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6 Ways to Optimize Your Strategic Planning: Part 2—Refine and Implement

Get the most out of your strategic planning process with our specific practices – part 2 of this 2-part series covers plan execution., (more) key techniques for strengthening your strategic planning process.

In the  first part of this series , we examined how certain key elements of a  strategic planning  process – the approach to your organization’s  strategy journey  – can be helpful to getting the most out of your work.

This second post focuses on another  three practical components that are key to a successful strategic planning effort:

4) Without priorities, it’s just a to-do list. That’s not strategic.

3-dimensional rendering of a lit lightbulb floating in the center of a circular maze with white walls

We’ve all seen it before: the “Top 10 list” of objectives for an organization. Of course, you need to identify what you want to accomplish, but, without a  prioritization of those objectives , your organization is left without a sense of focus or where to start. Because the team will be unsure as to where to invest their efforts, limited attention will be paid to all of the objectives. Allocating time and energy in this fashion won’t drive the most effective and positive change, and your key managers will quickly get frustrated and lose interest. The good news is that there is a fix for this:

5) Make sure your plan is actionable. Immediately.

Strategic planning, like many other initiatives, can be subject to a type of organizational inertia. As you conclude the planning effort and shift to implementation mode, you must  maintain momentum to avoid leaving your organization wondering what all the fuss was about .  Why did we spend all of this time and effort to generate this strategic plan? I don’t see that anything is different now that it is done. What a waste of time!  This is not abstract: it really happens. And it happens because the creators of the strategic plan are often at some organizational distance from those tasked with implementing it. You can guard against this, however, by  taking some deliberate steps  during  your planning process :

6) Periodically assess your strategic plan. Adjust course if needed.

Finally, you should  periodically check your progress against your strategic plan and evaluate whether you are still on the right path . You may decide to modify some objectives, supporting activities, or both. Every plan needs some adjustments to keep it current and fresh (another shout out to the strategy journey!). Just find the right frequency of review for your organization.

But also  keep an eye out for organizational fatigue . Part of the art of strategic planning is finding the right balance between doing and assessing. Consider the analogy of a ship navigating a narrow channel in dense fog. The ship moves at a deliberate speed to ensure safe passage and validates its position by sighting the buoys on either side of the ship’s bow. While the ship’s captain and crew may not always have clear sight to the end objective, they will make adjustments to ensure they remain on the right path. To help you stay on course:

Wrap Up: Six Tips for Successful Strategic Planning

In summary, make sure your strategic planning process:

These helpful hints should enable a strategic planning process that is more effective, efficient, and enjoyable for your organization. Good luck!

How Nexight Can Help

At Nexight, we have diverse experience  developing strategic plans for both programs and entire organizations, from academic departments to government agencies with complex missions. Get in touch if you’d to discuss ways that we can help with your strategic planning process.

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20 strategic planning models to consider, missing a piece of your strategic puzzle these planning models are sure to help..

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Ted Jackson


In this article, 1. balanced scorecard, 2. strategy map, 3. swot analysis, 4. pest model, 5. gap planning, 6. blue ocean strategy, 7. porter’s five forces, 8. vrio framework, 9. baldrige framework, 10. okrs (objectives and key results), 11. hoshin planning, 12. issue-based strategic planning, 13. goal-based strategic planning, 14. alignment strategic planning model, 15. organic model of strategic planning, 16. real-time strategic planning, 17. scenario planning, 18. ansoff matrix, 19. 7s model, 20. constraints analysis (theory of constraints), is one strategic planning model better than the others, is there ever a need to switch strategic planning models, using clearpoint to track your strategic planning models.

Strategic planning models are designed to help organizations develop an action plan to achieve their goals. There are a lot of strategic planning models out there. We know. Which is why we pulled together a list of 20 of the most popular ones and describe the scenario that they are most useful.

I’m willing to bet one of these situations sounds familiar:

If you can identify with one of these scenarios, this article is for you!

Read through each of the models or find the ones you're looking for from the list below and jump right to them. Then stick around for some insight on how you can make the most of whatever strategic planning model you choose—and increase the likelihood of its success. (Hint: It’s all about performance tracking.)

The Balanced Scorecard is a strategy management framework created by Drs. Robert Kaplan and David Norton. It takes into account your:

There are many ways you can create a Balanced Scorecard, including using a program like Excel , Google Sheets, or PowerPoint or using reporting software. For the sake of example, the screenshot below is from ClearPoint’s reporting software.

Balanced Scorecard example, strategic planning model, strategic planning process model

This is just one of the many “views” you’d be able to see in scorecard software once your BSC was complete. It gives you high-level details into your measures and initiatives and allows you to drill down into each by clicking on them. At a glance, you can tell what the RAG status of each objective, measure, or initiative is. (Green indicates everything is going as planned, while yellow and red indicate that there are various degrees of trouble with whatever is being looked at.)

All in all, a Balanced Scorecard is an effective, proven way to get your team on the same page with your strategy.

See Also: A Full & Exhaustive Balanced Scorecard Example

A strategy map is a visual tool designed to clearly communicate a strategic plan and achieve high-level business goals. Strategy mapping is a major part of the Balanced Scorecard (though it isn’t exclusive to the BSC) and offers an excellent way to communicate the high-level information across your organization in an easily-digestible format.

Balanced Scorecard strategy map, strategic planning model, strategic planning process model

A strategy map offers a host of benefits:

See Also: A Strategy Map Template For Medium-Sized Companies

A SWOT analysis (or SWOT matrix) is a high-level model used at the beginning of an organization’s strategic planning. It is an acronym for “strengths, weaknesses, opportunities, and threats.” Strengths and weaknesses are considered internal factors , and opportunities and threats are considered external factors .

Below is an example SWOT analysis from the Queensland, Australia, government:

SWOT analysis example, strategic planning model, strategic planning process model

Using a SWOT analysis as part of your strategic business model helps an organization identify where they’re doing well and in what areas they can improve. If you’re interested in reading more, this Business News Daily article offers some additional details about each area of the SWOT analysis and what to look for when you create one.

See Also: SWOT Analysis Template (+ Seven Other Strategic Planning Templates)

Like SWOT, PEST is also an acronym—it stands for “political, economic, sociocultural, and technological.” Each of these factors is used to look at an industry or business environment, and determine what could affect an organization’s health. The PEST model is often used in conjunction with the external factors of a SWOT analysis. You may also run into Porter’s Five Forces (see #7 below), which is a similar take on examining your business from various angles.

PEST analysis model example, strategic planning model, strategic planning process model

You’ll occasionally see the PEST model with a few extra letters added on. For example, PESTEL (or PESTLE) indicates an organization is also considering “environmental” and “legal” factors. STEEPLED is another variation, which stands for “sociocultural, technological economic, environmental, political, legal, education, and demographic.”

See Also: PEST Analysis Template (+ Seven Other Strategic Planning Templates)

Gap planning is also referred to as a “Need-Gap Analysis,” “Need Assessment,” or “the Strategic-Planning Gap.” It is used to compare where an organization is now, where it wants to be, and how to bridge the gap between. It is primarily used to identify specific internal deficiencies.

In your gap planning research, you may also hear about a “change agenda” or “shift chart.” These are similar to gap planning, as they both take into consideration the difference between where you are now and where you want to be along various axes. From there, your planning process is about how to ‘close the gap.’

The chart below, for example, demonstrates the difference between the projected and desired sales of a mock company:

The strategic-planning gap, strategic planning model, strategic planning process model

See Also: Gap Planning Template (+ Seven Other Strategic Planning Templates)

Blue Ocean Strategy is a strategic planning model that emerged in a book by the same name in 2005. The book—titled “Blue Ocean Strategy: How to Create Uncontested Market Space and Make Competition Irrelevant”—was written by W. Chan Kim and Renée Mauborgne, professors at the European Institute of Business Administration (INSEAD).

The idea behind Blue Ocean Strategy is for organizations to develop in “uncontested market space” (e.g. a blue ocean) instead of a market space that is either developed or saturated (e.g. a red ocean). If your organization is able to create a blue ocean, it can mean a massive value boost for your company, its buyers, and its employees.

For example, Kim and Mauborgne explain via their 2004 Harvard Business Review article how Cirque du Soleil didn’t attempt to operate as a normal circus, and instead carved out a niche for itself that no other circus had ever tried.

Below is a simple comparison chart from the Blue Ocean Strategy website that will help you understand if you’re working in a blue ocean or a red ocean:

Red ocean strategy vs. blue ocean strategy, strategic planning model, strategic planning process model

See Also: Blue Ocean Analysis Template (+ Seven Other Strategic Planning Templates)

Porter’s Five Forces is an older strategy execution framework (created by Michael Porter in 1979) built around the forces that impact the profitability of an industry or a market. The five forces it examines are:

The amount of pressure on each of these forces can help you determine how future events will impact the future of your company.

Porter’s Five Forces, strategic planning model, strategic planning process model

See Also: Porter’s Five Forces Template (+ Seven Other Strategic Planning Templates)

The VRIO framework is an acronym for “ v alue, r arity, i mitability, o rganization.” This strategic planning process relates more to your vision statement than your overall strategy. The ultimate goal in implementing the VRIO model is that it will result in a competitive advantage in the marketplace.

Here’s how to think of each of the four VRIO components:

Once you answer these four questions, you’ll be able to formulate a more precise vision statement to help carry you through all the additional strategic elements in your plan.

See Also: VRIO Analysis Template (+ Seven Other Strategic Planning Templates)

The Malcolm Baldrige National Quality Award is “the highest level of national recognition for performance excellence that a U.S. organization can receive.” Created in 1987, the goal of Baldrige is to help organizations innovate and improve, while achieving their mission and vision. The award is currently open to manufacturing, service, small business, nonprofit, government, education, and healthcare sectors.

When applying to win the Baldrige award at the national level, organizations undergo a competitive process that involves the implementation of the Baldrige framework. The framework outlines the “Baldrige Criteria For Performance Excellence,” where organizations must demonstrate achievement and improvement to an independent board of examiners in these seven areas:

To implement the Baldrige framework in your organization, start with two questionnaires that help you self-assess based on the seven Baldrige Criteria categories, and get a snapshot of your strengths and opportunities for improvement.

Baldrige framework example, strategic planning model, strategic planning process model

The strategic planning model of choice for Google, Intel, Spotify, Twitter, LinkedIn, and many other Silicon Valley successes, the OKR framework , is one of the more straightforward strategic planning tools. It’s designed to create alignment and engagement around measurable goals by clearly defining:

The strategic planning model of choice for many businesses—including Google, Intel, Spotify, Twitter, LinkedIn, and many other Silicon Valley successes— the OKR framework is also effective because goals are continually set, tracked, and re-evaluated so organizations can quickly adapt when needed. This is a fast-paced, iterative approach that flips the traditional top-down strategic models. The RACI matrix is a helpful visual for defining the role each person in your organization has for projects and processes, ensuring it aligns with their OKRs.

Objective and Key Results OKR definition, strategic planning model, strategic planning process model

See a strategic planning model fits your business? Download one of these free templates to put your planning process in play instantly.

The Hoshin Planning approach aligns your strategic goals with your projects and tasks to ensure that efforts are coordinated. This strategic management model is less focused on measures and more on goals and initiatives.

Some sources cite up to seven steps in the Hoshin Planning model, but the four most critical are:

You visualize your objectives, measures and targets, measure programs, and action items in a Hoshin Planning matrix. Four directional quadrants (north, south, east, west) inform each other and demonstrate alignment.

Hoshin Planning matrix example, strategic planning model, strategic planning process model

The issue-based strategic model is oriented in the present and projects into the future. It aims to identify the major challenges your organization faces now —in other words, you start with the problems to iron out issues before expanding, shifting your strategy, etc. This is typically a short-term (6-12 months), internally-focused process. Issue-based planning is ideal for young or resource-restricted organizations.

The leadership team or stakeholders identify the major issues and goals as a first step. Next, your organization will create action plans to address the issues, including budget allocation. From there, you will execute and track progress. After an issues-based plan has been implemented and the major issues you identified are resolved, then your organization might consider shifting to a broader, more complex strategic management model.

Issue-based strategic planning, strategic planning model, strategic planning process model

Goal-based strategic planning is the reverse of issue-based. This approach works backward from the future to the present. It all starts with your organization’s vision.

By nature, vision statements are aspirational and forward-thinking, but they need specifics in order to be realized. Goal-based planning tackles that challenge by setting measurable goals that align with your vision and strategic plan. Next, you define time frames for goal achievement. This is a long-term strategic planning tool, so goal time frames are typically about three to five years. From there, stakeholders will create action plans for each goal and begin tracking and measuring progress.

You want your department to be able to see their goals and the steps to achieve them. Use a Department Business Plan Dashboard

Similar to issue-based planning, the alignment model focuses on first looking internally to develop a strategy. This model is designed to sync the organization’s internal operations with its strategic goals.

Your strategic planning will start by identifying a goal and analyzing which operations or resources need to be aligned with that goal. Then you’ll identify which parts of operations are working well and which are not, brainstorming ideas from the successful aspects on how to address problems. Finally, you’ll create a series of proposed changes to operations or processes to achieve goals that will create the desired strategic alignment. The alignment strategic planning model is particularly useful when a company needs to refine its objectives or address ongoing challenges or inefficiencies that are blocking progress.

The organic model takes an unconventional approach because it focuses on the organization’s vision and values, versus plans and processes. With this model, a company uses “natural,” self-organizing systems that originate from its values and then leverages its own resources to achieve goals, conserve funds, and operate effectively.

The organic model takes an unconventional approach because it focuses on the organization’s vision and values, versus plans and processes. Click To Tweet

In the simplest form, there are three basic steps to follow when implementing the organic model of strategic planning:

What type of company would the organic strategic planning model work best for? If your organization has a large, diverse group of stakeholders that need to find common ground, a vision that will take a long time to achieve, and a strong strategic emphasis on vision and values (instead of structure and procedures), this may be the right model for you. It would also be beneficial for younger organizations that need to gain funding without presenting a formal strategic plan.

Similar to the organic model, real-time strategic planning is a fluid, nontraditional system. It’s primarily used by organizations that need to be more reactive, and perform strategic planning in “real time.” For these companies, detailed, long-term plans tend to become irrelevant within the typical three- to five-year planning cycle because the environment they operate in rapidly changes. Many nonprofits use this model—for example, a disaster relief agency needs the ability to respond quickly and adapt its strategy to immediately address a crisis.

Real-time strategic planning involves three levels of strategy: organizational, programmatic, and operational. For the first level, you’ll define the organization’s mission, vision, market position, competitors, trends, etc. Then, the programmatic strategy requires research into the external environment to identify approaches and offerings that would help the organization achieve its mission. The research should cover opportunities, threats, competitive advantages, and other points to spur strategic brainstorming.

The final operational level analyzes internal processes, systems, and personnel to develop a strategy that addresses “in-house” strengths and weaknesses. Looking at all three levels as a whole, strategy leaders can form criteria for developing, testing, implementing, and adapting strategies on an ongoing basis, allowing for quick and thoughtful responses when needed.

Real-time strategic planning, strategic planning model, strategic planning process model

In planning for their own future, too few organizations take the time to consider the multitude of external changes that could take place that would impact their plans. A healthcare company that fails to anticipate certain regulatory actions, an energy company that ignores the possibility of rising oil prices, and a global organization that hasn’t examined the potential for supply chain disruptions may all be impacted by those changes to some degree if they happened. And it isn’t just about mitigating the possible risks; it’s also about pursuing potential opportunities.

Scenario planning involves examining the variable elements of your environment, evaluating them for plausibility and impact, and factoring those scenarios that are most relevant into your decision-making. Two to five scenarios is the ideal number—this lets you explore a variety of themes without getting mired down in too many possibilities. Other frameworks (like SWOT or PESTLE) can be useful in developing those scenarios.

You can use scenario planning at the individual and departmental levels, but it is especially useful for organizational strategy planning. If your company is part of an industry that tends to be volatile or your organization itself has had to navigate costly, unexpected changes in the past, scenario planning is an excellent tool for developing your strategic vision. It can also be used to foster managerial thinking, encouraging leaders to consider the broadest range of future possibilities, and provide guidance when evaluating new projects or investment proposals.

Scenario planning - ClearPoint Strategy

(Image Source SME Strategy )

The Ansoff Matrix was developed to help organizations plan their strategies for growth. It is a 2x2 matrix with product on one axis and markets on the other axis. Depending on the box you are in, you may choose a different strategy for growth:

The level of risk increases with each strategy, with market penetration being the least risky and diversification being the most risky.

The Ansoff Matrix is useful for organizations that are actively trying to grow. Not only does it help you analyze and clarify your current strategy, but it also helps evaluate the risks associated with moving to a new strategy. SWOT and PEST are often used in combination with the Ansoff Matrix; business strengths and weaknesses as well as external factors should all play into your choice of growth strategy.

Ansoff matrix - Scenario planning models - ClearPoint Strategy

Developed by McKinsey consultants, this strategic business planning model emphasizes the importance of aligning an organization’s key internal elements to achieve strategy. Those key elements are:

The first step in applying the 7S model is to examine the current interconnectedness of these elements within your own organization; are there areas of weakness or inconsistencies? For example, you might discover that your skills training for employees is hindered by antiquated workflows and technology. Once you understand the relationships between these elements, you can work toward creating synergies that better support your strategy, whatever it may be.

The 7S model is best used during a strategy change, or whenever a major shift is occurring in any one of the seven areas.

7s model - Scenario planning models - ClearPoint Strategy

Constraints analysis is predicated on the idea that there are clear obstacles to strategy execution within your organization. Eliminating the weak link (or at least improving performance in that area) is the key to better results.

To apply constraints analysis correctly, you must first identify the constraint, or the main factor that limits your success. Process bottlenecks, faulty thinking, labor shortages, an unhealthy company culture, market conditions, or any number of other issues could be the culprit. While you might identify more than one problem area, constraints analysis focuses on improving one area at a time to achieve quick, impactful results.

That’s a great question—and the answer isn’t cut and dried. Some of these frameworks have been around longer than others, or have been used in various case studies in different ways. And sometimes managers are more comfortable with one over another, for any number of reasons.

We recommend determining which of these strategic planning models applies most to your organization’s way of thinking. For example, if you still need to work out your vision statement, it may be wise to begin with the VRIO framework and then move to something like the Balanced Scorecard to track and manage your ongoing strategy.

If you are set on pitching a particular strategic planning model to management, be prepared to give your boss or board of directors an example of another successful company that has utilized that particular model. An actual demonstration of success will make a somewhat abstract concept become more concrete.

If you are evaluating different approaches, I would recommend thinking about both creating your strategic plan and also executing on your plan. It doesn’t do you any good to have a strategic plan and not put it to use.

Yes. As your organization grows and changes, the frameworks you use to manage your strategy will change too. There are a lot of options out there—even more than the 20 we’ve explored above! It’s reasonable to expect that the framework you use today won't necessarily match your organization’s needs 10 or even five years from now. The added complexities of a growing business may make it necessary to rethink your approach to strategy planning. For example, the Balanced Scorecard might work well for tracking organizational and departmental plans, but you may eventually want a system that easily extends to the individual level. For that, you might add OKRs to your management framework.

You can also combine strategic planning models. Some organizations use elements of two or more frameworks to create a custom approach. Great! Every organization manages differently; your planning model should reflect your approach. But it’s always easier to have a starting point, which is why these frameworks exist in the first place.

Framework choices—and even strategies themselves—are flexible, but what’s not flexible is the need for software to track your performance.

Tracking is the only way to know if your strategic plan is working—if the data shows your actions aren’t making an impact, you need to make a change. While most organizations understand that tracking itself is a necessity, they’re using the wrong tools to do it.

The most common alternative to strategy software is Excel. Excel may be a familiar and affordable tool, but it’s costing your organization dearly in ways you may not have thought of:

For decision-makers, Excel-based reports are difficult to digest, which negatively impacts decision-making. Excel was built for numbers but it wasn’t built to easily show analysis and recommendations or real-time data, all of which are essential components of tracking. On top of that, spreadsheets simply make it harder to understand performance data. As a result, your leadership team isn’t getting all the information they need to make strategic decisions.

For the strategy and reporting teams, the use of spreadsheets and the manual collection and updating they require is a tremendous waste of time and a constant headache. There is also inherent risk—even with the most meticulous and careful management—in manual updating and version control across large and elaborate spreadsheets.

No doubt about it, Excel is, in fact, a somewhat costly tool. There is a better way—software exists that automates data updating and collects everything in one place for faster, safer, and better reporting.

Strategy software like ClearPoint was built exclusively for strategy performance reporting. So not only does it solve the above issues but it actually improves the likelihood of executing your strategy successfully. Here’s why:

Strategic planning models - Executive dashboard template

Another important benefit: ClearPoint will improve your strategy team’s productivity by simplifying the strategy reporting process. (One ClearPoint customer was able to reduce the cost of its monthly management board reporting by 70%!) To learn more about how ClearPoint addresses the day-to-day challenges of strategy reporting, read our Ultimate Guide on the subject.

If you’d like to learn more about ClearPoint, we’d love to talk with you! ClearPoint works with any and all of the strategic planning models mentioned above (the same can’t be said for other strategy software tools), so no matter which direction you’re planning to go, we can go with you. See how ClearPoint can help you achieve more— reach out to us today!

20 Strategic Planning Models To Consider

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The future depends on actions we take today. By deploying leading-edge work processes and technology tools and applications, we help our clients become more efficient as they execute their projects and achieve their goals. VHB’s Strategic Planning Optimization Tools (SPOT) gives our clients a future-focused solution to develop, plan, prioritize, track, and maintain programs like sustainability, alternatives analyses, and environmental impact studies. As our clients look to the future, SPOT supports initiatives that safeguard the long-term viability of their assets.

Clients like Los Angeles World Airports (LAWA), the authority that owns and operates Los Angeles International Airport (LAX) and Van Nuys Airport (VNY), use sustainability planning to increase facility performance, reduce environmental effects, and enhance the benefits they offer to the communities they serve. Utilizing SPOT, VHB worked with LAWA to collect and analyze data, develop meaningful content, and design Annual Sustainability Reports. In addition to the report itself, we developed collateral and tools, including a database of key performance indicator data, display boards, and skill sheets that allow the client to have a better understanding of their programs. SPOT gave LAWA a complete dashboard to keep relevant information easily accessible and understandable so that as these programs move forward, smarter decisions can be made.

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Why have a strategic plan?

Why is strategic planning important?

Why hire a facilitator?

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There are many strategic planning models, frameworks and systems. While such models provide a basis for process, each strategic planning framework should be adaptable to the needs of the organization. Best practices for strategic planning support a model based on inputs and outputs that deliver results:

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1. Research- In the Research phase, we discover the current state in terms of the market and the client’s positioning.

2. Plan- In the Plan phase, we work with the management team (usually 7 to 12 people) to agree on what markets to pursue, with what products and services, supported by what infrastructure (people, processes and technology). We consider what external forces may impact future demand, the competitive environment in which the client operates, and internal data that may inform decisions.

3. Act- In the Act phase, the client focuses on executing their strategic plan, meeting regularly to update market conditions and hold each other accountable to performance outcomes.

4. Measure- Aligning KPIs (key performance indicators) to the business strategy is central to the execution of a strategic plan. As consultants, we help managers choose relevant KPIs and implement a process for holding their teams accountable to them.

5. Iterate- Our plan is always changing based on market conditions. We meet regularly to review what mid-course corrections are required.

Certain components are typically included in a strategic plan:

After a cycle of strategic planning is completed, best-in-class companies continue working on strategy all the time. They embed strategic thinking into their management DNA and create a cycle to constantly re-evaluate and improve their business.

For more on how Optimize can help you create a best-in-class strategic plan, contact us .

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Strategic Planning and Business Model Optimization

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Our strategic engagements are designed for continuous business model optimization.  Continuous Business Model Optimization is key for enabling financial institutions to maintain long-term relevancy.  Our solutions for business model optimization help move organizations toward their desired culture, create greater clarity and alignment, and improve their execution.

Our Continuous Business Model Optimization process is intentionally designed to be customized for each organization.  Decision-makers choose the components of the process that are relevant to their needs.


Why Strategic Sprints?   Because there is tremendous opportunity in a world of uncertainty.  Leaders who frequently invest time as a team to think strategically about how to create opportunities, and then move into action, are steps ahead in appropriately adapting and optimizing their business models.

Strategic Sprints are frequent and condensed, virtual sessions, which can be supported by real-time financial modeling, designed to continue strategic momentum and recalculate routes as necessary.  These sessions are often had to complement, substitute for, or help teams prepare for Strategic Planning sessions.  Leaders use Strategic Sprints in a number of ways, such as:

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CEOs and senior teams realize that Strategic Planning is more important now than ever.  Whether virtual or in-person, c. myers’ Strategic Planning , which can be supported by real-time financial modeling, guides teams on continuing to move their business forward.  This is accomplished by helping leaders gain clarity and realign strategic priorities, while feeling the freedom to remain fluid in their implementation of strategy.


Part of remaining relevant is evaluating different futures and thinking through how those futures might impact the institution and how the institution might respond.  This kind of thinking can help decision-makers evaluate whether or not they need to adjust their current business model.


The future will be an opportunity for those who embrace uncertainty and are forward-thinking and intentional about optimizing their business model before it is necessary.  Clearly articulating your desired business model and gaining buy-in among all key stakeholders is a necessary big step to achieving your desired strategic impact.


Connecting the strategy with its longer-term financial implications helps ensure that the strategy, business model, and desired financial performance are aligned.


A brilliant strategy without successful implementation is really just a collection of terrific ideas.  Taking action that is aligned with strategy is key to achieving desired results.  If implementation is not a strength, then it impacts the level of business model optimization you can achieve.


Creating a solid infrastructure for building and maintaining the appropriate amount of high-performing talent can help position an organization to drive toward its desired business model and, ultimately, the strategic plan for the financial institution.  Directly linking desired talent capabilities and characteristics to the institution’s unique strategy translates to a keener ability to find, grow, and maintain an appropriate amount of high-performing talent.


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Optimization models for strategic planning

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Introduction, section snippets, references (15), cited by (34), recommended articles (6).


Agricultural Systems

Mathematical model for strategic planning optimization in the pome fruit industry.

This paper presents a strategic planning model for optimal restructuring of a pome (pears and apples) production farm concerning varieties and planting densities. The model decides the optimal investment policy for a given farm, maximizing the net present value of business while dynamically deciding its planting structure along a given time horizon under different financing scenarios. The model constraints impose restrictions on the activities to take into account risks and cultural practices. The mathematical model corresponds to a mixed integer linear programming problem, where integer decisions are related to the minimum reconversion land unit and funding requirements.

The model was applied to a realistic case study of a typical farm in the “Alto Valle de Río Negro” Argentine region. The study was conducted over a 20-year time horizon considering four varieties of apples and five of pears. The results showed the optimal investment policy for the replacement of varieties under different scenarios, with and without external financing. A sensitivity analysis was also performed on some of the most influential parameters. The model could be used either by governmental agencies to advise private sectors and to develop strategic economic policies or by companies to optimize the business profit.

► A general MILP model for strategic optimization of investments in the pome industry was developed. ► The model can be used by investment planning in private companies or as an advising tool for governmental agencies. ► A realistic case study for a typical farm in the Black River High Valley of Argentina is presented. ► Results of the case study provide optimal fruit replacement policy over a 20   year horizon.

Supply chain planning has been intensively studied in recent years. Typically, there are three planning instances depending on the specific objectives and time horizons: operational, tactical and strategic (Shapiro, 2001). In particular, strategic planning involves long-term decisions (years) and usually considers structural aspects. Such decisions are often “irreversible” and therefore their impact should be carefully analyzed.

Depending on the specific supply chain, strategic planning can have different objectives (Shapiro, 2001). For example, in many supply chains, logistics related to the distribution of goods in a timely manner between the different system nodes is essential. Another aspect of strategic planning is related to the introduction of new products to the company portfolio.

Pome fruit (pears and apples) supply chain is a complex system involving the interaction between many production, processing, storage and distribution instances. Due to its economic importance, this system has motivated various studies from the planning viewpoint at all scales. In Masini et al., 2007, Masini et al., 2009 models for tactical and operational planning -short and medium term, respectively- have been proposed to study the fruit industry business in Argentina. Ortmann (2005) studied the South African fruit supply chain with emphasis in optimizing material flow within the system.

Pear and apple trees are perennial plants with a 20-to-60-year life span. However, few trees reach the end of their life span because at some stage of their productive cycle, it may be convenient to replace them by new varieties. This is essentially due to: (i) changes in consumer preferences for more widely accepted fruit varieties, and (ii) technical advances that provide better production options than those currently installed. Both factors impact on profitability and production of the different varieties.

For these reasons, strategic planning of a pome fruit farm variety structure is important to ensure an acceptable return on the activity in the long term. The basic objective of this strategic planning is to maximize some measure of profitability, typically the net present value of the system, resulting from the economic balance of removing and planting the different varieties. The trees are divided into different age groups per variety. Each age group has different productivities and, therefore, originates different cash flows. The removal of trees of different age groups has thus different effects on the objective function.

Whereas this is an important problem, very few studies have systematically addressed it from the point of view of mathematical modeling. Ward and Faris (1968) proposed a dynamic programming model based on Markov processes to study the replacement of trees of a variety of plum. The emphasis of that work was to capture the stochastic effect on the productivity of trees of different ages. Oppenheim, 1979, Oppenheim, 2003 presented a multi-period linear programming approach to generate replacement strategies in a typical pipfruit farm of a productive region in New Zealand. Also for New Zealand farms, Kearney (1994) proposed a multi-period linear model to replace apple varieties. In that work, a sale price profile consisting of a progressive reduction at a fixed rate for the first 10   years and then a plateau during the following decade was used for each variety. This profile was intended to reflect the increased supply of fruit, worldwide. More recently, Cittadini et al. (2008) proposed a multi-year linear modeling framework to explore options for Patagonian fruit production systems. The proposed dynamic farm-scale model optimally allocates production activities to the various land units. Their model considers several crop species (cherry, apple, plum, peach and walnut) as well as training, irrigation and frost control systems. Their model spans a 50-year time horizon and includes two different objective functions.

The present work addresses strategic planning in the pome fruit industry, specifically focused on the restructuring decisions regarding the distribution of fruit varieties in a given farm. It is considered that this aspect has a great impact on the economy of the value chain, since much of the production is devoted to the international market. The proposed model considers the simultaneous growing of apples and pears taking into account their specific developing practices. Besides removal and planting, grafting is also included as a developing option. Three typical planting densities are considered.

As a case study the model is applied to a typical farm located in the “Alto Valle de Río Negro” Argentine region. However, while the system under study has special features, the model was developed in general terms so that it can be applied on most production units worldwide.

Problem description

Pome fruit industry activity is strongly seasonal. In the southern hemisphere, apple and pear harvesting takes place between January and April. Each variety is picked in a given range of weeks within the harvest period. Pome fruit business yearly net profit is given by the sum of the sales of the different varieties minus the maintenance costs of the existing infrastructure and investments in restructuring.

In a farm, any of the different apple and pear varieties available in the market can be

In order to illustrate the performance of the proposed model, a typical pome fruit production farm in the “Alto Valle de Río Negro” Argentine region has been considered as case study.

The required information is provided in the Appendix. In all cases the data were obtained from official sources when available (Leskovar et al., 2010, Rodriguez et al., 2009, Villarreal et al., 2008), and from personal communications with stakeholders otherwise. Table 1 provides the initial state of the farm under

Financing scenario analysis

Fig. 2 shows the evolution of the area occupied by apple and pear plantings, integrating all the varieties of each species, together with the free area for the “No loan” case. Regarding apple and pear plantings, data are discriminated by tree density. Total annual production of each species is reported on the right axis.

It can be seen that the free area vanishes in year 8, after passing through a maximum in year 3. It is also evident that the model proposes a transition from low and medium

Conclusions and future work

This paper presented an optimization model to aid in investment planning regarding the restructuring of pome fruit farms. From a formal point of view, the study falls within the scope of strategic planning of supply chains. The model can be considered as a general tool since it addresses a typical pome fruit farm, and considers usual practices. Moreover it can be easily adapted to become a decision making tool for similar systems (stone, grape, citrus fruit, etc.). The explicit incorporation of


This work was partially supported by: Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Agencia Nacional de Promoción Científica y Técnica (ANPCyT) and Universidad Nacional del Sur (UNS) of Argentina. The authors also wish to thank Eng. Patricia Villarreal of INTA Alto Valle (Allen, Río Negro, Argentina) for the fruitful discussions and suggestions concerning the material presented in this paper.

Exploring options for farm-level strategic and tactical decision-making in fruit production systems of South Patagonia, Argentina

Agric. syst., engineering economy.

Costos referenciales de producción y empaque temporada 2010–2011, pera y manzana

Crop planting and harvesting planning: Conceptual framework and sustainable multi‐objective optimization for plants with variable molecule concentrations and minimum time between harvests

The planting and harvesting of medicinal plants have characteristics that differentiate them from other crop types and complicate their planning. For example, drug processors do not require large quantities of product to be harvested but have a high concentration of active molecules. There is no evidence for any optimization tool to support the planting and harvesting of such plants. Given this sector's importance and its impact on populations’ health, it is necessary to develop solutions to increase the sustainability of their supply chains. This paper aims to bridge this gap by proposing a conceptual framework to characterize a crop planting and harvesting planning problem, and a multi-objective optimization model for the planning of planting, harvesting, post-harvesting, distribution and storage of medicinal plants with variable concentrations of molecules and minimum time between harvests. The model optimizes three objectives aligned with sustainability: supply chain costs, concentration of molecules in plants, farmers’ perceived economic unfairness. It is validated by its application to a case study of medicinal plants in the Basilicata region (Italy). The ε-constraint method is used to obtain 11 non dominated solutions showing the possibility of eliminating farmers’ perception of economic unfairness by maintaining similar values for supply chain costs and concentrations of active molecules when planning the production of medicinal plants. Finally, the TOPSIS method is applied to select the best plan to be implemented into the supply chain.

Agricultural water and land resources allocation considering carbon sink/source and water scarcity/degradation footprint

There is an urgent need for scientific management of agricultural water and land resources to cope with global warming and water shortages. Therefore, a stochastic multi-objective non-linear programming model was established under the society-economy-ecology framework in this study, which is capable of (1) considering the carbon sink function of farmland vegetation and the carbon emissions produced from the input of production materials; (2) dealing with the potential impact of agricultural water use on ecology by the indexes of water scarcity and degradation footprint (3) weighing the conflicts and contradictions among different developing targets with economic benefits, and water productivity as well as ecological considerations; (4) obtaining the planting structure, irrigation water allocation schemes and irrigation schedules. In order to verify the applicability and effectiveness of the model, it was applied to Huangyang Irrigation District in Shiyang River Basin, northwest China. After optimization, the net carbon sink increased by 2.55 × 10 4  t of which fertilizer contributed nearly 50% of carbon emission, while the water footprint reduced by 0.48 × 10 8  m 3 . To analyze the impact of the different allocation schemes of water and land resources on the ecological, economic and social subsystems as well as their interaction, coupling coordination degree (CCD) models were introduced to evaluate status quo and the optimization results of different models. The results showed that, compared with the single-objective models and the status quo, the proposed model has improved the value of CCD from 0.428 to 0.674. The proposed model can promote the harmonious and sustainable development of agricultural production and is equally applicable to agricultural management systems in the regions with similar conditions.

Does age matter? A strategic planning model to optimise perennial crops based on cost and discounted carbon value

Many perennial crops are cultivated in large plantation estates by agro-industrial companies. Some of the attributes of perennial crops, like annual variation in yields and time lag from planting to initial yield, create complex challenges in developing land utilisation strategies in plantations. This work develops a mathematical programming model to determine the optimal maturity (age) of the different plantations needed to meet the demand with reduced environmental impacts. The model also determines the corresponding planting period for new plantations, accounting for the yield profile of the perennial crops. Piecewise linearisation technique is used to model the yield profile, thus reducing the model to mixed integer linear programming. The optimisation is carried under two approaches – total cost and discounted carbon value (DCV). The total cost approach aims to determine the planting strategy that results in minimising the capital and operations cost at plantations. The DCV approach aims to delay the peak carbon emissions, thereby reducing the intensity of climate change effects and also buying time for mitigation and adaptive measures. The model developed in this work is illustrated with an oil palm plantation case study, which showed that though the total cost is the same for both the approaches, carbon emissions are 3.28% lower in the DCV result compared to the cost approach.

Network design for local agriculture using robust optimization

For small farmers wishing to sell their products under the “local agriculture” marketing concept, connecting with consumers can be challenging. One approach to mitigating this disconnect between where production occurs and where consumers reside is through a network of regional consolidation points. In this study, we utilize optimization models to assist the Missouri Coalition of Environment (MCE) in helping farmers from Missouri and Illinois route products from their farms to a central hub in St. Louis. The aim of this study was to minimize the ton-miles traveled by farmers and MCE vehicles in delivering agricultural products from farms to regional hubs to the central hub. Given historical data about variability of plant and animal production in the Greater Plains region, a robust optimization approach was incorporated to increase the likelihood that the network can accommodate uncertainty in agricultural production. GAMS/CPLEX was used to solve the model under different configurations and identify potential locations for regional hubs. Computational testing determined that the capacity of hubs plays a key role in the optimal assignments: given the assumed model constraint that farmers can travel only to their nearest regional hub, solutions may sacrifice a better objective function value in order to accommodate farmers’ travel requirements.

Optimizing tactical harvest planning for multiple fruit orchards using a metaheuristic modeling approach

In a fruit harvest season, the fruit must be collected during a relatively short period of intense activity. Moreover, large fruit export companies commonly manage multiple orchards where the resources and labor are shared, making the decision process more complex. In this study, we address this harvest problem by proposing a mixed integer linear programming model for supporting tactical decisions during the harvest season in order to reduce total costs. This includes costs related to the fruit not reaching maturity and the number of harvest days. Due to the difficulty of solving this model optimally when real cases are considered, we developed a GRASP metaheuristic method. We compared the GRASP metaheuristic solution to the best integer solution obtained by an exact method using a real case. We observed that the metaheuristic produced a solution in less computational time than the best integer solution. The total costs obtained by the GRASP metaheuristic were two percent greater than the total cost obtained by the best integer solution. Additionally, we analyzed two scenarios to establish if the joint resource planning of the orchards would allow a cost reduction. The GRASP metaheuristic provides orchard managers with a harvest plan in a timely manner and adds greater flexibility to the decision process. The proposed model can be used to plan the harvesting of a variety of fresh fruits.

Research directions in technology development to support real-time decisions of fresh produce logistics: A review and research agenda

There are many planning decisions in the fresh produce supply chain at strategic, tactical and operational level. For instance, in the literature we can find multiple papers addressing different planning aspects of the fresh produce supply chain, examples include (Ahumada et al., 2012; Ahumada and Villalobos, 2011; Amorim et al., 2012; Catalá et al., 2013; Ferrer et al., 2008; Flores and Villalobos, 2018; Soto-Silva et al., 2017; Van der Vorst et al., 2009; González-Araya et al., 2105; Nadal-Roig and Plà-Aragonés, 2015). Regarding more general supply chains such as that of traditional manufacturing products, fresh produce supply chains differ in important aspects.

Recent developments in consumption patterns, lowering of trade barriers, the emergence of low cost/miniature sensors and information technologies, and advanced business analytics tools are changing the playing field on which most of the agri-food supply chains operate. The intelligent use of sensing and information technologies has the potential to start a new food revolution in which limited resources such as water, capital, transportation capacity and labor could be optimally exploited so that fresh food, in particular fruits and vegetables, get to the consumer with minimal or no food waste. One of the keys for making this vision a reality is transforming the data collected as these products traverse the supply chain into effective and efficient supply chain decisions. This transformation relies on that the underlying decision systems that take advantage of this data exist or can be developed. The aim of this paper is to provide an overview of the state of the art and challenges and opportunities emerging from the integration of sensing data and information into decision support systems for supply chain of fresh fruits and vegetables.

Agricultural supply chain optimization and complexity: A comparison of analytic vs simulated solutions and policies

With worldwide food security emerging as a major policy issue moving forward, the structure and optimization of key agricultural supply chains is of growing importance. In turn, while many working models of supply chain optimization have been developed to ensure analytic tractability, others are building more precise characterizations of a supply chain as a complex system that may not be amenable to analytic solution. This research examines an important agricultural supply chain from the perspective of developing effective solutions to complex internal optimization issues that could ultimately affect food security. To this end, the Canadian wheat handling system is a complex export oriented supply chain that is currently undergoing extensive changes with respect to quality control. We develop both analytic and simulation models of this supply chain with the ultimate goal of identifying effective wheat quality testing strategies in a complex operational and regulatory environment. While the analytic model is founded on limited assumptions about individual behavior, agent-based simulation allows us to model farmers and handlers as rational and learning individuals who make decisions based on their own experiences as well as the experiences of others around them. We then make explicit comparisons between solutions and policies generated using the simulation approach against those generated by the analytically tractable model of the wheat supply chain. While the two approaches generate somewhat different solutions, in many respects they lead to similar conclusions regarding the overall testing and quality control issue in wheat handling.

Robust grain supply chain design considering post-harvest loss and harvest timing equilibrium

This paper presents a bi-level robust optimization model, where a food company maximizes its profit and minimizes post-harvest loss by optimally deploying grain processing/storage facilities and determining grain purchase price, while a group of spatially distributed non-cooperative farmers determine harvest time, shipment, storage, and market decisions under yield uncertainty and market equilibrium. The non-cooperative behavior of the food company and the farmers is represented by a bi-level Stackelberg leader follower’s game model with mixed-integer decision variables. The proposed model and solution approach are applied to case studies for Illinois and Brazil.

Vehicle scheduling and routing at a cross docking center for food supply chains

Cross-docking is increasingly used to reduce inventory holding and the time products spend in the supply chain. It is especially suitable for fresh produce distribution with a short shelf-life. This research studies cross docking operations to ensure food can be delivered just in time and with minimum cost of delivery, including inventory holding and transportation costs, and the penalty costs of early or tardy deliveries. The research focuses on the integration of vehicle scheduling and routing in one comprehensive model, which have traditionally been modeled separately. The integrated model also considers product consolidation at the warehouse and respects delivery time windows specified by the customer. The VRSP system is modeled as a mixed-integer linear program in CPLEX. Experiments reveal that it can be solved in a reasonable amount of time only for small sized problems. To reduce the size of the solution space, the concept of customer zones is introduced and hard instead of soft time windows are applied (VRSP-CZHTW). Extensive experiments are conducted with different size problems. The VRSP-CZHTW problem can still be modeled using CPLEX but can now be solved in a short time even for real-life size problems. The research is the first to include all the operations performed at a cross-dock warehouse in a single comprehensive model, and incorporates practical constraints for customer delivery zones and delivery time-windows. The novelty is in modeling food distribution efficiency, and in supporting food distributors in providing better delivery service for the time sensitive items at lowest logistics cost.

Applying the machine repair model to improve efficiency of harvesting fruit

Harvest costs are generally the single greatest expense for specialty crop producers. Identifying and reducing inefficiencies during harvest are essential steps towards reducing costs and maintaining product quality. To streamline harvest operations, the number of workers and machines required to harvest, handle, and transport the product needs to be planned along with the execution of field operations. In this paper the fruit harvest and the bin collection process are modelled, adopting a modified machine repair model (machine interference problem) from the operations research area. An algorithm was developed in Matlab ® to evaluate the performance of the system and improve confidence in sizing the fleet (workers and machines). Two different case studies are modelled using this algorithm: i) manual table grape harvest in Greece, and ii) manual sweet cherry harvest in Washington State, USA. First, the harvest procedures are described, and then the machine repair model is formulated to model each process. The bin loading process during grape harvest in Greece, and the sweet cherry picking process in USA are simulated. System performance under different number of bin carriers (or pickers) and loading stations is evaluated and results are presented.

Centralized and distributed optimization models for the multi-farmer crop planning problem under uncertainty: Application to a fresh tomato Argentinean supply chain case study

Imbalance between supply and demand of crops frequently occurs in markets originating an excess or shortage of supply in relation to demand. This causes high volatility and uncertainty in market prices, unmet demand, and waste, especially for fresh crops due to their limited shelf-life. This imbalance is mainly due to the inherent uncertainty present in the agricultural sector, the perishability of fresh crops, and the lack of coordination among farmers when making planting and harvesting decisions. Despite farmers usually plan the planting and harvesting in an individual way, there is a scarcity of research addressing the crop planning problem in a distributed manner and, even less, assessing their impact on the supply chain (SC) as a whole. In this paper, we developed a set of novel mathematical programming models to plan the planting and harvest of fresh tomatoes under a sustainable point of view for multi-farmer supply chains under uncertainty in different decision-making scenarios: i) distributed, ii) distributed with maximum and minimum land area constraints to be planted for each crop, iii) distributed with information sharing, and iv) centralized. Then, for each distributed scenario, the individual solution per farmer as regards the planting and harvesting decisions per crop are integrated to obtain the overall supply to satisfy the markets demand. This allows the assessment of the farmers’ real performance and the impact of their individual decisions to the entire SC performance. We also compare the results obtained for each scenario with the centralized model in terms of economic, environmental, and social impact. The experimental design shows that, when integrating the solutions for the whole SC, significant differences between planned and real results are obtained in each scenario as regards the gross margin per hectare, unmet demand, waste, and unfairness between farmers, being the distributed model with information sharing the most similar to the centralized one. The results show that uncertainty consideration in models improves the gross margin and the unfairness among farmers in all scenarios for both, planned and real evaluation.

Optimization approaches to support decision making in the production planning of a citrus company: A Brazilian case study

In this study a frozen concentrated orange juice aggregate production planning is modeled using linear programming to support decision making in the production process of a citrus company with multiple products, stages and periods. Then the model is extended to take into account uncertainty in some model parameters using a robust optimization approach. Besides tactical decisions in the production, blending and storage of juices, the nominal and robust models include the orange harvesting plan, which considers orange maturation curves. The models also include the blending process of different types of juices to match product specifications, for example, using orange acidity to calculate the ratio specification. These planning models take into account a large portion of typical supply chains of frozen concentrated orange juice and a case study was developed in a citrus company, which has different facilities and a worldwide distribution system, similar to other companies in this sector. To solve the models, an algebraic modeling language and a state-of-art optimization software of mathematical programming problems were used. The computational results obtained indicate that these optimization approaches can be useful in real situations.


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