Generative Discrete Event Process Simulation for Hidden Markov Models to Predict Competitor Time-to-Market

TL;DR

This study provides insight into how firms can use indirect market intelligence and modeling techniques to anticipate competitor actions and make strategic decisions based on time-to-market estimates.

Abstract

We study the challenge of predicting the time at which a competitor product, such as a novel high-capacity EV battery or a new car model, will be available to customers; as new information is obtained, this time-to-market estimate is revised. Our scenario is as follows: We assume that the product is under development at a Firm B, which is a competitor to Firm A; as they are in the same industry, Firm A has a relatively good understanding of the processes and steps required to produce the product. While Firm B tries to keep its activities hidden (think of stealth-mode for start-ups), Firm A is nevertheless able to gain periodic insights by observing what type of resources Firm B is using. We show how Firm A can build a model that predicts when Firm B will be ready to sell its product; the model leverages knowledge of the underlying processes and required resources to build a Parallel Discrete Simulation (PDES)-based process model that it then uses as a generative model to train a Hidden Markov Model (HMM). We study the question of how many resource observations Firm A requires in order to accurately assess the current state of development at Firm B. In order to gain general insights into the capabilities of this approach, we study the effect of different process graph densities, different densities of the resource-activity maps, etc., and also scaling properties as we increase the number resource counts. We find that in most cases, the HMM achieves a prediction accuracy of 70 to 80 percent after 20 (daily) observations of a production process that lasts 150 days on average and we characterize the effects of different problem instance densities on this prediction accuracy. Our results give insight into the level of market knowledge required for accurate and early time-to-market prediction.

Figures (10)

• Figure 1: Illustrative example of the Hidden HMM model with Activity Sets as Hidden States and Resource Sets as Observation or Emission states
• Figure 2: The figures show how matching-success rates change with observation sequence lengths when activity-graph density $p$ and resource-map graph density $q$ are varied across each figure in the array. Each figure shows several individual plots for different settings of number of resources $m$. Also shown are the $95\%$ confidence-bands around the sample means of the matching-success rates.
• Figure 3: The figures show how matching-success rates change with observation sequence lengths when the number of resources $m$ and activity-graph density $p$ are varied across each figure in the array. Each figure shows several individual plots for different settings of resource-map graph densities $q$. Also shown are the $95\%$ confidence-bands around the sample means of the matching-success rates.
• Figure 4: The figures show how matching-success rates change with observation sequence lengths when the number of resources $m$ and resource-map graph density $q$ are varied across each figure in the array. Each figure shows several individual plots for different settings of activity-graph densities $p$. Also shown are the $95\%$ confidence-bands around the sample means of the matching-success rates.
• Figure 5: The figures show how required observation-sequence lengths $\ell_r$ change with number of resources $m$ when activity-graph density $p$ and resource-map density $q$ are varied across each figure in the array. Each figure shows several individual plots for different settings of number of resources $m$.