DECISION MODELING
Relying solely on heuristics is a thing of the past!
In today’s dynamic business landscape, managers, entrepreneurs, and small businesses need to embrace data-driven strategies to ensure sustainable success.
(Near) daily, you’re navigating decisions, uncertainty, and preferences – all can be modeled quantitatively.
Modeling Decisions:
- Main focus – optimizing choice(s) such as: profit, cost, resource allocation
- Used when – you have specific options &/or alternatives to choose from
- Common techniques – linear programming, integer programming, & dynamic programming such as ‘shortest path’, ‘knapsack’, ‘inventory control’, ‘Fibonacci sequence’, & ‘game theory’
- Example use-cases:
- Product pricing strategy: optimal pricing strategy for your products or services based on factors like production costs, market demand, and competitors’ pricing
- Inventory management: optimize inventory levels, reorder points, & order quantities – balancing the cost of holding inventory against the cost of stockouts.
- Marketing campaign allocation: allocate marketing budgets across different channels to maximize return on investment.
- Hiring & workforce planning: workload, skill gaps, and budget constraints
- Location (and expansion) selection: market size, competition, & operational costs
- Product development: features, timelines, resource allocation
- Supplier selection & procurement: evaluate & select suppliers based on factors such as price, quality, reliability, & lead times
- Supply chain optimization: most efficient sourcing, production, & distribution strategies
- Financial forecasting & investment: financial forecasting, including cash flow projections, budget allocation, & investment decisions
- Customer segmentation & targeting: segment the customer base and determine the most effective strategies for targeting & acquiring different customer groups
- Risk management & insurance: assess the appropriate level of insurance coverage & risk mitigation strategies for the business
- Exit & succession planning: plan for the eventual sale of the business or the transfer of ownership to family members or partners
Modeling Uncertainty:
- Main focus – account for & quantify the inherent unpredictability, variability, & risk associated with different aspects of a decision-making process
- Used when – you want to quantify risk, optimize decision strategies, enhance decision quality, plan for contingencies, improve resiliency, &/or enhance transparency & clarity about potential outcomes & risks
- Common sources – incomplete / insufficient information, measurement errors, random variability, &/or bias / influence
- Common techniques – probability theory, Monte Carlo simulations, & stochastic programming
- Example use-cases:
- Product pricing strategy: demand fluctuations, changing market conditions, & competitive responses to assess potential revenue & profit under various pricing scenarios
- Inventory management: demand variability & lead time uncertainty to ensure that inventory levels can meet customer demand with minimal disruption
- Marketing campaign allocation: various conversion rates and customer responses, allowing for risk assessment and mitigation
- Hiring & workforce planning: employee performance & turnover rates to assess the potential impact on business operations
- Location (and expansion) selection: market growth rates, regulatory changes, & economic conditions to assess the risk associated with each location
- Product development: technical challenges, market shifts, & changing customer needs on product development timelines & costs
- Supplier selection & procurement: supplier performance, potential disruptions, & market price fluctuations when making procurement decisions
- Supply chain optimization: disruptions, transportation delays, & fluctuating demand
- Financial forecasting & investment: economic uncertainty, interest rate fluctuations, & market volatility when assessing investment opportunities & risks
- Customer segmentation & targeting: Account for uncertain customer behavior & market trends when planning marketing & sales campaigns
- Risk management & insurance: Evaluate the potential financial impact of various risks, including natural disasters, accidents, & legal liabilities
- Exit & succession planning: Consider uncertainties related to market conditions, valuation, & potential buyers when creating an exit strategy
Modeling Preferences:
- Main focus – when multiple / conflicting objectives &/or preferences need to be optimized
- Used when incorporating preferences &/or judgements are beneficial in optimizing alignment between features & expectations
- Common techniques: multi-objective optimization, aka “multicriteria decision analysis (MCDA),” utility theory, benefit-cost analysis (BCA), Markov decision processes (MDPs), game theory, recommendation systems, & fuzzy logic/sets
- Example use-cases:
- Product pricing strategy: how different pricing strategies might affect customer perception & willingness to purchase
- Inventory management: product availability and lead time to align inventory decisions with customer satisfaction
- Marketing campaign allocation: account for customer preferences & behavior when tailoring marketing campaigns & messaging for specific target audiences
- Hiring & workforce planning: consider employee preferences, career development opportunities, & work-life balance when making hiring & retention decisions
- Location (and expansion) selection: consider personal preferences & values, such as lifestyle choices & cultural fit, when choosing a new business location
- Product development: consider user preferences & feedback to ensure that product features align with customer expectations
- Supplier selection & procurement: sustainable sourcing, ethical practices, & long-term partnerships when choosing suppliers
- Supply chain optimization: sustainability goals, cost-efficiency objectives, & customer service preferences
- Financial forecasting & investment: risk tolerance & investment objectives to tailor financial strategies to the business owner’s preferences
- Customer segmentation & targeting: customer preferences, buying habits, & communication preferences when crafting marketing messages & offers
- Risk management & insurance: risk tolerance & desired level of protection when selecting insurance policies & risk management approaches
- Exit & succession planning: timing & terms of the exit, as well as legacy goals
For in-depth explorations into decision modeling and operational analyses, explore expanded and enhanced content on the ‘Resources‘ pages:
{Note: To ease your experience, pages will be linked here as content is posted.}
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