Statistical Quality Control for Process Improvement SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
Technology & Operations
Strategy / MBA Resources
Case Study SWOT Analysis Solution
Case Study Description of Statistical Quality Control for Process Improvement
Describes systematic methods for process debugging and improvement, based on statistical quality control. Examples are from manufacturing settings, but techniques are also useful for services and sales, and to quantity improvement as well as quality improvement.
Swot Analysis of "Statistical Quality Control for Process Improvement" written by Roger E. Bohn includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Improvement Statistical facing as an external strategic factors. Some of the topics covered in Statistical Quality Control for Process Improvement case study are - Strategic Management Strategies, Business processes, Developing employees, Manufacturing, Product development and Technology & Operations.
Some of the macro environment factors that can be used to understand the Statistical Quality Control for Process Improvement casestudy better are - – increasing transportation and logistics costs, geopolitical disruptions, increasing household debt because of falling income levels, competitive advantages are harder to sustain because of technology dispersion, challanges to central banks by blockchain based private currencies, customer relationship management is fast transforming because of increasing concerns over data privacy, increasing energy prices,
central banks are concerned over increasing inflation, digital marketing is dominated by two big players Facebook and Google, etc
Introduction to SWOT Analysis of Statistical Quality Control for Process Improvement
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in Statistical Quality Control for Process Improvement case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Improvement Statistical, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Improvement Statistical 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 Statistical Quality Control for Process Improvement can be done for the following purposes –
1. Strategic planning using facts provided in Statistical Quality Control for Process Improvement case study
2. Improving business portfolio management of Improvement Statistical
3. Assessing feasibility of the new initiative in Technology & Operations field.
4. Making a Technology & Operations topic specific business decision
5. Set goals for the organization
6. Organizational restructuring of Improvement Statistical
Strengths Statistical Quality Control for Process Improvement | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Improvement Statistical in Statistical Quality Control for Process Improvement Harvard Business Review case study are -
Operational resilience
– The operational resilience strategy in the Statistical Quality Control for Process Improvement Harvard Business Review case study comprises – understanding the underlying the factors in the industry, building diversified operations across different geographies so that disruption in one part of the world doesn’t impact the overall performance of the firm, and integrating the various business operations and processes through its digital transformation drive.
High switching costs
– The high switching costs that Improvement Statistical 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.
Innovation driven organization
– Improvement Statistical is one of the most innovative firm in sector. Manager in Statistical Quality Control for Process Improvement Harvard Business Review case study can use Clayton Christensen Disruptive Innovation strategies to further increase the scale of innovtions in the organization.
Low bargaining power of suppliers
– Suppliers of Improvement Statistical in the sector have low bargaining power. Statistical Quality Control for Process Improvement has further diversified its suppliers portfolio by building a robust supply chain across various countries. This helps Improvement Statistical to manage not only supply disruptions but also source products at highly competitive prices.
Cross disciplinary teams
– Horizontal connected teams at the Improvement Statistical 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
– Improvement Statistical 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 Statistical Quality Control for Process Improvement HBR case study have helped the firm to develop new products and bring them quickly to the marketplace.
High brand equity
– Improvement Statistical has strong brand awareness and brand recognition among both - the exiting customers and potential new customers. Strong brand equity has enabled Improvement Statistical to keep acquiring new customers and building profitable relationship with both the new and loyal customers.
Digital Transformation in Technology & Operations segment
- digital transformation varies from industry to industry. For Improvement Statistical digital transformation journey comprises differing goals based on market maturity, customer technology acceptance, and organizational culture. Improvement Statistical has successfully integrated the four key components of digital transformation – digital integration in processes, digital integration in marketing and customer relationship management, digital integration into the value chain, and using technology to explore new products and market opportunities.
Strong track record of project management
– Improvement Statistical is known for sticking to its project targets. This enables the firm to manage – time, project costs, and have sustainable margins on the projects.
Ability to recruit top talent
– Improvement Statistical is one of the leading recruiters in the industry. Managers in the Statistical Quality Control for Process Improvement are in a position to attract the best talent available. The firm has a robust talent identification program that helps in identifying the brightest.
Learning organization
- Improvement Statistical is a learning organization. It has inculcated three key characters of learning organization in its processes and operations – exploration, creativity, and expansiveness. The work place at Improvement Statistical is open place that encourages instructiveness, ideation, open minded discussions, and creativity. Employees and leaders in Statistical Quality Control for Process Improvement Harvard Business Review case study emphasize – knowledge, initiative, and innovation.
Training and development
– Improvement Statistical has one of the best training and development program in the industry. The effectiveness of the training programs can be measured in Statistical Quality Control for Process Improvement 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.
Weaknesses Statistical Quality Control for Process Improvement | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of Statistical Quality Control for Process Improvement are -
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 Improvement Statistical supply chain. Even after few cautionary changes mentioned in the HBR case study - Statistical Quality Control for Process Improvement, it is still heavily dependent upon the existing supply chain. The existing supply chain though brings in cost efficiencies but it has left Improvement Statistical vulnerable to further global disruptions in South East Asia.
High operating costs
– Compare to the competitors, firm in the HBR case study Statistical Quality Control for Process Improvement 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 Improvement Statistical 's lucrative customers.
Interest costs
– Compare to the competition, Improvement Statistical has borrowed money from the capital market at higher rates. It needs to restructure the interest payment and costs so that it can compete better and improve profitability.
Lack of clear differentiation of Improvement Statistical products
– To increase the profitability and margins on the products, Improvement Statistical needs to provide more differentiated products than what it is currently offering in the marketplace.
Low market penetration in new markets
– Outside its home market of Improvement Statistical, firm in the HBR case study Statistical Quality Control for Process Improvement needs to spend more promotional, marketing, and advertising efforts to penetrate international markets.
High bargaining power of channel partners
– Because of the regulatory requirements, Roger E. Bohn suggests that, Improvement Statistical 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.
Aligning sales with marketing
– It come across in the case study Statistical Quality Control for Process Improvement that the firm needs to have more collaboration between its sales team and marketing team. Sales professionals in the industry have deep experience in developing customer relationships. Marketing department in the case Statistical Quality Control for Process Improvement can leverage the sales team experience to cultivate customer relationships as Improvement Statistical is planning to shift buying processes online.
Slow decision making process
– As mentioned earlier in the report, Improvement Statistical has a very deliberative decision making approach. This approach has resulted in prudent decisions, but it has also resulted in missing opportunities in the industry over the last five years. Improvement Statistical even though has strong showing on digital transformation primary two stages, it has struggled to capitalize the power of digital transformation in marketing efforts and new venture efforts.
Workers concerns about automation
– As automation is fast increasing in the segment, Improvement Statistical 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.
Compensation and incentives
– The revenue per employee as mentioned in the HBR case study Statistical Quality Control for Process Improvement, is just above the industry average. Improvement Statistical 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.
Employees’ incomplete understanding of strategy
– From the instances in the HBR case study Statistical Quality Control for Process Improvement, it seems that the employees of Improvement Statistical 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.
Opportunities Statistical Quality Control for Process Improvement | 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 Statistical Quality Control for Process Improvement are -
Creating value in data economy
– The success of analytics program of Improvement Statistical has opened avenues for new revenue streams for the organization in the industry. This can help Improvement Statistical to build a more holistic ecosystem as suggested in the Statistical Quality Control for Process Improvement case study. Improvement Statistical can build new products and services such as - data insight services, data privacy related products, data based consulting services, etc.
Redefining models of collaboration and team work
– As explained in the weaknesses section, Improvement Statistical is facing challenges because of the dominance of functional experts in the organization. Statistical Quality Control for Process Improvement case study suggests that firm can utilize new technology to build more coordinated teams and streamline operations and communications using tools such as CAD, Zoom, etc.
Low interest rates
– Even though inflation is raising its head in most developed economies, Improvement Statistical can still utilize the low interest rates to borrow money for capital investment. Secondly it can also use the increase of government spending in infrastructure projects to get new business.
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 Improvement Statistical 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.
Loyalty marketing
– Improvement Statistical has focused on building a highly responsive customer relationship management platform. This platform is built on in-house data and driven by analytics and artificial intelligence. The customer analytics can help the organization to fine tune its loyalty marketing efforts, increase the wallet share of the organization, reduce wastage on mainstream advertising spending, build better pricing strategies using personalization, etc.
Buying journey improvements
– Improvement Statistical can improve the customer journey of consumers in the industry by using analytics and artificial intelligence. Statistical Quality Control for Process Improvement 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.
Changes in consumer behavior post Covid-19
– Consumer behavior has changed in the Technology & Operations industry because of Covid-19 restrictions. Some of this behavior will stay once things get back to normal. Improvement Statistical 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. Improvement Statistical 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.
Remote work and new talent hiring opportunities
– The widespread usage of remote working technologies during Covid-19 has opened opportunities for Improvement Statistical 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 Improvement Statistical to hire the very best people irrespective of their geographical location.
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 Improvement Statistical in the consumer business. Now Improvement Statistical can target international markets with far fewer capital restrictions requirements than the existing system.
Increase in government spending
– As the United States and other governments are increasing social spending and infrastructure spending to build economies post Covid-19, Improvement Statistical can use these opportunities to build new business models that can help the communities that Improvement Statistical operates in. Secondly it can use opportunities from government spending in Technology & Operations sector.
Building a culture of innovation
– managers at Improvement Statistical 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 Technology & Operations segment.
Manufacturing automation
– Improvement Statistical can use the latest technology developments to improve its manufacturing and designing process in Technology & Operations 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.
Harnessing reconfiguration of the global supply chains
– As the trade war between US and China heats up in the coming years, Improvement Statistical can build a diversified supply chain model across various countries in - South East Asia, India, and other parts of the world. This reconfiguration of global supply chain can help, as suggested in case study, Statistical Quality Control for Process Improvement, to buy more products closer to the markets, and it can leverage its size and influence to get better deal from the local markets.
Threats Statistical Quality Control for Process Improvement External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The threats mentioned in the HBR case study Statistical Quality Control for Process Improvement are -
Environmental challenges
– Improvement Statistical needs to have a robust strategy against the disruptions arising from climate change and energy requirements. EU has identified it as key priority area and spending 30% of its 880 billion Euros European post Covid-19 recovery funds on green technology. Improvement Statistical can take advantage of this fund but it will also bring new competitors in the Technology & Operations industry.
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. Improvement Statistical 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
– Improvement Statistical 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.
Backlash against dominant players
– US Congress and other legislative arms of the government are getting tough on big business especially technology companies. The digital arm of Improvement Statistical business can come under increasing regulations regarding data privacy, data security, 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.
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.
Trade war between China and United States
– The trade war between two of the biggest economies can hugely impact the opportunities for Improvement Statistical in the Technology & Operations industry. The Technology & Operations 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 Improvement Statistical
– 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 Improvement Statistical.
High level of anxiety and lack of motivation
– the Great Resignation in United States is the sign of broader dissatisfaction among the workforce in United States. Improvement Statistical needs to understand the core reasons impacting the Technology & Operations industry. This will help it in building a better workplace.
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 Improvement Statistical.
Shortening product life cycle
– it is one of the major threat that Improvement Statistical is facing in Technology & Operations sector. It can lead to higher research and development costs, higher marketing expenses, lower customer loyalty, etc.
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 Improvement Statistical in the Technology & Operations sector and impact the bottomline of the organization.
Technology acceleration in Forth Industrial Revolution
– Improvement Statistical has witnessed rapid integration of technology during Covid-19 in the Technology & Operations industry. As one of the leading players in the industry, Improvement Statistical needs to keep up with the evolution of technology in the Technology & Operations 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.
Weighted SWOT Analysis of Statistical Quality Control for Process Improvement 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 Statistical Quality Control for Process Improvement 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 Statistical Quality Control for Process Improvement 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 Statistical Quality Control for Process Improvement 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 Statistical Quality Control for Process Improvement 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 Improvement Statistical needs to make to build a sustainable competitive advantage.
