Time Series Forecasting SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
Leadership & Managing People
Strategy / MBA Resources
Case Study SWOT Analysis Solution
Case Study Description of Time Series Forecasting
This is a Darden case study.This technical note introduces (1) approaches to forecasting in general, (2) simple moving averages and exponential smoothing, (3) accounting for seasonality in forecasting, (4) accounting for trend in forecasting, and (5) implementing a forecasting model. Holt and Winter models for exponential smoothing are included.
Swot Analysis of "Time Series Forecasting" written by Samuel E Bodily includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Forecasting Smoothing facing as an external strategic factors. Some of the topics covered in Time Series Forecasting case study are - Strategic Management Strategies, Financial analysis, Forecasting and Leadership & Managing People.
Some of the macro environment factors that can be used to understand the Time Series Forecasting casestudy better are - – challanges to central banks by blockchain based private currencies, technology disruption, cloud computing is disrupting traditional business models, talent flight as more people leaving formal jobs, increasing household debt because of falling income levels, there is increasing trade war between United States & China, customer relationship management is fast transforming because of increasing concerns over data privacy,
increasing government debt because of Covid-19 spendings, there is backlash against globalization, etc
Introduction to SWOT Analysis of Time Series Forecasting
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in Time Series Forecasting case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Forecasting Smoothing, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Forecasting Smoothing operates in.
According to Harvard Business Review, 75% of the managers use SWOT analysis for various purposes such as – evaluating current scenario, strategic planning, new venture feasibility, personal growth goals, new market entry, Go To market strategies, portfolio management and strategic trade-off assessment, organizational restructuring, etc.
SWOT Objectives / Importance of SWOT Analysis and SWOT Matrix
SWOT analysis of Time Series Forecasting can be done for the following purposes –
1. Strategic planning using facts provided in Time Series Forecasting case study
2. Improving business portfolio management of Forecasting Smoothing
3. Assessing feasibility of the new initiative in Leadership & Managing People field.
4. Making a Leadership & Managing People topic specific business decision
5. Set goals for the organization
6. Organizational restructuring of Forecasting Smoothing
Strengths Time Series Forecasting | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Forecasting Smoothing in Time Series Forecasting Harvard Business Review case study are -
High switching costs
– The high switching costs that Forecasting Smoothing has built up over years in its products and services combo offer has resulted in high retention of customers, lower marketing costs, and greater ability of the firm to focus on its customers.
High brand equity
– Forecasting Smoothing has strong brand awareness and brand recognition among both - the exiting customers and potential new customers. Strong brand equity has enabled Forecasting Smoothing to keep acquiring new customers and building profitable relationship with both the new and loyal customers.
Strong track record of project management
– Forecasting Smoothing is known for sticking to its project targets. This enables the firm to manage – time, project costs, and have sustainable margins on the projects.
Innovation driven organization
– Forecasting Smoothing is one of the most innovative firm in sector. Manager in Time Series Forecasting Harvard Business Review case study can use Clayton Christensen Disruptive Innovation strategies to further increase the scale of innovtions in the organization.
Sustainable margins compare to other players in Leadership & Managing People industry
– Time Series Forecasting firm has clearly differentiated products in the market place. This has enabled Forecasting Smoothing to fetch slight price premium compare to the competitors in the Leadership & Managing People industry. The sustainable margins have also helped Forecasting Smoothing to invest into research and development (R&D) and innovation.
Successful track record of launching new products
– Forecasting Smoothing has launched numerous new products in last few years, keeping in mind evolving customer preferences and competitive pressures. Forecasting Smoothing has effective processes in place that helps in exploring new product needs, doing quick pilot testing, and then launching the products quickly using its extensive distribution network.
Ability to recruit top talent
– Forecasting Smoothing is one of the leading recruiters in the industry. Managers in the Time Series Forecasting are in a position to attract the best talent available. The firm has a robust talent identification program that helps in identifying the brightest.
Diverse revenue streams
– Forecasting Smoothing is present in almost all the verticals within the industry. This has provided firm in Time Series Forecasting case study a diverse revenue stream that has helped it to survive disruptions such as global pandemic in Covid-19, financial disruption of 2008, and supply chain disruption of 2021.
Training and development
– Forecasting Smoothing has one of the best training and development program in the industry. The effectiveness of the training programs can be measured in Time Series Forecasting Harvard Business Review case study by analyzing – employees retention, in-house promotion, loyalty, new venture initiation, lack of conflict, and high level of both employees and customer engagement.
Cross disciplinary teams
– Horizontal connected teams at the Forecasting Smoothing are driving operational speed, building greater agility, and keeping the organization nimble to compete with new competitors. It helps are organization to ideate new ideas, and execute them swiftly in the marketplace.
Highly skilled collaborators
– Forecasting Smoothing has highly efficient outsourcing and offshoring strategy. It has resulted in greater operational flexibility and bringing down the costs in highly price sensitive segment. Secondly the value chain collaborators of the firm in Time Series Forecasting HBR case study have helped the firm to develop new products and bring them quickly to the marketplace.
Low bargaining power of suppliers
– Suppliers of Forecasting Smoothing in the sector have low bargaining power. Time Series Forecasting has further diversified its suppliers portfolio by building a robust supply chain across various countries. This helps Forecasting Smoothing to manage not only supply disruptions but also source products at highly competitive prices.
Weaknesses Time Series Forecasting | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of Time Series Forecasting are -
Low market penetration in new markets
– Outside its home market of Forecasting Smoothing, firm in the HBR case study Time Series Forecasting needs to spend more promotional, marketing, and advertising efforts to penetrate international markets.
Workers concerns about automation
– As automation is fast increasing in the segment, Forecasting Smoothing needs to come up with a strategy to reduce the workers concern regarding automation. Without a clear strategy, it could lead to disruption and uncertainty within the organization.
High dependence on existing supply chain
– The disruption in the global supply chains because of the Covid-19 pandemic and blockage of the Suez Canal illustrated the fragile nature of Forecasting Smoothing supply chain. Even after few cautionary changes mentioned in the HBR case study - Time Series Forecasting, it is still heavily dependent upon the existing supply chain. The existing supply chain though brings in cost efficiencies but it has left Forecasting Smoothing vulnerable to further global disruptions in South East Asia.
Employees’ incomplete understanding of strategy
– From the instances in the HBR case study Time Series Forecasting, it seems that the employees of Forecasting Smoothing don’t have comprehensive understanding of the firm’s strategy. This is reflected in number of promotional campaigns over the last few years that had mixed messaging and competing priorities. Some of the strategic activities and services promoted in the promotional campaigns were not consistent with the organization’s strategy.
Slow to strategic competitive environment developments
– As Time Series Forecasting HBR case study mentions - Forecasting Smoothing takes time to assess the upcoming competitions. This has led to missing out on atleast 2-3 big opportunities in the industry in last five years.
Slow to harness new channels of communication
– Even though competitors are using new communication channels such as Instagram, Tiktok, and Snap, Forecasting Smoothing is slow explore the new channels of communication. These new channels of communication mentioned in marketing section of case study Time Series Forecasting can help to provide better information regarding products and services. It can also build an online community to further reach out to potential customers.
Compensation and incentives
– The revenue per employee as mentioned in the HBR case study Time Series Forecasting, is just above the industry average. Forecasting Smoothing needs to redesign the compensation structure and incentives to increase the revenue per employees. Some of the steps that it can take are – hiring more specialists on project basis, etc.
High dependence on star products
– The top 2 products and services of the firm as mentioned in the Time Series Forecasting HBR case study still accounts for major business revenue. This dependence on star products in has resulted into insufficient focus on developing new products, even though Forecasting Smoothing has relatively successful track record of launching new products.
High operating costs
– Compare to the competitors, firm in the HBR case study Time Series Forecasting has high operating costs in the. This can be harder to sustain given the new emerging competition from nimble players who are using technology to attract Forecasting Smoothing 's lucrative customers.
High cash cycle compare to competitors
Forecasting Smoothing has a high cash cycle compare to other players in the industry. It needs to shorten the cash cycle by 12% to be more competitive in the marketplace, reduce inventory costs, and be more profitable.
High bargaining power of channel partners
– Because of the regulatory requirements, Samuel E Bodily suggests that, Forecasting Smoothing is facing high bargaining power of the channel partners. So far it has not able to streamline the operations to reduce the bargaining power of the value chain partners in the industry.
Opportunities Time Series Forecasting | External Strategic Factors
What are Opportunities in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The opportunities highlighted in the Harvard Business Review case study Time Series Forecasting are -
Learning at scale
– Online learning technologies has now opened space for Forecasting Smoothing to conduct training and development for its employees across the world. This will result in not only reducing the cost of training but also help employees in different part of the world to integrate with the headquarter work culture, ethos, and standards.
Better consumer reach
– The expansion of the 5G network will help Forecasting Smoothing to increase its market reach. Forecasting Smoothing will be able to reach out to new customers. Secondly 5G will also provide technology framework to build new tools and products that can help more immersive consumer experience and faster consumer journey.
Use of Bitcoin and other crypto currencies for transactions
– The popularity of Bitcoin and other crypto currencies as asset class and medium of transaction has opened new opportunities for Forecasting Smoothing in the consumer business. Now Forecasting Smoothing can target international markets with far fewer capital restrictions requirements than the existing system.
Remote work and new talent hiring opportunities
– The widespread usage of remote working technologies during Covid-19 has opened opportunities for Forecasting Smoothing to expand its talent hiring zone. According to McKinsey Global Institute, 20% of the high end workforce in fields such as finance, information technology, can continously work from remote local post Covid-19. This presents a really great opportunity for Forecasting Smoothing to hire the very best people irrespective of their geographical location.
Finding new ways to collaborate
– Covid-19 has not only transformed business models of companies in Leadership & Managing People industry, but it has also influenced the consumer preferences. Forecasting Smoothing can tie-up with other value chain partners to explore new opportunities regarding meeting customer demands and building a rewarding and engaging relationship.
Building a culture of innovation
– managers at Forecasting Smoothing can make experimentation a productive activity and build a culture of innovation using approaches such as – mining transaction data, A/B testing of websites and selling platforms, engaging potential customers over various needs, and building on small ideas in the Leadership & Managing People segment.
Creating value in data economy
– The success of analytics program of Forecasting Smoothing has opened avenues for new revenue streams for the organization in the industry. This can help Forecasting Smoothing to build a more holistic ecosystem as suggested in the Time Series Forecasting case study. Forecasting Smoothing can build new products and services such as - data insight services, data privacy related products, data based consulting services, etc.
Manufacturing automation
– Forecasting Smoothing can use the latest technology developments to improve its manufacturing and designing process in Leadership & Managing People segment. It can use CAD and 3D printing to build a quick prototype and pilot testing products. It can leverage automation using machine learning and artificial intelligence to do faster production at lowers costs, and it can leverage the growth in satellite and tracking technologies to improve inventory management, transportation, and shipping.
Changes in consumer behavior post Covid-19
– Consumer behavior has changed in the Leadership & Managing People industry because of Covid-19 restrictions. Some of this behavior will stay once things get back to normal. Forecasting Smoothing can take advantage of these changes in consumer behavior to build a far more efficient business model. For example consumer regular ordering of products can reduce both last mile delivery costs and market penetration costs. Forecasting Smoothing can further use this consumer data to build better customer loyalty, provide better products and service collection, and improve the value proposition in inflationary times.
Reconfiguring business model
– The expansion of digital payment system, the bringing down of international transactions costs using Bitcoin and other blockchain based currencies, etc can help Forecasting Smoothing to reconfigure its entire business model. For example it can used blockchain based technologies to reduce piracy of its products in the big markets such as China. Secondly it can use the popularity of e-commerce in various developing markets to build a Direct to Customer business model rather than the current Channel Heavy distribution network.
Buying journey improvements
– Forecasting Smoothing can improve the customer journey of consumers in the industry by using analytics and artificial intelligence. Time Series Forecasting suggest that firm can provide automated chats to help consumers solve their own problems, provide online suggestions to get maximum out of the products and services, and help consumers to build a community where they can interact with each other to develop new features and uses.
Identify volunteer opportunities
– Covid-19 has impacted working population in two ways – it has led to people soul searching about their professional choices, resulting in mass resignation. Secondly it has encouraged people to do things that they are passionate about. This has opened opportunities for businesses to build volunteer oriented socially driven projects. Forecasting Smoothing can explore opportunities that can attract volunteers and are consistent with its mission and vision.
Developing new processes and practices
– Forecasting Smoothing can develop new processes and procedures in Leadership & Managing People industry using technology such as automation using artificial intelligence, real time transportation and products tracking, 3D modeling for concept development and new products pilot testing etc.
Threats Time Series Forecasting External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The threats mentioned in the HBR case study Time Series Forecasting are -
Shortening product life cycle
– it is one of the major threat that Forecasting Smoothing is facing in Leadership & Managing People sector. It can lead to higher research and development costs, higher marketing expenses, lower customer loyalty, etc.
Aging population
– As the populations of most advanced economies are aging, it will lead to high social security costs, higher savings among population, and lower demand for goods and services in the economy. The household savings in US, France, UK, Germany, and Japan are growing faster than predicted because of uncertainty caused by pandemic.
Increasing international competition and downward pressure on margins
– Apart from technology driven competitive advantage dilution, Forecasting Smoothing can face downward pressure on margins from increasing competition from international players. The international players have stable revenue in their home market and can use those resources to penetrate prominent markets illustrated in HBR case study Time Series Forecasting .
Instability in the European markets
– European Union markets are facing three big challenges post Covid – expanded balance sheets, Brexit related business disruption, and aggressive Russia looking to distract the existing security mechanism. Forecasting Smoothing will face different problems in different parts of Europe. For example it will face inflationary pressures in UK, France, and Germany, balance sheet expansion and demand challenges in Southern European countries, and geopolitical instability in the Eastern Europe.
High dependence on third party suppliers
– Forecasting Smoothing high dependence on third party suppliers can disrupt its processes and delivery mechanism. For example -the current troubles of car makers because of chip shortage is because the chip companies started producing chips for electronic companies rather than car manufacturers.
Consumer confidence and its impact on Forecasting Smoothing demand
– There is a high probability of declining consumer confidence, given – high inflammation rate, rise of gig economy, lower job stability, increasing cost of living, higher interest rates, and aging demography. All the factors contribute to people saving higher rate of their income, resulting in lower consumer demand in the industry and other sectors.
New competition
– After the dotcom bust of 2001, financial crisis of 2008-09, the business formation in US economy had declined. But in 2020 alone, there are more than 1.5 million new business applications in United States. This can lead to greater competition for Forecasting Smoothing in the Leadership & Managing People sector and impact the bottomline of the organization.
Technology disruption because of hacks, piracy etc
– The colonial pipeline illustrated, how vulnerable modern organization are to international hackers, miscreants, and disruptors. The cyber security interruption, data leaks, etc can seriously jeopardize the future growth of the organization.
Technology acceleration in Forth Industrial Revolution
– Forecasting Smoothing has witnessed rapid integration of technology during Covid-19 in the Leadership & Managing People industry. As one of the leading players in the industry, Forecasting Smoothing needs to keep up with the evolution of technology in the Leadership & Managing People sector. According to Mckinsey study top managers believe that the adoption of technology in operations, communications is 20-25 times faster than what they planned in the beginning of 2019.
Easy access to finance
– Easy access to finance in Leadership & Managing People field will also reduce the barriers to entry in the industry, thus putting downward pressure on the prices because of increasing competition. Forecasting Smoothing can utilize it by borrowing at lower rates and invest it into research and development, capital expenditure to fortify its core competitive advantage.
Trade war between China and United States
– The trade war between two of the biggest economies can hugely impact the opportunities for Forecasting Smoothing in the Leadership & Managing People industry. The Leadership & Managing People industry is already at various protected from local competition in China, with the rise of trade war the protection levels may go up. This presents a clear threat of current business model in Chinese market.
Increasing wage structure of Forecasting Smoothing
– Post Covid-19 there is a sharp increase in the wages especially in the jobs that require interaction with people. The increasing wages can put downward pressure on the margins of Forecasting Smoothing.
Capital market disruption
– During the Covid-19, Dow Jones has touched record high. The valuations of a number of companies are way beyond their existing business model potential. This can lead to capital market correction which can put a number of suppliers, collaborators, value chain partners in great financial difficulty. It will directly impact the business of Forecasting Smoothing.
Weighted SWOT Analysis of Time Series Forecasting Template, Example
Not all factors mentioned under the Strengths, Weakness, Opportunities, and Threats quadrants in the SWOT Analysis are equal. Managers in the HBR case study Time Series Forecasting needs to zero down on the relative importance of each factor mentioned in the Strengths, Weakness, Opportunities, and Threats quadrants.
We can provide the relative importance to each factor by assigning relative weights. Weighted SWOT analysis process is a three stage process –
First stage for doing weighted SWOT analysis of the case study Time Series Forecasting is to rank the strengths and weaknesses of the organization. This will help you to assess the most important strengths and weaknesses of the firm and which one of the strengths and weaknesses mentioned in the initial lists are marginal and can be left out.
Second stage for conducting weighted SWOT analysis of the Harvard case study Time Series Forecasting is to give probabilities to the external strategic factors thus better understanding the opportunities and threats arising out of macro environment changes and developments.
Third stage of constructing weighted SWOT analysis of Time Series Forecasting is to provide strategic recommendations includes – joining likelihood of external strategic factors such as opportunities and threats to the internal strategic factors – strengths and weaknesses. You should start with external factors as they will provide the direction of the overall industry. Secondly by joining probabilities with internal strategic factors can help the company not only strategic fit but also the most probably strategic trade-off that Forecasting Smoothing needs to make to build a sustainable competitive advantage.