the suitability of this ideal solution for varying levels of demand or cost. In other words, if demand increased or dropped by 10%, would five distribution centres still be the best option? A decision tree is then used to select the best alternative based on sensitivity analysis and assessing potential scenario probabilities.

13.1.3 Phase III: Recommendations and Implementation

Phase III puts planning and design efforts into action by generating specific management suggestions and creating implementation plans.

Develop Recommendations

The results of the alternative and sensitivity analyses are examined to make recommendations to management. This review method consists of four tasks:

(1) determining the best alternative,

(2) weighing costs and benefits,

(3) establishing a risk assessment, and

(4) creating a presentation.

Identifying Best Alternative

Alternative and sensitivity studies should find the best implementation solutions. Multiple choices, on the other hand, frequently provide identical or comparable results. Each alternative’s performance attributes and conditions must be compared to determine the two or three best possibilities. Although “best” might be interpreted differently, it is commonly defined as the option that achieves specified service objectives at the lowest total cost.

Evaluating Costs and Benefits 

Previously, potential benefits of strategic planning were identified as service improvement, cost reduction, and cost prevention. It was highlighted that these benefits are not mutually exclusive and that a solid plan may achieve them simultaneously. When assessing the potential of a specific logistics strategy, each alternative must undergo a cost-benefit analysis that compares current costs and service capabilities with projected conditions. The optimum cost/benefit analysis evaluates the options for a baseline period and then forecasts comparative operations over some time horizon. Thus, Benefits can be estimated based on one-time savings from system restructuring and recurring operating economies.

Developing Risk Appraisal

A risk assessment is a second rationale required to justify strategic planning suggestions. It considers the likelihood that the planning environment will meet the assumptions and evaluates the potential hazards associated with system switching. Sensitivity analysis can be used to quantify the risk associated with the acceptance of a particular alternative.

Assumptions can be changed, and each possibility’s impact on system performance can be calculated.

Sensitivity analysis, for example, can be used to determine system performance under various demand and cost assumptions. If the chosen alternative remains the best, even if demand increases or decreases by 20%, management can conclude that moderate deviations in the demand environment pose low risk. A risk appraisal gives a financial evaluation of the downside risk if planning assumptions do not materialise.

Risks associated with system transition can also be measured. Executing a logistics strategic plan may take several years. Typically, an implementation timeline is created to guide system changeover. To assess the risk of unplanned delays, several contingency plans can be tested to identify their potential impact.

Uncertainty connected with demand, performance cycle, cost, and competitive actions are familiar drivers of external risk. Labour and productivity issues, changes in corporate strategy, and changes in resource accessibility are all familiar sources of internal risk. These factors must be evaluated quantitatively and qualitatively for management guidance and justification.

Developing a Presentation

The final objective is to create a management presentation that identifies, rationalises, and justifies proposed adjustments. The presentation and accompanying report must highlight specific operational and strategic adjustments, provide a qualitative reason for why such changes are necessary, and then quantitatively justify the changes in terms of service, expense, asset utilisation, and productivity gains. The presentation should use graphs, maps, and flowcharts to demonstrate changes in logistics operating procedures, flows, and distribution networks.

Implementation

The final process activity is the actual plan or design implementation. An adequate implementation plan is essential since implementing the plan or design is the only way to see a return on investment from the planning process.

Defining the Implementation Plan

The first task establishes the implementation strategy regarding individual events, their sequence, and interdependence. While the initial plan may be on a macro scale, it must eventually be tweaked to provide individual assignment duty and accountability. Plan dependencies define the completion sequence by identifying the interrelationships between events.

Scheduling Implementation

The second task sets the implementation and time phases of the previously defined assignments. The timetable must allow for the acquisition of facilities and equipment, the negotiation of agreements, the development of procedures, and the training of employees. One of the software scheduling tools should be used for implementation scheduling.

Defining Acceptance Criteria

The third task establishes the acceptance criteria for determining the plan’s success. Acceptance criteria should prioritise service enhancements, cost reductions, increased asset use, and quality enhancements. Acceptance criteria must identify detailed components such as enhanced product availability or reduced performance cycle time if service is the primary focus. If cost is the critical consideration, the acceptance criteria must specify the predicted positive and negative changes in all cost categories. The acceptance criteria must take a broad view so that motivation focuses on overall logistics system performance rather than specific function performance. It is also critical that the acceptance criteria consider broad organisational feedback.

Implementing the Plan

The final task is to implement the idea or design. Implementation must include enough controls to ensure that performance is completed on time and that acceptance criteria are closely monitored.

It is vital to employ a defined process to lead logistics system design and refinement initiatives to ensure the objectives are documented and understood and the analyses are conducted correctly. While the preceding technique is helpful for logistics planning and design analysis, it may also influence the design of a logistics information system. The scenario analysis focuses on the current system’s characteristics and capabilities for a system design application. In contrast, the data gathering and analysis activities concentrate on new system design, development, and validation.

13.3 SC Analysis Methods and Techniques 

High-performance logistics necessitates a thorough examination of supply chain tactics and plans regularly. Freight lane analysis is required regularly to respond to rate changes and balance freight flows; tactical inventory analyses to identify items with excess inventory and determine the appropriate inventory target levels; and location analysis, now commonly referred to as supply chain planning, to perform the strategic evaluation of supply chain alternatives such as sourcing, plant location, warehouse location, and market service areas, which is becoming increasingly important to optimise flows. Dynamic simulation is used to analyse the dynamics of multiple-stage inventories, such as those between suppliers, facilities, and distribution centres. At the same time, tactical transportation analysis aids in truck routing and scheduling. The following sections detail the precise questions, alternate analytical methodologies, and typical data needs for each of these judgments.

13.2.1 Freight Lane Analysis

One type of frequent logistics analysis involves transit movements on specific freight channels. A freight lane is the shipment activity between two origin and destination places. The analysis can be done on a facility-by-facility basis or a regional scale. The volume balance between origin and destination points is the subject of freight channel analysis. Movements in both directions should be balanced, or about equal, to optimum vehicle use. Triangular freight lanes aim to coordinate movement between three places by transporting material and final product combinations between suppliers, manufacturers, and buyers.

Freight lane analysis considers both the amount of transportation and the number of shipments or trips made between sites. The goal is to uncover imbalances that can be exploited to improve logistical productivity. After identifying lane imbalances, management strives to locate capacity that can be transferred in the underutilised direction. This could be done by transferring carriers or modes, changing volume to or from a private fleet, expanding raw material backhaul, or forming a partnership with another shipper. Volume in the overutilized direction, on the other hand, could be redirected to other carriers or shippers or obtained from a different site.

13.2.2 Inventory Analysis

The second type of ad hoc logistics analysis concerns inventory performance and productivity. Typical inventory analysis is performed on an ABC basis and considers relative product sales volume and inventory turnover.

A logistics manager, for example, can rapidly identify product groups that significantly impact volume and inventory levels by listing the top 10 sales and inventory groupings in decreasing order.

As we all know, 20% of the items often account for 80% of the sales. It is also common for 80 percent of inventory to account for only 20 percent of volume. Knowledge of these qualities, as well as the goods that comprise each product group, is beneficial in directing inventory management efforts. Items with a high inventory commitment relative to sales can be targeted for rigorous inventory management efforts to reduce inventory levels and improve performance (e.g., turnover).

13.2.3 Location Decisions

The location of plants and distribution centres is a common issue for logistics managers. Warehouses have received much attention due to increased manufacturing economies of scale and lower transportation costs. As a result of global sourcing and marketing considerations, location analysis has been expanded in recent years to incorporate logistics channel design. Because worldwide operations enhance the complexity of logistics channel decision-making, design choices, and related logistics costs, location analysis has become increasingly important. Location analysis, or supply chain design, frequently considers material sources, production sites, distribution centres, and service providers.

Position decisions, as the name implies, are concerned with determining the quantity and location of warehouses. Typical management questions include:

(1) how many warehouses should the company use, and where should they be located?

(2) Which consumers or market segments should each warehouse serve?

(3) Which product lines should each facility or warehouse produce or stock?

(4) How should logistical routes be leveraged to procure materials and serve overseas markets? and

(5) How should a mix of public and private warehousing facilities be used? More refined logistics network challenges raise issue complexity by necessitating combinatorial analysis that incorporates the questions mentioned above.

Typical location analysis tasks are sophisticated and data-intensive. The number of plants, distribution centres, markets, and product choices that can be studied adds complexity and data intensity adds intensity because the study requires precise demand and transportation data. To effectively cope with such complexity and data intensity, sophisticated modelling and analytic approaches must be used to determine the best solutions. Mathematical programming and simulation are the two broad categories of techniques used to help location analysis.

Mathematical Programming

Mathematical programming is one of the most extensively used strategic and tactical logistics planning tools, and it is classified as an optimization methodology. One of the most prevalent tools for location analysis is linear programming, which selects the best supply chain architecture from a set of choices while considering certain limitations. House and Karrenbauer established a long-standing concept of logistics optimization:

An optimization model considers the aggregate set of customer requirements, the aggregate set of production options for producers, prospective intermediary points, and transportation choices to design the optimal system. The model specifies where the warehouses should be located, where the stocking stations should be located, how big the warehouses should be, and what types of transportation options should be implemented on an aggregate flow basis.

Several conditions must be met to solve a problem using linear programming. First, two or more activities or locations must vie for limited resources.

For example, shipments must be able to be made to a client from at least two different locations.

Second, in the problem structure, all relevant relationships must be deterministic and capable of linear approximation. Unless these enabling requirements are met, a linear programming solution, while mathematically efficient, may not be appropriate for logistical planning.

While linear programming is commonly used for strategic logistics planning, it is also utilised to solve operational challenges like production assignment and inventory allocation. Distribution analysts have employed two separate solution approaches for logistics analysis within optimization.

Network optimization is one of the most used types of linear programming for logistics problems. Network optimization considers the distribution channel a network comprising nodes representing production, warehouses, and marketplaces and arcs representing transportation links. Handling items at nodes and transferring goods across arcs incur costs. The network model aims to reduce production and incoming and outgoing transportation costs while considering supply, demand, and capacity limits.

Aside from the general concerns that apply to all analytical approaches, network optimization has unique advantages and limitations that enhance and limit its implementation in logistics analysis. The key benefits of network models are rapid solution timeframes and ease of communication between specialists and non-specialists. They can also be used monthly rather than annual increments, allowing for longitudinal or cross-time examination of inventory level variations. Fixed expenses may also be included in network formulations to simulate facility ownership. The network model output identifies the optimal distribution facilities and material flows for the logistics design problems defined in the analysis.

The magnitude of the problem that can be solved and the inclusion of fixed cost components have traditionally considered the downsides of network optimization. The problem size issue was significant for multistage distribution systems, including suppliers, manufacturing locations, distribution centres, wholesalers, and customers. While problem size remains an issue, developments in solution algorithms and hardware performance have greatly enhanced network optimization capabilities. The fixed cost constraint refers to the capacity to optimise both fixed and variable costs for manufacturing and distribution facilities. Significant progress has been made in solving this difficulty through network optimization and mixed-integer programming.

The other optimization solution technique that has been effectively applied to logistics problems is mixed-integer programming. The formulation is adaptable, allowing it to integrate many of the complexities and quirks in logistics applications. The essential advantage of the mixed-integer format is that it includes fixed and variable costs in the analysis.

For example, demand can be regarded as a non-integer, allowing for incremental improvements in system capacity in particular step increases.

In other words, as larger distribution centres are used, mixed-integer programming allows solutions to appropriately reflect rising fixed costs and economies of scale. The mixed-integer technique provides a high degree of pragmatism in accommodating constraints encountered in day-to-day logistical operations.

Historically, the most significant limitation of optimization has been issue size constraints along with other developments in mixed-integer programming; issue size limits have been solved for a long time by applying decomposition to solution strategies. Decomposition enables the incorporation of various commodities into the design of a logistical system. Most businesses sell multiple products or commodities to customers in varying amounts and assortments. While such products can be shipped and stored together, they are not interchangeable in terms of customer service.

The decomposition technique divides a multi-commodity scenario into a succession of single-commodity challenges. Commodity assignment is accomplished by an iterative process in which costs associated with each commodity are examined for convergence until a minimal fee or optimal solution is identified.

These optimization methods provide valuable tools for analysing location-related issues such as facility location, optimal product flow, and capacity allocation. Mixed-integer techniques are often more adaptable to operational idiosyncrasies, whereas network approaches are more computationally efficient.

Simulation

Static simulation is a second location analysis method. Almost any attempt to reproduce a scenario can be called a simulation. Robert Shannon defined simulation as “the process of creating a model of a real system and running experiments with it for either understanding system behaviour or assessing various methods within the boundaries imposed by a criterion or set of criteria for the system’s functioning.”

Static simulation mimics the product flows and associated costs of existing or planned logistics channel networks. Plants, distribution centres, and markets are all part of the network. The primary expense components are raw material procurement, manufacturing, inbound freight, fixed and variable distribution centre costs, outbound customer freight, and inventory carrying costs.

Static simulation looks at product flow as if it all happened at once during the year. In this regard, the primary distinction between static and dynamic simulation is how time-related events are handled. Dynamic simulation examines system performance through time, whereas static simulation does not attempt to account for the dynamics between periods. Each operational period within the planning horizon is treated as a finite interval in static simulation. The final findings assume operating performance for each period of the planning horizon.

For example, in developing a 5-year plan, each year is simulated as a separate event.

13.2.4 Location Analysis Data Requirements

The primary data for location analysis include definitions of markets, products, networks, customer demand, transportation rates, and variable and fixed costs.

Market Definition: For analysis, demand must be categorised or assigned to a geographic area. The merging of the geographical regions forms a logistics service area. This area could be a country or a worldwide region. Each customer’s demand is assigned to one of the market sectors. Selecting a market definition approach is critical in the system design process.

A variety of market-defining frameworks have emerged.

(1) county,

(2) standard metropolitan statistical area (SMSA), and

(3) zip or postal codes are the most helpful structures for logistics modelling. (Internationally, postal codes are the equivalent of zip codes.)

Because corporate records typically include such information, the most common structure employs zip or postal codes. Furthermore, significant government and transit data per zip code is available. The number of areas required to deliver correct findings is one of the most important considerations when choosing a market definition strategy. While more market detail improves accuracy, it also necessitates more study time. According to research, around 200 markets provide an effective trade-off between accuracy and analysis effort.

Product Definition: Although particular product flows might be examined when performing location analysis, such detail is usually unnecessary. Individual goods are pooled or aggregated to facilitate the study, particularly those with comparable distribution patterns, production sites, and channel arrangements. The majority of supply chain evaluations are done at the product family level.

Network Defined: The network definition specifies the channel members, institutions, and potential locations for analysis. Specific issues concern the combinations of suppliers, manufacturing facilities, distribution centres, wholesalers, and retailers that will be included. Consideration of new distribution centres or channel member alternatives is also part of the network definition. While a broader definition minimises the possibility of suboptimizing system performance, entire channel location analysis increases analysis complexity. Supply chain analysts must weigh the benefits and drawbacks of rising analysis complexity versus the increased possibility for complete supply chain improvement.

Market Demand: Market demand describes the volume of goods shipped to each geographical area designated as a market. The supply chain analysis is based on the proportional product volume supplied to each market area. While volume may refer to the number of units or cases transported to each market, most location assessments are weight-based because transportation costs are heavily determined by the weight moved. If significant changes are foreseen, market demand used in the study may also be based on previous shipments or forecast volume. Because shipment size substantially impacts transportation costs, market demand must be categorised into distinct shipment sizes.

Transportation Rates: Inbound and outbound transportation rates are critical information for location studies. Rates for shipments between existing and potential distribution channel members and markets must be supplied. Furthermore, tariffs must be established for each cargo size and transportation link between distribution centres and markets. It is not uncommon for supply chain analysis to necessitate more than a million individual rates.

Variable and Fixed Costs: The variable and fixed costs associated with operating distribution facilities are the last location analysis data required. Labour, energy, utilities, and materials are examples of variable costs. Variable expenditures are, in general, a function of throughput. Fixed costs include facility, equipment, and supervisory management charges. Fixed costs remain relatively constant within the operating range of a relevant distribution plant. While the differences in variable and fixed costs by geography are often not significant, minor locational concerns should be incorporated to ensure analysis correctness. The most considerable disparities are due to regional variations in pay rates, energy costs, land values, and taxes.

A significant amount of logistics planning attention is placed on site analysis. Previously, distribution networks were relatively stable, so firms did not need to conduct logistics system analyses regularly; however, the dynamics of alternative supply chain options, changing cost levels, and the availability of third-party services necessitate that supply chain networks be evaluated and refined more frequently today. Firms frequently conduct evaluations on an annual or even weekly basis.

13.2.5 Inventory

Inventory analysis decisions are made to determine the best inventory management parameters to meet desired service levels with the least amount of investment. Inventory parameters for a particular facility and product combination include safety stock, reorder point, order quantity, and review cycles. This study can be used to refine inventory parameters daily or periodically. Daily improvements increase the sensitivity of parameters to external changes such as demand levels or performance cycle length; nevertheless, they can result in anxious inventory management systems. Because of system anxiety, countless minor shipments are frequently expedited and de-expedited.

The decisions are the focus of inventory analysis. Specific questions include:

(1) How many goods should be manufactured during the subsequent manufacturing cycle?

(2) Which distribution centres should keep track of each item’s inventory (for example, should slow-moving items be centralised)?

(3) What is the best size for replenishment orders (decision on order quantity)?

(4) What is the required re-order point for replenishment orders (the safety stock decision)?

 Analytic Inventory Techniques

Analytic inventory methods use functional linkages to estimate appropriate inventory stocking parameters and service levels. The technique calculates optimal inventory parameters based on service objectives, demand characteristics, performance cycle characteristics, and logistics system features. Service objectives are often articulated regarding case or order fill rates from an inventory management standpoint. The demand characteristics describe the periodic average and standard deviation of customer demand; the performance cycle characteristics describe the average and standard deviations for replenishment performance cycles; and the logistics system characteristics describe the number of distribution stages or echelons requiring inventory management decisions. The analytical inventory technique is based on assumptions describing the logistics system’s characteristics (stocking echelons) and the probabilities related to demand and performance cycle characteristics. The probability relationships and the service level objectives determine the ideal inventory management parameters for replenishment order numbers and reorder points. There are numerous instances of software systems that use analytic methodologies to discover the best inventory management parameters.

Analytical inventory techniques have the advantage of identifying optimal inventory parameters immediately, given particular operational environment assumptions. On the other hand, analytical inventory techniques have limitations in terms of accuracy when assumptions are not met.

Because most analytic inventory techniques assume normally distributed demand and performance cycles, their accuracy suffers when the shape of actual demand or performance cycles deviates from the normality assumption.

Nonetheless, analytical inventory techniques are frequently a helpful place to start when seeking optimal inventory parameters.

Simulation Inventory Techniques

The inventory simulation approach generates a quantitative and probabilistic model of the logistics operational environment. The simulation technique is analogous to setting up a laboratory testing environment for the logistics network and operational regulations. The analytic method is similar to simulation, except that the roles of inventory parameters and service levels are inverted.

Inventory parameters such as order quantities and reorder points that will be checked become simulation inputs. These inputs define the testing environment. The main simulation outputs are the testing environment’s service level and inventory performance characteristics. In effect, the simulation examines the performance of a particular situation. If the reported performance does not meet the expected goals, the inventory parameters must be adjusted, and a new environment must be simulated.

The primary advantage of inventory simulation techniques is their capacity to mimic a wide range of logistics situations without requiring simplifying assumptions. Combining characteristics and operational norms makes it feasible to replicate practically any logistics environment realistically. The main limitation of simulation tools is their inability to find and recognise optimal solutions. While there are inventory simulation models that use search methods, their power and reach are restricted. As companies try to understand inventory dynamics in the logistics channel, there are signs that simulation is growing more popular.

Inventory decision support applications are becoming more important as the emphasis shifts to streamlining inventory levels to lower the logistics asset base. More precise inventory criteria have increased the requirement for more advanced inventory analysis methodologies. As a result, software companies have created both standalone and integrated applications.

13.2.6 Transportation Decisions

Transportation studies are concerned with the routing and scheduling of transportation equipment to maximise vehicle and driver utilisation while meeting customer service criteria. Transportation choices can be classified as either strategic or tactical. Strategic transportation decisions concern long-term resource allocation, such as for lengthy periods. As a result, strategic routing decisions identify fixed transportation routes that can be used for months or years. Tactical transportation decisions are about allocating short-term resources, such as daily or weekly routes. Transportation analysis aims to reduce the number of vehicles, hours, or miles required to deliver a product. Some common transportation analysis questions are:

(1) How should deliveries be grouped to form routes?

(2) What is the ideal delivery sequence for customer service?

(3) Which routes should be assigned to which sorts of vehicles?

(4) What is the optimal vehicle type for servicing various customer types? and

(5) How will clients impose delivery time constraints? The distribution centre is the principal point of departure for all delivery vehicles, and each stop represents a consumer location, such as a merchant.

Transportation Analysis Techniques

Routing and scheduling analyses have been thoroughly researched for logistics design and planning. They are especially significant for businesses that perform partial load delivery activities such as package or beverage distribution. The four types of procedures are Heuristic, precise, interactive, and combination approaches. Heuristic approaches create routes by successively adding and eliminating stops using rule-of-thumb clustering and other savings techniques. Exact or optimal approaches use mathematical (linear) programming to identify the best routes. Historically, optimization solution methods were too computationally complex for even the most powerful computers to handle, but recent mathematical programming advances have improved their capabilities.

Interactive decision-making processes utilize a combination of simulation, cost calculators, and graphics capabilities to facilitate the decision-making process. The decision maker identifies the options for evaluation, and the interactive decision support system then maps the routes, calculates time, and evaluates cost performance characteristics. The decision maker then assesses the performance characteristics of each alternative interactively and refines the strategy until no further improvement is possible. However, the challenge with interactive techniques is that they rely heavily on the decision maker’s competence and aptitude, particularly as the complexity and size of the problem increase.

Combinations of the three approaches have shown to be highly effective. When comparing alternative solution approaches, generalizability and accuracy must be considered. Generalizability is the capacity to include extensions for unusual situations, such as pickups and deliveries, different depots, time frames, vehicle capacities, and legal driving times, into a real-world context in an efficient manner. The ability to closely approximate performance characteristics and the results’ proximity to an optimal solution is called accuracy.

Transportation Analysis Data Requirements

Transportation analysis necessitates using three data networks: pickup or delivery demand and operating characteristics. The network identifies all potential routes and is the foundation for any transportation system analysis. Sometimes, a network is defined utilising the delivery area’s street maps. Each intersection serves as a node, and the streets serve as links. The network provides the connections between each node, as well as the road distance, transit time, and any particular constraints such as weight limits or tolls. A street-level network is exact, especially with restrictions like rivers and mountains. The high cost of creation and maintenance is a limitation of a street-level network. The other method involves arranging customers on a grid and calculating the possible connections between them using straight line distance. Coordinates of latitude and longitude are frequently used. A grid system is less expensive to create and operate than a street-level network, but it is less accurate and does not consider limits.

Demand data specifies the frequency with which customers must be picked up and delivered. Demand is defined as the average periodic pick-ups or deliveries per customer for strategic or long-term analysis. Routes are built based on average demand, with capacity reserved for extraordinarily high demand. Demand in tactical routing analysis often represents client orders scheduled for delivery during the planning period, such as daily. Tactical analysis enables routes to be precisely constructed for delivery requirements with no room for error.

Operating characteristics define the number of vehicles, vehicle limitations, driver constraints, and operating expenses. Vehicle constraints include capacity and weight limits and unloading constraints such as port requirements. Driving time and unloading restrictions are two examples of driver constraints. Fixed and variable expenses related to vehicles and drivers are included in operating costs.

Because of the effectiveness and availability of low-cost software, transportation analysis for vehicle routing and scheduling is gaining popularity. Many companies involved in day-to-day transportation operations have cut transportation costs by 10 to 15% using tactical or strategic transportation research. As customers place smaller orders, transportation analysis will become more necessary to make appropriate routing, scheduling, and consolidation decisions.

REVIEW QUESTIONS:

1. What is the fundamental goal of a logistics design and analysis study, and is it typically a one-time endeavour?
2. Define sensitivity analysis and elucidate its significance in systems design and analysis.
3. Why is it crucial to formulate supporting logic to steer the logistical planning process?
4. Enumerate the measures considered in internal and external review assessments and justify their importance.
5. Discuss the significance of cost/benefit evaluation in logistical systems design endeavours.
6. What is the primary objective of freight lane analysis?
7. Provide a broad overview of the fundamental disparities between analytic and simulation techniques.
8. What is the typical optimization technique’s primary advantage compared to simulation methods?
9. Compare and contrast strategic and tactical transportation decisions.
10. Discuss how transportation analyses focus on optimizing vehicle and driver utilization while meeting customer service requirements through routing and scheduling of transportation equipment.