Data­driven optimal dynamic pricing strategy for reducing perishable food waste at retailers

Ph.D


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The novelty of this paper is to present a real-time IoT sensor data-driven optimal dynamic 126 pricing strategy to decide pricing at different stages of a sales season at retailers in the perishable 127 food supply chain. Both large and small companies have recognised the value associated with 128 effectively utilising big data. Data-driven businesses have delivered 5-6% higher performance 129 than similar organisations that do not utilise data-driven processes (Brownlow et al., 2015).

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Additionally, the practical applicability of the model is tested using a case study. The results

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are discussed with the company to check the rigour of the model.

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In their study, stochastic optimization techniques are employed to derive an optimal policy for 262 the process for different actors, including retailers.

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Food chains can be severely affected by disasters such as an earthquake, COVID-19 etc.

Evaluation of streamlined data by using hyperspectral imaging sensors
Stage 2 price is set (t2 period starts) If the freshness score is lower than 60% Stage 3 price is set (t3 period starts) If the freshness score is lower than 20%  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65 profit by continuously inspecting food quality and sending signals to a computer that updates 536 the unit price based on the freshness score and the remaining quantity of the product. To 537 simulate the operations of a grocery chain, we generate real-time IoT sensor data and use this 538 data in our simulation. We use the random function of Python with Gaussian distribution to 539 simulate the real-life application (Pseudo-random numbers, 2021). The number of daily 540 customers is regenerated based on the parameters given in Table 2 experiments are performed to analyse the effect of the sales price ( i ), initial replenishment 549 amount ( 0 ) and discount rate ( i ) on profit and food waste. Our goal is to find the optimal 550 sales price, initial amount to purchase and discount rate that maximise profit while minimising 551 food waste under the different scenarios with the parameters given in  In this subsection, we conduct experiments to identify the effects of the sales price on profit  The grocery manager should set the initial selling price to meet both objectives.  Here, we analyse the effect of the initial replenishment amount on profit and food waste. We 581 conduct experiments to test profit and food waste based on the on-hand inventory under 582   1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64 3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65 The retailer can balance the inventory on-hand and the turnover rate by applying variable 612 discount rates during the selling season. Product price is one of the most prominent factors 613 together with quality, for customers. During the selling season, retailers might need to update 614 product pricing in the right manner to fulfil customer needs, recover investment for the next 615 sales season, and generate revenue. A prudent discount price is an important factor for the 616 retailer to fight possible food waste. An effective pricing strategy leads to depleting on-hand 617 inventory before perishable products reach their best-before date. As the selling season 618 continues, retailers might need to lower product prices based on their on-hand quantity and 619 freshness score. Selling produce with a quality loss at a discounted price can help to reduce 620 food waste. Any price reduction must be aligned with the product and its defects.